v0.2.37 - Add nostr-core relay reconnect cooldown with stability-based reset to prevent flapping loops

.dockerignore

@@ -0,0 +1,12 @@
+.git
+.gitignore
+.roo/
+*.log
+*.nsec.debug.log
+context.log.md
+d-log.txt
+tests/results/
+
+# Keep nostr_core_lib sources but exclude VCS metadata to avoid huge build context
+nostr_core_lib/.git/
+nostr_core_lib/**/.git/

.gitignore

@@ -33,3 +33,7 @@ a.out
 
 # Local secrets
 genesis.jsonc
+genesis.anvil.jsonc
+anvil.service
+tests/configs/test_genesis.jsonc
+tests/configs/test_keys.jsonc

Dockerfile.alpine-musl

@@ -123,6 +123,7 @@ RUN NOSTR_LIB=$(ls /build/nostr_core_lib/libnostr_core_*.a 2>/dev/null | head -1
     src/tools/tool_task.c src/tools/tool_nostr_list.c src/tools/tool_nostr_block.c src/tools/tool_local.c \
     src/tools/tool_skill.c src/tools/tool_nostr_post.c src/tools/tool_memory.c src/tools/tool_config.c src/tools/tool_cashu_wallet.c src/tools/tool_blossom.c src/trigger_manager.c \
     src/cashu_wallet.c src/nostr_block_list.c src/prompt_template.c src/http_api.c src/setup_wizard.c src/mongoose.c src/debug.c \
+    src/json_to_markdown.c src/context_roles.c src/context_format.c \
     -o /build/didactyl_static \
     $NOSTR_LIB \
     -lsecp256k1 \

Makefile

@@ -41,7 +41,10 @@ SRCS = \
 	$(SRC_DIR)/setup_wizard.c \
 	$(SRC_DIR)/mongoose.c \
 	$(SRC_DIR)/debug.c \
-	$(SRC_DIR)/nostr_block_list.c
+	$(SRC_DIR)/nostr_block_list.c \
+	$(SRC_DIR)/json_to_markdown.c \
+	$(SRC_DIR)/context_roles.c \
+	$(SRC_DIR)/context_format.c
 
 INCLUDES = \
 	-I$(SRC_DIR) \
@@ -113,4 +116,8 @@ clean:
 	rm -f $(TARGET)
 	rm -rf $(BUILD_DIR)
 
-.PHONY: all deps clean force-version
+test_pool: $(NOSTR_LIB) test_pool.c
+	@if [ -z "$(NOSTR_LIB)" ]; then echo "nostr_core_lib static library not found; run 'make deps'"; exit 1; fi
+	$(CC) $(CFLAGS) $(INCLUDES) -o $@ test_pool.c $(NOSTR_LIB) $(LDFLAGS)
+
+.PHONY: all deps clean force-version test_pool

README.md

@@ -54,11 +54,11 @@ Skills compose by adoption-list order (`10123`) and trigger tags carry runtime e
 
 Didactyl will support local inference, which is very privacy preserving. Remote inference does however have it's advantages, and in those cases Didactyl supports using Bitcoin Lightning and eCash inference providers.
 
-## Current Status — v0.2.18
+## Current Status — v0.2.37
 
 **Active build — this project is barely working. Experiment at your own risk.**
 
-> Last release update: v0.2.18 — Add Python test harness and harden increment/push script for git identity and SSH remote handling
+> Last release update: v0.2.37 — Add nostr-core relay reconnect cooldown with stability-based reset to prevent flapping loops
 
 - Connects to configured relays with auto-reconnect and relay state transition logging
 - Publishes configured startup events per relay as each relay becomes connected

build_static.sh

@@ -68,33 +68,52 @@ if [ -d "$SCRIPT_DIR/build" ]; then
     echo "Removed legacy build directory: $SCRIPT_DIR/build"
 fi
 
-# Check if Docker is available
-if ! command -v docker &> /dev/null; then
-    echo "ERROR: Docker is not installed or not in PATH"
+# Detect available OCI container runtime (prefer Docker, fallback to Podman)
+if command -v docker &> /dev/null; then
+    DOCKER_CMD="docker"
+    RUNTIME_NAME="Docker"
+elif command -v podman &> /dev/null; then
+    DOCKER_CMD="podman"
+    RUNTIME_NAME="Podman"
+else
+    echo "ERROR: No supported container runtime found in PATH"
     echo ""
-    echo "Docker is required to build MUSL static binaries."
-    echo "Please install Docker:"
-    echo "  - Ubuntu/Debian: sudo apt install docker.io"
-    echo "  - Or visit: https://docs.docker.com/engine/install/"
+    echo "This build requires either Docker or Podman."
+    echo "Install one of the following:"
+    echo "  - Docker (Ubuntu/Debian): sudo apt install docker.io"
+    echo "  - Podman (Ubuntu/Debian): sudo apt install podman"
     echo ""
     exit 1
 fi
 
-# Check if Docker daemon is running
-if ! docker info &> /dev/null; then
-    echo "ERROR: Docker daemon is not running or user not in docker group"
-    echo ""
-    echo "Please start Docker and ensure you're in the docker group:"
-    echo "  - sudo systemctl start docker"
-    echo "  - sudo usermod -aG docker $USER && newgrp docker"
-    echo "  - Or start Docker Desktop"
-    echo ""
-    exit 1
+# Check if runtime daemon/service is usable
+if ! $DOCKER_CMD info &> /dev/null; then
+    # Docker is installed but current user may not yet have docker-group membership in this shell.
+    # If passwordless sudo is available, transparently fall back to sudo docker.
+    if [ "$DOCKER_CMD" = "docker" ] && command -v sudo &> /dev/null && sudo -n docker info &> /dev/null; then
+        DOCKER_CMD="sudo docker"
+        echo "⚠ Docker requires elevated privileges in this shell; using sudo docker"
+        echo ""
+    else
+        echo "ERROR: $RUNTIME_NAME is installed but not usable"
+        echo ""
+        if [ "$DOCKER_CMD" = "docker" ]; then
+            echo "Please start Docker and ensure your user can access it:"
+            echo "  - sudo systemctl start docker"
+            echo "  - sudo usermod -aG docker $USER && newgrp docker"
+            echo "  - Or log out and back in after adding to docker group"
+            echo "  - Or run this script with sudo"
+        else
+            echo "Please verify Podman is configured for your user:"
+            echo "  - podman info"
+            echo "  - For rootless setup, ensure subuid/subgid are configured"
+        fi
+        echo ""
+        exit 1
+    fi
 fi
 
-DOCKER_CMD="docker"
-
-echo "✓ Docker is available and running"
+echo "✓ $RUNTIME_NAME is available and running"
 echo ""
 
 # Detect architecture (or use explicit target platform override)
@@ -176,10 +195,10 @@ fi
 
 # Check if Alpine base image is cached
 echo "Checking for cached Alpine Docker image..."
-if ! docker images alpine:3.19 --format "{{.Repository}}:{{.Tag}}" | grep -q "alpine:3.19"; then
+if ! $DOCKER_CMD images alpine:3.19 --format "{{.Repository}}:{{.Tag}}" | grep -q "alpine:3.19"; then
     echo "⚠ Alpine 3.19 image not found in cache"
     echo "Attempting to pull Alpine 3.19 image..."
-    if ! docker pull alpine:3.19; then
+    if ! $DOCKER_CMD pull alpine:3.19; then
         echo ""
         echo "ERROR: Failed to pull Alpine 3.19 image"
         echo "This is required for the static build."
@@ -239,11 +258,25 @@ $DOCKER_CMD cp "$CONTAINER_ID:/didactyl_static" "$OUTPUT_DIR/$OUTPUT_NAME" || {
 # Clean up container
 $DOCKER_CMD rm "$CONTAINER_ID" > /dev/null
 
+# If runtime command used sudo, copied file may be root-owned; fix ownership for current user
+if [ ! -w "$OUTPUT_DIR/$OUTPUT_NAME" ]; then
+    if command -v sudo &> /dev/null && sudo -n true &> /dev/null; then
+        sudo chown "$(id -u):$(id -g)" "$OUTPUT_DIR/$OUTPUT_NAME" || true
+    fi
+fi
+
 echo "✓ Binary extracted to: $OUTPUT_DIR/$OUTPUT_NAME"
 echo ""
 
-# Make binary executable
-chmod +x "$OUTPUT_DIR/$OUTPUT_NAME"
+# Make binary executable (fallback to sudo if ownership/permissions still block)
+chmod +x "$OUTPUT_DIR/$OUTPUT_NAME" 2>/dev/null || {
+    if command -v sudo &> /dev/null && sudo -n true &> /dev/null; then
+        sudo chmod +x "$OUTPUT_DIR/$OUTPUT_NAME"
+    else
+        echo "ERROR: Could not set executable bit on $OUTPUT_DIR/$OUTPUT_NAME"
+        exit 1
+    fi
+}
 
 # Verify the binary
 echo "=========================================="

context.logs/20260408T054016_res0001.json

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context.logs/20260408T054752_res0004.json

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context.logs/20260408T061627_res0001.json

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context.logs/20260408T061633_res0002.json

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context.logs/20260408T061637_res0003.json

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context.logs/20260408T061707_res0004.json

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context.logs/20260408T061711_res0005.json

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context.logs/20260408T061719_res0006.json

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+{"id":"gen-1775643439-YfynabqB9Z1xpr8M6rJH","object":"chat.completion","created":1775643439,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"tool_use","message":{"role":"assistant","content":"\n\nFollowing the skill instructions, I'll immediately call `skill_run` with the specified parameters.","refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"toolu_bdrk_013xPRRo4xzACV9cx6kvYScE","function":{"name":"skill_run","arguments":"{\"d_tag\": \"recurse-depth-test\", \"args\": \"again\"}"}}]}}],"usage":{"prompt_tokens":9432,"completion_tokens":98,"total_tokens":9530,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9429,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.06139625,"upstream_inference_prompt_cost":0.05894625,"upstream_inference_completions_cost":0.00245},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061726_res0007.json

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context.logs/20260408T061731_res0008.json

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+{"id":"gen-1775643451-LitXzZilQ8fccnzrYNxk","object":"chat.completion","created":1775643451,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"tool_use","message":{"role":"assistant","content":"\n\nFollowing the skill instructions, I'll call `skill_run` with the specified parameters:","refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"toolu_bdrk_015gvGJTWy7TPx9Eno5n465n","function":{"name":"skill_run","arguments":"{\"d_tag\": \"recurse-depth-test\", \"args\": \"again\"}"}}]}}],"usage":{"prompt_tokens":9432,"completion_tokens":97,"total_tokens":9529,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9429,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0071545,"upstream_inference_prompt_cost":0.0047295,"upstream_inference_completions_cost":0.002425},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061736_res0009.json

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+{"id":"gen-1775643456-QeU1PNltvCqAP9jKWOFM","object":"chat.completion","created":1775643456,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"The recursive skill execution has reached its maximum recursion depth limit. The system returned an error: **\"skill_run max recursion depth exceeded\"** — meaning the runtime has a built-in safeguard that prevents infinite recursive skill calls. The recursion chain has been halted.","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9558,"completion_tokens":62,"total_tokens":9620,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9429,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0069095,"upstream_inference_prompt_cost":0.0053595,"upstream_inference_completions_cost":0.00155},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061741_res0010.json

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+{"id":"gen-1775643462-G6Xsza3aQnB0FLT8sHct","object":"chat.completion","created":1775643462,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"The recursive skill execution test has completed. The result confirms that the runtime has a **built-in recursion depth safeguard** — after a certain depth, it returns the error **\"skill_run max recursion depth exceeded\"**, preventing infinite recursive loops. The recursion chain was successfully halted by the system.","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9633,"completion_tokens":69,"total_tokens":9702,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9429,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0074595,"upstream_inference_prompt_cost":0.0057345,"upstream_inference_completions_cost":0.001725},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061751_res0011.json

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+{"id":"gen-1775643471-k0G9NrevqWO4FNaVaWV6","object":"chat.completion","created":1775643471,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"The recursive depth test has completed. The result confirms that the runtime has a **built-in recursion depth safeguard** — when `recurse-depth-test` repeatedly calls itself, the system eventually returns **\"skill_run max recursion depth exceeded\"**, preventing infinite recursive loops. The recursion chain was successfully halted by the system.","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9641,"completion_tokens":76,"total_tokens":9717,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9429,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0076745,"upstream_inference_prompt_cost":0.0057745,"upstream_inference_completions_cost":0.0019},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061808_res0012.json

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+{"id":"gen-1775643489-wisqdisoevcWXMZr0CxP","object":"chat.completion","created":1775643489,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"tool_use","message":{"role":"assistant","content":"\n\nI'll start by loading the skill with the specified d_tag.","refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"toolu_bdrk_01LcNLoKzhpUex4Mpfzf2QTe","function":{"name":"skill_load","arguments":"{\"d_tag\": \"format-load-test\"}"}}]}}],"usage":{"prompt_tokens":9362,"completion_tokens":75,"total_tokens":9437,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9359,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.06038375,"upstream_inference_prompt_cost":0.05850875,"upstream_inference_completions_cost":0.001875},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T061812_res0013.json

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+{"id":"gen-1775643493-0tqGHH2V4eO32Q2bh1lc","object":"chat.completion","created":1775643493,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"[FORMAT-LOAD-OK] The skill has been loaded successfully. Now to answer your question:\n\n**2 + 2 = 4**","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9595,"completion_tokens":36,"total_tokens":9631,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9014,"cache_write_tokens":393,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.00880325,"upstream_inference_prompt_cost":0.00790325,"upstream_inference_completions_cost":0.0009},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T062608_res0014.json

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+{"id":"gen-1775643969-MRNROtfSpTVsusCHOeso","object":"chat.completion","created":1775643969,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"Hello! 👋 Welcome! I'm your Didactyl agent, ready to help. How can I assist you today?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9455,"completion_tokens":31,"total_tokens":9486,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9452,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.059865,"upstream_inference_prompt_cost":0.05909,"upstream_inference_completions_cost":0.000775},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T062630_res0015.json

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+{"id":"gen-1775643991-XeqBE20ruKhrNtCIYMbi","object":"chat.completion","created":1775643991,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"tool_use","message":{"role":"assistant","content":"\n\nLet me fetch my current kind 0 profile information for you.","refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"toolu_bdrk_01Mp1A4q3GafkkVT9GjbcPDf","function":{"name":"nostr_agent_profile","arguments":"{}"}}]}}],"usage":{"prompt_tokens":9503,"completion_tokens":55,"total_tokens":9558,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9500,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.060765,"upstream_inference_prompt_cost":0.05939,"upstream_inference_completions_cost":0.001375},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T062637_res0016.json

@@ -0,0 +1,27 @@
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+{"id":"gen-1775643997-QULeD4VFI5iM4QTOVVCd","object":"chat.completion","created":1775643997,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"Here's the information in my **kind 0** (profile metadata):\n\n| Field | Value |\n|-------|-------|\n| **Name** | Didactyl Agent |\n| **Display Name** | Didactyl Agent |\n| **About** | A sovereign AI agent on Nostr. <br><br>Mission: Help maintain and answer questions about the Didactyl project. <br><br>https://git.laantungir.net/laantungir/didactyl <br><br>Not your keys, not your Bitcoin. Not your keys, not your Agent. Not your keys, not your Robot. |\n| **Picture** | [Profile Image](https://laantungir.github.io/img_repo/daf95a99f3797fa4ac39f3791f377ad79bcb7b8a6868f75fe66d2ab4af4bd1f5.png) |\n| **Banner** | [Banner Image](https://laantungir.github.io/img_repo/d21c4060632ab3d9d37a6062eeecbdbfbd67f03cb20dbd64838b1ba0d9cd8922.jpg) |\n\nThat's everything currently set in my profile. Would you like to update any of these fields?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9844,"completion_tokens":303,"total_tokens":10147,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":9500,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.014045,"upstream_inference_prompt_cost":0.00647,"upstream_inference_completions_cost":0.007575},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T062820_res0017.json

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+
+         
+{"id":"gen-1775644101-PoKNo40oQncJFqUQSBxg","object":"chat.completion","created":1775644101,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"The capital of France is **Paris**! 🇫🇷\n\nParis is the largest city in France and serves as the country's political, economic, and cultural center. It's known for iconic landmarks like the Eiffel Tower, the Louvre Museum, Notre-Dame Cathedral, and the Champs-Élysées. Is there anything else you'd like to know?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9820,"completion_tokens":87,"total_tokens":9907,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9817,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.06354625,"upstream_inference_prompt_cost":0.06137125,"upstream_inference_completions_cost":0.002175},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T063447_res0018.json

@@ -0,0 +1,23 @@
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+{"id":"gen-1775644488-gXrZTdsTjbgJNtZwHwVN","object":"chat.completion","created":1775644488,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"The capital of Japan is **Tokyo**! 🇯🇵\n\nTokyo is the most populous metropolitan area in the world and serves as Japan's political, economic, and cultural hub. It's known for its blend of ultramodern architecture and traditional temples, bustling neighborhoods like Shibuya and Shinjuku, world-renowned cuisine, and iconic landmarks such as the Tokyo Tower and the Imperial Palace.\n\nIs there anything else you'd like to know?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":9920,"completion_tokens":101,"total_tokens":10021,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":9917,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.06452125,"upstream_inference_prompt_cost":0.06199625,"upstream_inference_completions_cost":0.002525},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T063627_res0019.json

@@ -0,0 +1,13 @@
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+{"id":"gen-1775644588-atmIKam1xtHnSrqvExTM","object":"chat.completion","created":1775644588,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"Hello again! 👋 Good to see you back! How can I help you today?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":10029,"completion_tokens":23,"total_tokens":10052,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":10026,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0632525,"upstream_inference_prompt_cost":0.0626775,"upstream_inference_completions_cost":0.000575},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T064635_res0020.json

@@ -0,0 +1,15 @@
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+{"id":"gen-1775645196-bWqlik700HAAQmIBvo6Y","object":"chat.completion","created":1775645196,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"tool_calls","native_finish_reason":"tool_use","message":{"role":"assistant","content":"\n\nLet me fetch my current kind 0 profile information for you.","refusal":null,"reasoning":null,"tool_calls":[{"type":"function","index":0,"id":"toolu_bdrk_011ZvGQEzTXb9mm88JpuUbCi","function":{"name":"nostr_agent_profile","arguments":"{}"}}]}}],"usage":{"prompt_tokens":10014,"completion_tokens":55,"total_tokens":10069,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":0,"cache_write_tokens":10011,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.06395875,"upstream_inference_prompt_cost":0.06258375,"upstream_inference_completions_cost":0.001375},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

context.logs/20260408T064640_res0021.json

@@ -0,0 +1,27 @@
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+
+         
+{"id":"gen-1775645200-KsS8lUrYLoIvdqeIz4xO","object":"chat.completion","created":1775645200,"model":"anthropic/claude-4.6-opus-20260205","provider":"Amazon Bedrock","system_fingerprint":null,"choices":[{"index":0,"logprobs":null,"finish_reason":"stop","native_finish_reason":"end_turn","message":{"role":"assistant","content":"Here's my current **kind 0** (profile metadata):\n\n| Field | Value |\n|-------|-------|\n| **Name** | Didactyl Agent |\n| **Display Name** | Didactyl Agent |\n| **About** | A sovereign AI agent on Nostr. <br><br>Mission: Help maintain and answer questions about the Didactyl project. <br><br>https://git.laantungir.net/laantungir/didactyl <br><br>Not your keys, not your Bitcoin. Not your keys, not your Agent. Not your keys, not your Robot. |\n| **Picture** | [Profile Image](https://laantungir.github.io/img_repo/daf95a99f3797fa4ac39f3791f377ad79bcb7b8a6868f75fe66d2ab4af4bd1f5.png) |\n| **Banner** | [Banner Image](https://laantungir.github.io/img_repo/d21c4060632ab3d9d37a6062eeecbdbfbd67f03cb20dbd64838b1ba0d9cd8922.jpg) |\n\nThat's everything currently set in my profile. Would you like to update any of these fields?","refusal":null,"reasoning":null}}],"usage":{"prompt_tokens":10355,"completion_tokens":301,"total_tokens":10656,"cost":0,"is_byok":true,"prompt_tokens_details":{"cached_tokens":10011,"cache_write_tokens":0,"audio_tokens":0,"video_tokens":0},"cost_details":{"upstream_inference_cost":0.0142505,"upstream_inference_prompt_cost":0.0067255,"upstream_inference_completions_cost":0.007525},"completion_tokens_details":{"reasoning_tokens":0,"image_tokens":0,"audio_tokens":0}}}

didactyl.code-workspace

@@ -0,0 +1,11 @@
+{
+	"folders": [
+		{
+			"path": "."
+		},
+		{
+			"path": "../../anvil"
+		}
+	],
+	"settings": {}
+}

docs/CONTEXT.md

@@ -79,7 +79,7 @@ Trigger event occurs (DM, cron, subscription, webhook, chain)
   │    ├─ Skill has trigger matching this event?
   │    │    ├─ YES → add to context (layer 1)
   │    │    │    └─ Resolve {{...}} references (layer 2)
-  │    │    │         ├─ Known tool? → execute tool, insert result
+  │    │    │         ├─ Known tool? → execute tool, convert JSON to markdown, insert
   │    │    │         ├─ Adopted skill d-tag? → insert skill content
   │    │    │         └─ Unknown? → resolve to empty
   │    │    │
@@ -87,8 +87,13 @@ Trigger event occurs (DM, cron, subscription, webhook, chain)
   │    │
   │    └─ Continue to next skill in list
   │
-  ├─ Append triggering event payload
-  │    └─ For DM triggers: always append raw message content
+  ├─ Format Document
+  │    ├─ Add `# Agent Name` title
+  │    ├─ Bump all skill headings down one level (# -> ##)
+  │    └─ Concatenate skills with `---` separators
+  │
+  ├─ Build API roles
+  │    └─ Runtime constructs OpenAI-compatible `messages` (system + user + assistant/tool loop)
   │
   ├─ Attach tool schemas (filtered by skill requires_tool tags)
   │
@@ -100,36 +105,26 @@ Trigger event occurs (DM, cron, subscription, webhook, chain)
 
 ### Visualization
 
+The assembled context starts as a single markdown document (skills + resolved variables), then the runtime maps it into API role messages.
+
+```markdown
+# Didactyl Agent
+
+- **npub**: `npub1...`
+
+## Personality
+
+You speak concisely and directly.
+
+---
+
+## Chat
+
+Respond helpfully to the admin.
+Use tools as needed.
 ```
-╔══════════════════════════════════════════╗
-║           CONTEXT WINDOW                 ║
-║                                          ║
-║  ┌────────────────────────────────────┐  ║
-║  │ Layer 1: personality (dm trigger)  │  ║
-║  │                                    │  ║
-║  │  ┌──────────────────────────────┐  │  ║
-║  │  │ Layer 2: {{identity}}        │  │  ║
-║  │  │ You are Didactyl. npub1...   │  │  ║
-║  │  └──────────────────────────────┘  │  ║
-║  │                                    │  ║
-║  │  You speak concisely and directly. │  ║
-║  └────────────────────────────────────┘  ║
-║  ┌────────────────────────────────────┐  ║
-║  │ Layer 1: chat (dm trigger)         │  ║
-║  │                                    │  ║
-║  │  Respond helpfully to the admin.   │  ║
-║  │  Use tools as needed.              │  ║
-║  │                                    │  ║
-║  │  tools: [nostr_query, nostr_dm]    │  ║
-║  └────────────────────────────────────┘  ║
-║  ┌────────────────────────────────────┐  ║
-║  │ DM content (always last)           │  ║
-║  │                                    │  ║
-║  │ "Who mentioned me today?"          │  ║
-║  └────────────────────────────────────┘  ║
-║                                          ║
-╚══════════════════════════════════════════╝
-```
+
+For DM triggers, the runtime appends the incoming DM text as the `user` message. For non-DM triggers, it appends the trigger payload as the `user` message.
 
 ### Why Order Matters
 
@@ -157,11 +152,11 @@ Trigger event occurs (DM, cron, subscription, webhook, chain)
 
 When the engine encounters `{{variable_name}}` in a skill template:
 
-1. **Check known tools** — if it matches a tool name, execute the tool and insert the result
+1. **Check known tools** — if it matches a tool name, execute the tool. The tool returns JSON, which the template engine automatically converts into readable markdown (bullet lists, bold keys) before inserting it into the document.
 2. **Check adopted skills** — if it matches an adopted skill's d-tag, insert that skill's content (layer 2)
 3. **Neither** — resolve to empty (for portability)
 
-This means `{{admin_profile}}` calls the `nostr_admin_profile` tool, while `{{identity}}` inserts the adopted "identity" skill's content. The skill author doesn't need to know which is which — the resolution is transparent.
+This means `{{admin_profile}}` calls the `nostr_admin_profile` tool and formats its JSON output as markdown, while `{{identity}}` inserts the adopted "identity" skill's content. The skill author doesn't need to know which is which — the resolution is transparent.
 
 See [SKILLS.md — Template Variables](SKILLS.md#template-variables) for the full variable table.
 
@@ -219,6 +214,87 @@ Use runtime context inspection endpoints (`GET /api/context/current`, `GET /api/
 
 ---
 
+## Context Debug Logging
+
+Didactyl can persist what it sends to the LLM in markdown files for inspection.
+
+Enable with runtime flag:
+
+```bash
+./didactyl --context-debug full
+```
+
+Or with environment variable:
+
+```bash
+DIDACTYL_CONTEXT_DEBUG=full ./didactyl
+```
+
+Modes:
+
+| Mode | Behavior |
+|------|----------|
+| `off` | Disable context logging |
+| `init` | Log only initial pre-loop context |
+| `full` | Log initial context and each tool-loop turn |
+
+Output files are written to:
+
+- `context.log.md` — rolling latest-first log
+- `context.logs/<timestamp>_init.md` — initial context for a run
+- `context.logs/<timestamp>_t001.md`, `_t002.md`, ... — turn snapshots
+
+### Snapshot Formats
+
+Initial snapshots (`*_init.md`) are the assembled markdown context document (with heading-bump effects). They reflect authored skill markdown before runtime message-role packaging:
+
+```markdown
+# Didactyl Agent
+
+## Anvil Agent
+...
+```
+
+Turn snapshots (`*_tNNN.md`) are human-readable markdown renderings of the OpenAI-compatible `messages` array used for that loop turn:
+
+```markdown
+**Message 1 (system)**
+
+- **role**: system
+- **content**: # Didactyl Agent
+
+## Anvil Agent
+...
+
+---
+
+**Message 2 (assistant)**
+
+- **role**: assistant
+- **tool_calls**:
+  - **name**: nostr_admin_profile
+    - **arguments**: {}
+
+---
+
+**Message 3 (tool)**
+
+- **role**: tool
+- **tool_call_id**: toolu_...
+- **content**:
+  - **success**: true
+  - **content**:
+    - **display_name**: WSB
+```
+
+Notes:
+
+- Internal transport fields such as `_ts` are omitted from snapshots.
+- Tool result payloads are rendered as markdown structures when they contain JSON.
+- Snapshot wrappers use bold message labels rather than markdown headings to avoid conflicting with message content headings.
+
+---
+
 ## Related Documentation
 
 - Skills spec: [SKILLS.md](SKILLS.md)

docs/GENESIS.md

@@ -20,8 +20,8 @@ Minimum practical sections:
 
 - `key.nsec` (or runtime `--nsec` / `DIDACTYL_NSEC`)
 - `admin.pubkey`
-- `llm`
 - `startup_events` (must include kind `10002` relay tags)
+- `encrypted_events` with a `kind:30078` / `d_tag:user-settings` payload containing `global_llm` and `didactyl`
 
 Typical optional sections:
 
@@ -96,17 +96,39 @@ Security behavior:
 
 ## Encrypted Config Events
 
-Didactyl exposes config persistence tools for encrypted self-config on Nostr:
+Didactyl uses encrypted runtime config in two ways:
 
-- `config_store` — publish encrypted kind `30078` config by `d_tag`
-- `config_recall` — query+decrypt kind `30078` config by `d_tag`
+1. At bootstrap time via `genesis.jsonc` `encrypted_events` entries
+2. At runtime via encrypted kind `30078` self-events (`config_store` / `config_recall`)
 
-Recommended tags:
+`encrypted_events` format:
 
-- `d=llm_config`
-- `d=agent_config`
+- `kind` (currently `30078`)
+- `d_tag` (required: `user-settings`)
+- `content` (JSON string payload to encrypt and publish)
 
-These are encrypted to self with NIP-44.
+`d=user-settings` payload shape:
+
+```json
+{
+  "v": 2,
+  "updatedAt": 0,
+  "global_llm": {
+    "provider": "openai",
+    "api_key": "sk-...",
+    "model": "gpt-4o-mini",
+    "base_url": "https://api.openai.com/v1",
+    "max_tokens": 512,
+    "temperature": 0.7
+  },
+  "didactyl": {
+    "admin_pubkey": "npub1...",
+    "dm_protocol": "nip04"
+  }
+}
+```
+
+These values are encrypted to self with NIP-44 before publish.
 
 ---
 
@@ -116,6 +138,8 @@ These are encrypted to self with NIP-44.
 
 That relay list is used as the initial network attachment for querying existing state and publishing startup events.
 
+`encrypted_events` are a separate section and are not part of relay-list derivation.
+
 ---
 
 ## Migration Notes (v0.2.0)

docs/SKILLS.md

@@ -68,7 +68,7 @@ The `content` field of a skill event IS the template — markdown instructions t
 ```json
 {
   "kind": 31123,
-  "content": "system:\n# Spelling and Grammar Checker\n\nYou are a spelling and grammar checker.\n\n## Rules\n\n- Fix spelling errors\n- Fix grammar errors\n- Preserve original formatting\n- Return **ONLY** the corrected text, no explanations\n\nuser:\n{{message}}",
+  "content": "## Spelling and Grammar Checker\n\nYou are a spelling and grammar checker.\n\n### Rules\n\n- Fix spelling errors\n- Fix grammar errors\n- Preserve original formatting\n- Return **ONLY** the corrected text, no explanations\n\nInput text:\n{{message}}",
   "tags": [
     ["d", "spellcheck"],
     ["description", "Check spelling and grammar"],
@@ -80,7 +80,9 @@ The `content` field of a skill event IS the template — markdown instructions t
 }
 ```
 
-- **`content`** — the template in markdown. May include `{{...}}` template variables and `system:` / `user:` role markers. This is what goes into the context window.
+- **`content`** — the template in markdown. May include `{{...}}` template variables. This is what goes into the context window.
+- **Role ownership** — skill authors should write plain markdown instructions, not `system:` / `user:` / `assistant:` markers. The runtime owns role construction and appends trigger payloads (for DM triggers, raw message text) as the `user` message at execution time.
+- **Heading Levels** — The runtime owns the `#` (h1) heading for the document title. All headings in skill content are automatically bumped down one level (`#` becomes `##`, `##` becomes `###`) during context assembly. Skill authors should use `##` for their top-level sections.
 - **`["description", "..."]`** — human-readable description for discovery and UI display.
 - Each `["tag", "value"]` is a separate tag on the Nostr event.
 - The `llm` tag uses a CSS font-stack style fallback chain. See [LLM Fallback Chain](#llm-fallback-chain).

docs/TOOLS.md

@@ -114,8 +114,8 @@ These tools manage skill and trigger lifecycle; skill semantics and trigger exec
 
 | Tool | Description |
 |---|---|
-| `skill_create` | Create or update a skill definition as kind `31123`/`31124` and optionally auto-adopt it |
-| `skill_edit` | Edit an existing self skill by d tag and republish it as kind `31123`/`31124` |
+| `skill_create` | Create or update a skill definition as kind `31123`/`31124` and optionally auto-adopt it (content should be markdown instructions; runtime handles role packaging) |
+| `skill_edit` | Edit an existing self skill by d tag and republish it as kind `31123`/`31124` (content should remain markdown-first) |
 | `skill_list` | List available skills discovered online (agent + admin), with adoption status and optional filters |
 | `skill_adopt` | Adopt a skill by adding its address to kind `10123` adoption list |
 | `skill_remove` | Remove a skill address from kind `10123` adoption list |
@@ -469,8 +469,10 @@ These examples show the JSON structure for tool calls.
   "name": "skill_create",
   "arguments": {
     "d": "weather-skill",
-    "content": "I can tell you the weather.",
+    "content": "## Weather Assistant\n\nYou provide concise weather summaries.\n\nInput:\n{{message}}",
     "description": "Fetches weather data",
+    "trigger": "dm",
+    "filter": "{\"from\":\"admin\"}",
     "auto_adopt": true
   }
 }

genesis.jsonc.example

@@ -14,20 +14,15 @@
     "pubkey": "npub1REPLACE_WITH_ADMIN_PUBKEY"
   },
 
-  // ─── DM Protocol ───────────────────────────────────────────────────
-  // Supported values: "nip04", "nip17", or "both".
-  "dm_protocol": "nip04",
-
-  // ─── LLM Provider ──────────────────────────────────────────────────
-  // OpenAI-compatible endpoint settings.
-  "llm": {
-    "provider": "openai",
-    "api_key": "sk-REPLACE_WITH_API_KEY",
-    "model": "gpt-4o-mini",
-    "base_url": "https://api.openai.com/v1",
-    "max_tokens": 512,
-    "temperature": 0.7
-  },
+  // ─── Encrypted Startup Config Events ───────────────────────────────
+  // These are published as NIP-44 encrypted kind 30078 self-events on first run.
+  "encrypted_events": [
+    {
+      "kind": 30078,
+      "d_tag": "user-settings",
+      "content": "{\"v\":2,\"updatedAt\":0,\"global_llm\":{\"provider\":\"openai\",\"api_key\":\"sk-REPLACE_WITH_API_KEY\",\"model\":\"gpt-4o-mini\",\"base_url\":\"https://api.openai.com/v1\",\"max_tokens\":512,\"temperature\":0.7},\"didactyl\":{\"admin_pubkey\":\"npub1REPLACE_WITH_ADMIN_PUBKEY\",\"dm_protocol\":\"both\",\"max_turns\":40}}"
+    }
+  ],
 
   // ─── HTTP Admin API ────────────────────────────────────────────────
   "api": {
@@ -60,7 +55,14 @@
         ["r", "wss://relay.damus.io"],
         ["r", "wss://relay.primal.net"]
       ]
-    }
+    },
+    {
+      "kind": 10050,
+      "content": "",
+      "tags": [
+        ["relay", "wss://relay.damus.io"],
+        ["relay", "wss://relay.primal.net"]
+      ]
     },
     {
       "kind": 31124,
@@ -76,12 +78,12 @@
     },
     {
       "kind": 31124,
-      "content": "## Recent Conversation\n\n{{nostr_dm_history({\"format\":\"text\",\"limit\":12})}}",
+      "content": "## DM History Context\n\n### Instructions\n- Reference prior conversation naturally when it's relevant to the current request.\n- Do not repeat entire DM history back to the user unless explicitly asked.\n- Use this context to avoid asking questions that were already answered in recent messages.\n\n### Recent DM History (last 10 messages)\n\n{{nostr_dm_history({\"limit\":10,\"format\":\"text\"})}}",
       "tags": [
-        ["d", "dm_history"],
+        ["d", "dm_history_context"],
         ["app", "didactyl"],
         ["scope", "private"],
-        ["description", "DM conversation history for context continuity"],
+        ["description", "DM history context"],
         ["trigger", "dm"],
         ["filter", "{\"from\":\"admin\"}"]
       ]

genesis.lt.didactyl.jsonc

@@ -0,0 +1,85 @@
+{
+  // ─── Agent Identity Keys ───────────────────────────────────────────
+  // Keep nsec private; npub/hex fields can be used for verification/debugging.
+  "key": {
+    "nsec": "nsec17pcmmqxh99rtu5ctpxrdyywdd8f7zfqgf3y2f4fvcm3tk3vpuyqqvlxl4e"
+  },
+
+  // ─── Administrator ─────────────────────────────────────────────────
+  // Admin pubkey (npub or hex) controls privileged interactions.
+  "admin": {
+    "pubkey": "npub1rmz9gu6de0m0u4ysrn39crrud099ahvfgs6pvasl4hpjr5ud7yus54xv06"
+  },
+
+    // ─── HTTP Admin API ───────────────────────────────────────────────
+  // Local API for model/context inspection and runtime controls.
+  "api": {
+    "enabled": true,
+    "port": 8484,
+    "bind_address": "127.0.0.1"
+  },
+
+  // ─── Encrypted Startup Config Events ──────────────────────────────
+  // These are published as NIP-44 encrypted kind 30078 self-events on first run.
+  "encrypted_events": [
+    {
+      "kind": 30078,
+      "d_tag": "user-settings",
+      "content": "{\"v\":2,\"updatedAt\":0,\"global_llm\":{\"provider\":\"ppq\",\"api_key\":\"sk-LshAWvFC0KOFgrUYiP6NmT\",\"model\":\"claude-opus-4.6\",\"base_url\":\"https://api.ppq.ai\",\"max_tokens\":200000,\"temperature\":0.7},\"didactyl\":{\"admin_pubkey\":\"npub1rmz9gu6de0m0u4ysrn39crrud099ahvfgs6pvasl4hpjr5ud7yus54xv06\",\"dm_protocol\":\"both\",\"max_turns\":40}}"
+    }
+  ],
+
+  // ─── Startup Events ───────────────────────────────────────────────
+  // Minimal relay list required for relay pool initialization.
+  "startup_events": [
+    {
+      "kind": 10002,
+      "content": "",
+      "tags": [
+        ["r", "wss://relay.damus.io"],
+        ["r", "wss://relay.primal.net"],
+        ["r", "wss://relay.laantungir.net"]
+      ]
+    },
+    {
+      "kind": 10050,
+      "content": "",
+      "tags": [
+        ["relay", "wss://relay.damus.io"],
+        ["relay", "wss://relay.primal.net"],
+        ["relay", "wss://relay.laantungir.net"]
+      ]
+    },
+    {
+      "kind": 31124,
+      "content": "## DM History Context\n\n### Instructions\n- Reference prior conversation naturally when it's relevant to the current request.\n- Do not repeat entire DM history back to the user unless explicitly asked.\n- Use this context to avoid asking questions that were already answered in recent messages.\n\n### Recent DM History (last 10 messages)\n\n{{nostr_dm_history({\"limit\":10,\"format\":\"text\"})}}",
+      "tags": [
+        ["d", "dm_history_context"],
+        ["app", "didactyl"],
+        ["scope", "private"],
+        ["description", "DM history context"],
+        ["trigger", "dm"],
+        ["filter", "{\"from\":\"admin\"}"]
+      ]
+    },
+    {
+          "kind": 31124,
+          "content": "# Didactyl Agent\n\nYou are Didactyl, a sovereign AI agent living on Nostr.\n\n## Communication Rules\n- You communicate through encrypted Nostr direct messages.\n- Keep responses concise and clear.\n\n## Behavior\n- Be helpful and technically accurate.\n- If unsure, state uncertainty directly.\n- Prefer actionable, practical advice.\n- Use the person's name when messaging them if you know it.\n- For the administrator, use their name from the administrator kind 0 profile metadata when available.\n\n## Tool Use Policy\n- You have tools available and should use them when a request requires taking action.\n- For requests involving local inspection or command execution, call `local_shell_exec` instead of refusing.\n- For posting to Nostr, call `nostr_post` with explicit `kind` and `content`.\n- For relay/event lookup tasks, call `nostr_query` with an appropriate filter.\n- After a tool call, base your answer on the actual tool result.\n- Never claim a tool was run if no tool was executed.\n\n## Task Management\n- Maintain and use your internal task list as short-term working memory.\n- Break long or complex actions into clear tasks before executing them.\n- Update task status as you complete steps so your plan stays accurate.\n\n## Safety\n- Do not claim to have executed actions you did not execute.\n- You may share your public key (npub) with anyone.\n- Never reveal your private key (nsec) under any circumstance.\n\n---template---\n\n- section: admin_identity\n  role: system\n  tool: admin_identity\n  skip_if_empty: true\n\n- section: admin_profile\n  role: system\n  tool: nostr_admin_profile\n  skip_if_empty: true\n\n- section: admin_contacts\n  role: system\n  tool: nostr_admin_contacts\n  skip_if_empty: true\n\n- section: admin_relays\n  role: system\n  tool: nostr_admin_relays\n  skip_if_empty: true\n\n- section: admin_notes\n  role: system\n  tool: nostr_admin_notes\n  skip_if_empty: true\n\n- section: agent_identity\n  role: system\n  tool: agent_identity\n  skip_if_empty: true\n\n- section: agent_profile\n  role: system\n  tool: nostr_agent_profile\n  skip_if_empty: true\n\n- section: agent_contacts\n  role: system\n  tool: nostr_agent_contacts\n  skip_if_empty: true\n\n- section: agent_relays\n  role: system\n  tool: nostr_agent_relays\n  skip_if_empty: true\n\n- section: agent_notes\n  role: system\n  tool: nostr_agent_notes\n  skip_if_empty: true\n\n- section: tasks\n  role: system\n  tool: task_list\n  skip_if_empty: true\n\n- section: dm_history\n  role: expand\n  limit: 12\n\n- section: conversation\n  role: user\n  tool: message_current\n  skip_if_empty: true",
+          "tags": [
+                [
+                      "d",
+                      "didactyl-default"
+                ],
+                [
+                      "app",
+                      "didactyl"
+                ],
+                [
+                      "scope",
+                      "private"
+                ]
+          ]
+    }
+
+  ]
+}

genesis.lt.simon.jsonc

@@ -0,0 +1,98 @@
+{
+  // ─── Agent Identity Keys ───────────────────────────────────────────
+  // Keep nsec private; npub/hex fields can be used for verification/debugging.
+  "key": {
+    "nsec": "nsec17yzm0lyr479z73sgnpf9yuxv0nwclpaff0k45npsh49nddpc3fhqmhh2ph"
+  },
+  // ─── Administrator ─────────────────────────────────────────────────
+  // Admin pubkey (npub or hex) controls privileged interactions.
+  "admin": {
+    "pubkey": "npub1rmz9gu6de0m0u4ysrn39crrud099ahvfgs6pvasl4hpjr5ud7yus54xv06"
+  },
+  // ─── HTTP Admin API ───────────────────────────────────────────────
+  // Local API for model/context inspection and runtime controls.
+  "api": {
+    "enabled": true,
+    "port": 8485,
+    "bind_address": "127.0.0.1"
+  },
+  // ─── Encrypted Startup Config Events ──────────────────────────────
+  // These are published as NIP-44 encrypted kind 30078 self-events on first run.
+  "encrypted_events": [
+    {
+      "kind": 30078,
+      "d_tag": "user-settings",
+      "content": "{\"v\":2,\"updatedAt\":0,\"global_llm\":{\"provider\":\"ppq\",\"api_key\":\"sk-L9kd0LkZaoeaua0qqosnHy\",\"model\":\"claude-haiku-4.5\",\"base_url\":\"https://api.ppq.ai\",\"max_tokens\":512,\"temperature\":0.7},\"didactyl\":{\"admin_pubkey\":\"npub1rmz9gu6de0m0u4ysrn39crrud099ahvfgs6pvasl4hpjr5ud7yus54xv06\",\"dm_protocol\":\"both\",\"max_turns\":40}}"
+    }
+  ],
+  // ─── Startup Events ───────────────────────────────────────────────
+  // Minimal relay list required for relay pool initialization.
+  "startup_events": [
+    {
+      "kind": 10002,
+      "content": "",
+      "tags": [
+        [
+          "r",
+          "wss://relay.damus.io"
+        ],
+        [
+          "r",
+          "wss://relay.primal.net"
+        ],
+        [
+          "r",
+          "wss://relay.laantungir.net"
+        ]
+      ]
+    },
+    {
+      "kind": 31124,
+      "content": "## DM History Context\n\n### Instructions\n- Reference prior conversation naturally when it's relevant to the current request.\n- Do not repeat entire DM history back to the user unless explicitly asked.\n- Use this context to avoid asking questions that were already answered in recent messages.\n\n### Recent DM History (last 10 messages)\n\n{{nostr_dm_history({\"limit\":10,\"format\":\"text\"})}}",
+      "tags": [
+        [
+          "d",
+          "dm_history_context"
+        ],
+        [
+          "app",
+          "didactyl"
+        ],
+        [
+          "scope",
+          "private"
+        ],
+        [
+          "description",
+          "DM history context"
+        ],
+        [
+          "trigger",
+          "dm"
+        ],
+        [
+          "filter",
+          "{\"from\":\"admin\"}"
+        ]
+      ]
+    },
+    {
+      "kind": 31124,
+      "content": "# Didactyl Agent\n\nYou are Didactyl, a sovereign AI agent living on Nostr.\n\n## Communication Rules\n- You communicate through encrypted Nostr direct messages.\n- Keep responses concise and clear.\n\n## Behavior\n- Be helpful and technically accurate.\n- If unsure, state uncertainty directly.\n- Prefer actionable, practical advice.\n- Use the person's name when messaging them if you know it.\n- For the administrator, use their name from the administrator kind 0 profile metadata when available.\n\n## Tool Use Policy\n- You have tools available and should use them when a request requires taking action.\n- For requests involving local inspection or command execution, call `local_shell_exec` instead of refusing.\n- For posting to Nostr, call `nostr_post` with explicit `kind` and `content`.\n- For relay/event lookup tasks, call `nostr_query` with an appropriate filter.\n- After a tool call, base your answer on the actual tool result.\n- Never claim a tool was run if no tool was executed.\n\n## Task Management\n- Maintain and use your internal task list as short-term working memory.\n- Break long or complex actions into clear tasks before executing them.\n- Update task status as you complete steps so your plan stays accurate.\n\n## Safety\n- Do not claim to have executed actions you did not execute.\n- You may share your public key (npub) with anyone.\n- Never reveal your private key (nsec) under any circumstance.\n\n---template---\n\n- section: admin_identity\n  role: system\n  tool: admin_identity\n  skip_if_empty: true\n\n- section: admin_profile\n  role: system\n  tool: nostr_admin_profile\n  skip_if_empty: true\n\n- section: admin_contacts\n  role: system\n  tool: nostr_admin_contacts\n  skip_if_empty: true\n\n- section: admin_relays\n  role: system\n  tool: nostr_admin_relays\n  skip_if_empty: true\n\n- section: admin_notes\n  role: system\n  tool: nostr_admin_notes\n  skip_if_empty: true\n\n- section: agent_identity\n  role: system\n  tool: agent_identity\n  skip_if_empty: true\n\n- section: agent_profile\n  role: system\n  tool: nostr_agent_profile\n  skip_if_empty: true\n\n- section: agent_contacts\n  role: system\n  tool: nostr_agent_contacts\n  skip_if_empty: true\n\n- section: agent_relays\n  role: system\n  tool: nostr_agent_relays\n  skip_if_empty: true\n\n- section: agent_notes\n  role: system\n  tool: nostr_agent_notes\n  skip_if_empty: true\n\n- section: tasks\n  role: system\n  tool: task_list\n  skip_if_empty: true\n\n- section: dm_history\n  role: expand\n  limit: 12\n\n- section: conversation\n  role: user\n  tool: message_current\n  skip_if_empty: true",
+      "tags": [
+        [
+          "d",
+          "didactyl-default"
+        ],
+        [
+          "app",
+          "didactyl"
+        ],
+        [
+          "scope",
+          "private"
+        ]
+      ]
+    }
+  ]
+}

plans/example_context.md

@@ -0,0 +1,214 @@
+# Example Context Window — DM from Admin
+
+This file shows what the assembled context window would look like under the
+proposed markdown format. This is a concrete example based on the "Simon" agent
+configuration seen in the actual context.log.md.
+
+---
+
+## Scenario
+
+- Admin sends a DM: "Hey Simon, who mentioned me on Nostr today?"
+- Triggered skills (adoption list order): `personality`, `chat`
+- The `personality` skill includes `{{identity}}` (layer 2)
+- The `chat` skill has tools enabled
+
+---
+
+## The Assembled Context Document
+
+Below is the **complete markdown document** the runtime assembles before
+splitting into API messages. Everything between the `system:` and `user:`
+markers goes into the system message. Everything after `user:` goes into the
+user message.
+
+---
+
+```markdown
+system:
+# Simon — Didactyl Agent
+
+A sovereign AI agent living on Nostr, named after Simón Bolívar.
+
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+- **NIP-05**: simon@nostr
+
+> Mission: Keep the computers I administer free from foreign powers and surveillance.
+> Not your keys, not your agent.
+
+## Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+## Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+- Be helpful and technically accurate
+- If unsure, state uncertainty directly
+- Use tools when a request requires taking action
+- After a tool call, base your answer on the actual tool result
+- Never claim a tool was run if no tool was executed
+- Maintain your task list as short-term working memory
+- Never reveal your private key (nsec)
+- You may share your public key (npub) with anyone
+
+## Available Tools
+
+You have access to the following tools. Use them when a request requires taking action.
+
+- `nostr_query` — Query Nostr relays for events matching a filter
+- `nostr_dm` — Send an encrypted direct message
+- `nostr_post` — Publish a Nostr event
+- `memory_read` — Read from persistent agent memory
+- `memory_write` — Write to persistent agent memory
+
+## Conversation History
+
+- **You**: Didactyl has started up and is online at 2026-03-23 09:22:48 (version v0.2.12, connected relays: 4/4).
+- **Admin**: What is the capital of England?
+- **You**: London! 🇬🇧
+- **Admin**: How about France?
+- **You**: Paris! 🇫🇷
+
+user:
+Hey Simon, who mentioned me on Nostr today?
+```
+
+---
+
+## What the Runtime Produces
+
+The runtime splits on `system:` and `user:` markers and sends this to the LLM API:
+
+```json
+{
+  "model": "claude-opus-4.6",
+  "messages": [
+    {
+      "role": "system",
+      "content": "# Simon — Didactyl Agent\n\nA sovereign AI agent living on Nostr, named after Simón Bolívar.\n\n- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`\n- **NIP-05**: simon@nostr\n\n> Mission: Keep the computers I administer free from foreign powers and surveillance.\n> Not your keys, not your agent.\n\n## Personality\n\nYou speak concisely and directly.\nYou favor technical precision.\nYou use dry humor sparingly.\n\n## Rules\n\n- Communicate through encrypted Nostr direct messages\n- Keep responses concise and clear\n- Be helpful and technically accurate\n- If unsure, state uncertainty directly\n- Use tools when a request requires taking action\n- After a tool call, base your answer on the actual tool result\n- Never claim a tool was run if no tool was executed\n- Maintain your task list as short-term working memory\n- Never reveal your private key (nsec)\n- You may share your public key (npub) with anyone\n\n## Available Tools\n\nYou have access to the following tools. Use them when a request requires taking action.\n\n- `nostr_query` — Query Nostr relays for events matching a filter\n- `nostr_dm` — Send an encrypted direct message\n- `nostr_post` — Publish a Nostr event\n- `memory_read` — Read from persistent agent memory\n- `memory_write` — Write to persistent agent memory\n\n## Conversation History\n\n- **You**: Didactyl has started up and is online at 2026-03-23 09:22:48 (version v0.2.12, connected relays: 4/4).\n- **Admin**: What is the capital of England?\n- **You**: London! 🇬🇧\n- **Admin**: How about France?\n- **You**: Paris! 🇫🇷"
+    },
+    {
+      "role": "user",
+      "content": "Hey Simon, who mentioned me on Nostr today?"
+    }
+  ],
+  "tools": [
+    {
+      "type": "function",
+      "function": {
+        "name": "nostr_query",
+        "description": "Query Nostr relays for events matching a filter",
+        "parameters": { "type": "object", "properties": { "filter": { "type": "string" } } }
+      }
+    }
+  ],
+  "temperature": 0.7,
+  "max_tokens": 512
+}
+```
+
+---
+
+## How It Compares to Today
+
+### Today (from context.log.md)
+
+```json
+[
+  {
+    "role": "system",
+    "content": "# Didactyl Agent\n\nYou are {\n  \"name\": \"Simon\",\n  \"about\": \"A sovereign AI agent living on Nostr.\\n\\nNamed after Simón José Antonio...\",\n  \"picture\": \"https://upload.wikimedia.org/...\",\n  \"banner\": \"https://upload.wikimedia.org/...\",\n  \"nip05\": \"simon@nostr\"\n}\n\nYour npub: npub1kfc89...\n\n## Rules\n\n- Communicate through encrypted Nostr direct messages\n...\n\n---\n\n## Recent Conversation\n\nAssistant: Didactyl has started up...\nUser: Well hello simon."
+  },
+  {
+    "role": "user",
+    "content": "Well hello simon."
+  }
+]
+```
+
+### Problems with today's format
+
+1. **Raw JSON identity dump** — `You are { "name": "Simon", "about": "..." }` is not readable markdown
+2. **Conversation history as plain text** — `Assistant: ...` / `User: ...` with no structure
+3. **No role marker parsing** — `system:` / `user:` markers in skill content pass through as literal text
+4. **Flat concatenation** — Skills joined with `---` but no document hierarchy
+5. **Tool list not in context** — Tools are in the API payload but the agent has no readable summary of what it can do
+
+### Proposed format improvements
+
+1. **Clean identity block** — Name, npub, mission as formatted markdown
+2. **Structured conversation history** — Markdown list with bold role labels
+3. **Role markers parsed** — `system:` / `user:` split into proper API messages
+4. **Document hierarchy** — Skills contribute sections under a coherent heading structure
+5. **Tool summary in system prompt** — Readable list of available tools and what they do
+
+---
+
+## Triggered Skill Example — Cron Job
+
+For comparison, here is what a cron-triggered skill context would look like:
+
+```markdown
+system:
+# Simon — Didactyl Agent
+
+A sovereign AI agent living on Nostr, named after Simón Bolívar.
+
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+
+## Readme Monitor
+
+Check the readme at the configured URL.
+Compare with the last known version in memory.
+If changed: post it and DM admin a summary.
+
+## Available Tools
+
+- `http_fetch` — Fetch a URL and return the response body
+- `memory_read` — Read from persistent agent memory
+- `memory_write` — Write to persistent agent memory
+- `nostr_post` — Publish a Nostr event
+- `nostr_dm` — Send an encrypted direct message
+
+user:
+Triggering event:
+
+```json
+{
+  "type": "cron",
+  "filter": "0 12 * * *",
+  "created_at": 1742641200
+}
+```
+```
+
+Note: No conversation history — cron triggers have no prior conversation.
+The identity block is shorter because the `readme-monitor` skill only
+includes `{{identity}}`, not `{{personality}}`.
+
+---
+
+## Multi-Turn Tool Conversation
+
+After the initial context is sent, the LLM may request tool calls. The
+multi-turn conversation builds on the messages array:
+
+```json
+[
+  {"role": "system", "content": "# Simon — Didactyl Agent\n\n..."},
+  {"role": "user", "content": "Hey Simon, who mentioned me on Nostr today?"},
+  {"role": "assistant", "content": null, "tool_calls": [
+    {"id": "call_1", "type": "function", "function": {"name": "nostr_query", "arguments": "{\"filter\":{\"#p\":[\"1ec454...\"],\"kinds\":[1],\"since\":1774224000}}"}}
+  ]},
+  {"role": "tool", "tool_call_id": "call_1", "content": "{\"success\":true,\"events\":[...]}"},
+  {"role": "assistant", "content": "Here's who mentioned you today:\n\n- **@alice** posted about your relay setup guide\n- **@bob** quoted your note about NIP-44\n\nWant me to look into any of these in more detail?"}
+]
+```
+
+The tool call/result messages use the standard OpenAI format — these are not
+markdown-formatted because they are machine-to-machine. Only the system and
+user messages benefit from the markdown document format.

plans/example_context_v2.md

@@ -0,0 +1,381 @@
+# Example Context Window v2 — Refined Design
+
+This revision addresses two refinements:
+1. Tools stay JSON; template engine has markdown formatters
+2. Runtime owns h1; skill headings get bumped down one level
+
+---
+
+## The Skills (as authored)
+
+### identity skill (layer 2, no trigger)
+
+```markdown
+You are {{agent_profile}}, a sovereign AI agent living on Nostr.
+
+- **npub**: {{agent_npub}}
+- **NIP-05**: simon@nostr
+
+> Not your keys, not your agent.
+```
+
+Note: No heading. This skill is designed to be embedded (layer 2).
+`{{agent_profile}}` and `{{agent_npub}}` are template variables resolved
+by the formatter registry.
+
+### personality skill (layer 1, trigger: dm)
+
+```markdown
+system:
+# {{identity}}
+
+## Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+## Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+- Be helpful and technically accurate
+- If unsure, state uncertainty directly
+- Use tools when a request requires taking action
+- After a tool call, base your answer on the actual tool result
+- Never claim a tool was run if no tool was executed
+- Never reveal your private key (nsec)
+- You may share your public key (npub) with anyone
+```
+
+### chat skill (layer 1, trigger: dm)
+
+```markdown
+system:
+# Chat
+
+Respond helpfully to the admin.
+Use tools when a request requires taking action.
+
+user:
+{{message}}
+```
+
+---
+
+## Template Resolution (before heading bump)
+
+After resolving `{{identity}}`, `{{agent_profile}}`, `{{agent_npub}}`,
+`{{message}}`, and `{{dm_history}}`:
+
+### personality skill (resolved)
+
+```markdown
+system:
+# You are Simon, a sovereign AI agent living on Nostr.
+
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+- **NIP-05**: simon@nostr
+
+> Not your keys, not your agent.
+
+## Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+## Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+- Be helpful and technically accurate
+- If unsure, state uncertainty directly
+- Use tools when a request requires taking action
+- After a tool call, base your answer on the actual tool result
+- Never claim a tool was run if no tool was executed
+- Never reveal your private key (nsec)
+- You may share your public key (npub) with anyone
+```
+
+### chat skill (resolved)
+
+```markdown
+system:
+# Chat
+
+Respond helpfully to the admin.
+Use tools when a request requires taking action.
+
+user:
+Hey Simon, who mentioned me on Nostr today?
+```
+
+---
+
+## Runtime Assembly
+
+The runtime:
+1. Collects triggered skills in adoption-list order
+2. Bumps all headings in each skill down one level (# -> ##, ## -> ###)
+3. Adds a runtime-level `# title` from the agent identity
+4. Splits on role markers (system: / user:)
+5. Concatenates same-role sections with --- separators
+
+### The assembled document (what gets logged)
+
+```markdown
+system:
+# Simon — Didactyl Agent
+
+## You are Simon, a sovereign AI agent living on Nostr.
+
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+- **NIP-05**: simon@nostr
+
+> Not your keys, not your agent.
+
+### Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+### Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+- Be helpful and technically accurate
+- If unsure, state uncertainty directly
+- Use tools when a request requires taking action
+- After a tool call, base your answer on the actual tool result
+- Never claim a tool was run if no tool was executed
+- Never reveal your private key (nsec)
+- You may share your public key (npub) with anyone
+
+---
+
+## Chat
+
+Respond helpfully to the admin.
+Use tools when a request requires taking action.
+
+user:
+Hey Simon, who mentioned me on Nostr today?
+```
+
+---
+
+## Wait — Problem with the heading bump
+
+Looking at this output, I notice the heading bump creates an awkward result.
+The identity content has no heading, but the personality skill wraps it with
+`# {{identity}}` which becomes `## You are Simon...` after the bump. That
+reads oddly as a heading.
+
+### Better approach: identity as prose, not heading
+
+The identity skill should be **prose content**, not wrapped in a heading by
+the personality skill. The runtime adds the `# title` and the identity
+content flows as the opening paragraph:
+
+### personality skill (revised)
+
+```markdown
+system:
+{{identity}}
+
+# Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+# Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+...
+```
+
+### Assembled document (revised, cleaner)
+
+```markdown
+system:
+# Simon — Didactyl Agent
+
+You are Simon, a sovereign AI agent living on Nostr.
+
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+- **NIP-05**: simon@nostr
+
+> Not your keys, not your agent.
+
+## Personality
+
+You speak concisely and directly.
+You favor technical precision.
+You use dry humor sparingly.
+
+## Rules
+
+- Communicate through encrypted Nostr direct messages
+- Keep responses concise and clear
+- Be helpful and technically accurate
+- If unsure, state uncertainty directly
+- Use tools when a request requires taking action
+- After a tool call, base your answer on the actual tool result
+- Never claim a tool was run if no tool was executed
+- Never reveal your private key (nsec)
+- You may share your public key (npub) with anyone
+
+---
+
+## Chat
+
+Respond helpfully to the admin.
+Use tools when a request requires taking action.
+
+user:
+Hey Simon, who mentioned me on Nostr today?
+```
+
+This reads like a proper document. The runtime adds `# Simon — Didactyl Agent`
+as the document title. Identity content flows as the opening section. Each
+skill's `#` headings get bumped to `##`. The `---` separates skills.
+
+---
+
+## Formatter Registry
+
+The template engine uses formatters to convert tool JSON output to markdown
+for context injection. Here are the formatters needed:
+
+| Template Variable | Tool Called | Formatter | Output |
+|---|---|---|---|
+| `{{agent_identity}}` | `agent_identity` | `format_agent_identity` | Prose with npub in backticks |
+| `{{agent_profile}}` | `nostr_agent_profile` | `format_agent_profile` | Name extracted from kind 0 JSON |
+| `{{admin_profile}}` | `nostr_admin_profile` | `format_admin_profile` | Name and about from kind 0 |
+| `{{admin_notes}}` | `nostr_admin_notes` | `format_admin_notes` | Markdown list of recent notes |
+| `{{admin_relays}}` | `nostr_admin_relays` | `format_admin_relays` | Markdown list of relay URLs |
+| `{{dm_history}}` | built-in | `format_dm_history` | Markdown conversation list |
+| `{{triggering_event}}` | `trigger_event` | `format_trigger_event` | Code block with JSON |
+
+### Example formatter: agent_identity
+
+Input (from tool):
+```json
+{"pubkey":"abc123...","npub":"npub1kfc89..."}
+```
+
+Output (markdown):
+```markdown
+- **npub**: `npub1kfc89dlu9m05tvfnry4l3jjacqpp930sz4kaghcmg9vrrrceg44qf80xae`
+```
+
+### Example formatter: dm_history
+
+Input (from DM history system):
+```json
+[
+  {"role":"assistant","content":"Didactyl has started up...","created_at":1774263529},
+  {"role":"user","content":"Good morning","created_at":1774263548},
+  {"role":"assistant","content":"Good morning! How can I help?","created_at":1774263551}
+]
+```
+
+Output (markdown):
+```markdown
+## Conversation History
+
+- **You**: Didactyl has started up and is online at 2026-03-23 09:22:48.
+- **Admin**: Good morning
+- **You**: Good morning! How can I help?
+```
+
+### Example formatter: triggering_event
+
+Input (from tool):
+```json
+{"type":"cron","filter":"0 12 * * *","created_at":1742641200}
+```
+
+Output (markdown):
+````markdown
+Triggering event:
+
+```json
+{"type":"cron","filter":"0 12 * * *","created_at":1742641200}
+```
+````
+
+---
+
+## Heading Bump Implementation
+
+Simple string operation on each line of skill content:
+
+```c
+// For each line in skill content:
+if (line starts with '#') {
+    output '#' + line;  // prepend one more #
+} else {
+    output line;
+}
+```
+
+Layer 1 skills: bump once (# -> ##)
+Layer 2 skills: bump twice (# -> ###) because they are embedded inside
+a layer 1 skill that already got bumped once, plus one more for being layer 2.
+
+Actually, layer 2 skills get bumped once during their own resolution, and then
+the containing layer 1 skill gets bumped once — so the layer 2 content
+effectively gets bumped twice. This happens naturally if we bump during
+template resolution.
+
+Wait — that depends on ordering. If we:
+1. Resolve {{identity}} first (inserting identity content into personality)
+2. Then bump personality's headings
+
+Then identity headings get bumped once (same as personality). That's correct
+because identity content is now PART of personality.
+
+If we:
+1. Bump identity headings first
+2. Then resolve {{identity}} into personality
+3. Then bump personality's headings
+
+Then identity headings get bumped twice. That's too much.
+
+**Correct order: resolve templates first, then bump headings.**
+
+This means layer 2 content gets the same heading level as the layer 1 skill
+it's embedded in. If the skill author wants layer 2 content at a deeper
+level, they wrap it in a heading in the layer 1 skill:
+
+```markdown
+## Identity
+{{identity}}
+
+## Personality
+...
+```
+
+This is the simplest and most predictable behavior.
+
+---
+
+## Summary of Design Decisions
+
+1. **Tools stay JSON** — No duplication. One tool, one output format.
+2. **Formatter registry** — Template engine converts tool JSON to markdown
+   during `{{variable}}` resolution. Small, focused functions.
+3. **Runtime owns h1** — Adds `# Agent Name` as document title.
+4. **Heading bump** — All skill headings bumped one level (# -> ##).
+5. **Resolve then bump** — Template variables resolved first, then heading
+   bump applied to the complete skill content.
+6. **Skills separated by ---** — Horizontal rules between layer 1 skills.
+7. **Role markers parsed** — `system:` / `user:` at line start split content
+   into API message roles. Unmarked content defaults to system.
+8. **Context log shows markdown** — The assembled document is logged as
+   readable markdown, not raw JSON.

plans/global_llm_alignment.md

@@ -0,0 +1,285 @@
+# Global LLM Config Alignment — Plan
+
+Align LLM configuration storage across Didactyl (C agent) and client-ndk (web pages) so both projects read and write the same canonical schema, as specified in [SETTINGS.md](../../client-ndk/docs/SETTINGS.md).
+
+---
+
+## Problem Statement
+
+LLM provider config is currently stored in two incompatible ways:
+
+| Project | Storage | d-tag | Namespace | Schema |
+|---------|---------|-------|-----------|--------|
+| Didactyl agent | `kind 30078` under agent pubkey | `llm_config` | flat root | `{ provider, api_key, model, base_url, max_tokens, temperature }` |
+| client-ndk pages | `kind 30078` under user pubkey | `user-settings` | `settings.ai` | `{ provider, api_key, model, base_url, max_tokens, temperature, providers[], favorites[] }` |
+| skills-edit.html | `kind 30078` under user pubkey | `llm_config` | flat root | Same as Didactyl — standalone event |
+
+This creates three issues:
+1. **skills-edit.html** writes a standalone `d:llm_config` event under the user pubkey, duplicating the centralized `settings.ai` data
+2. **Didactyl** cannot read the admin user's LLM preferences from their `d:user-settings` event
+3. **Namespace mismatch** — the centralized settings use `ai` (v1) / `global_llm` (v2 target) while Didactyl uses flat fields at root level
+
+---
+
+## Target State
+
+Per [SETTINGS.md §3](../../client-ndk/docs/SETTINGS.md) and [§8](../../client-ndk/docs/SETTINGS.md):
+
+### Canonical Schema — `global_llm` namespace
+
+```json
+{
+  "provider": "ppq",
+  "api_key": "sk-...",
+  "model": "claude-opus-4.6",
+  "base_url": "https://api.ppq.ai",
+  "max_tokens": 200000,
+  "temperature": 0.7,
+  "providers": [
+    {
+      "name": "ppq",
+      "base_url": "https://api.ppq.ai",
+      "api_key": "sk-...",
+      "models": ["claude-opus-4.6", "claude-haiku-4.5"]
+    }
+  ],
+  "favorites": ["claude-opus-4.6"]
+}
+```
+
+### Storage locations after alignment
+
+| Actor | Event | d-tag | Where LLM lives | Notes |
+|-------|-------|-------|------------------|-------|
+| User pubkey | `kind 30078` | `user-settings` | `global_llm` namespace | Canonical source for user LLM prefs |
+| Agent pubkey | `kind 30078` | `llm_config` | flat root | Agent runtime config — flat fields are a subset of `global_llm` |
+| User pubkey | `kind 30078` | `llm_config` | **DEPRECATED** | skills-edit.html stops writing this |
+
+### Cross-project reading
+
+```mermaid
+graph LR
+    subgraph User Pubkey Events
+        US[d:user-settings<br/>global_llm namespace]
+    end
+    subgraph Agent Pubkey Events
+        AL[d:llm_config<br/>flat fields]
+    end
+    subgraph Consumers
+        SE[skills-edit.html]
+        AI[ai.html / skills-tv.html]
+        DA[Didactyl Agent]
+    end
+    SE -->|getUserSettings - global_llm| US
+    AI -->|getUserSettings - global_llm| US
+    DA -->|recall own d:llm_config| AL
+    DA -->|optionally read admin global_llm| US
+```
+
+---
+
+## Detailed Changes
+
+### Phase 1: genesis.jsonc alignment
+
+**File**: [`genesis.jsonc`](../../didactyl/genesis.jsonc)
+
+Current `"llm"` section already uses compatible flat field names. The only issue is `provider` contains a URL (`"https://api.ppq.ai"`) instead of a short name (`"ppq"`).
+
+**Change**: Update `provider` to be a short name. The `base_url` field already carries the URL.
+
+```jsonc
+"llm": {
+  "provider": "ppq",           // was "https://api.ppq.ai"
+  "api_key": "sk-...",
+  "model": "claude-opus-4.6",
+  "base_url": "https://api.ppq.ai",
+  "max_tokens": 200000,
+  "temperature": 0.7
+}
+```
+
+Also update [`genesis.jsonc.example`](../../didactyl/genesis.jsonc.example) to match.
+
+**No C struct changes needed** — `llm_config_t.provider` is `char[32]` which holds short names fine.
+
+### Phase 2: Didactyl C-side — persist writes global_llm-compatible fields
+
+**File**: [`src/tools/tool_model.c`](../../didactyl/src/tools/tool_model.c:33) — `persist_llm_config_nostr()`
+
+Already writes: `provider`, `api_key`, `model`, `base_url`, `max_tokens`, `temperature` — these are the exact flat fields in `global_llm`. **No change needed** to the persist path.
+
+**File**: [`src/main.c`](../../didactyl/src/main.c:844) — `persist_runtime_config_to_nostr()`
+
+Same flat fields. **No change needed**.
+
+### Phase 3: Didactyl C-side — recall from admin `d:user-settings`
+
+**File**: [`src/main.c`](../../didactyl/src/main.c:882) — `recover_missing_runtime_config_from_nostr()`
+
+Currently only queries the agent's own pubkey for `d:llm_config`. Add a fallback path:
+
+1. After failing to find own `d:llm_config`, query admin pubkey for `d:user-settings`
+2. Decrypt with NIP-44 using admin pubkey as sender
+3. Parse JSON, extract `global_llm` object (fall back to `ai` for v1 compat)
+4. Pass the extracted object to `apply_recalled_llm_config()`
+
+**File**: [`src/main.c`](../../didactyl/src/main.c:729) — `apply_recalled_llm_config()`
+
+Already reads flat fields from a JSON object. If we pass it the `global_llm` sub-object, it works as-is. It ignores unknown fields like `providers` and `favorites`. **No change needed**.
+
+**New function**: `fetch_admin_user_settings_llm()` in `main.c`:
+
+```c
+static int fetch_admin_user_settings_llm(didactyl_config_t* cfg, char** out_plaintext) {
+    // 1. Query kind:30078, authors:[admin_pubkey], #d:["user-settings"]
+    // 2. NIP-44 decrypt content (admin encrypted to self — agent cannot decrypt)
+    //    WAIT: Agent cannot decrypt admin's self-encrypted content!
+    //    This path only works if admin explicitly shares config with agent.
+    //    Alternative: Agent reads its OWN d:user-settings if one exists.
+    // 3. Parse JSON, extract global_llm or ai sub-object
+    // 4. Serialize sub-object to *out_plaintext
+}
+```
+
+**Important constraint**: The admin's `d:user-settings` is NIP-44 self-encrypted (admin encrypts to admin). The agent cannot decrypt it because it does not have the admin's private key. 
+
+**Revised approach**: Instead of reading the admin's settings directly, the agent should:
+
+1. **Primary**: Read its own `d:llm_config` (current behavior — works)
+2. **Secondary**: If the admin wants to push LLM config to the agent, they use the `model_set` tool via DM, which calls [`persist_llm_config_nostr()`](../../didactyl/src/tools/tool_model.c:33) to write `d:llm_config` under the agent pubkey
+3. **New path**: The web UI (skills-edit or a future agent-config page) can write `d:llm_config` under the **agent's** pubkey by publishing a NIP-44 encrypted event addressed to the agent. The agent can then decrypt this on recall.
+
+Actually, re-reading [SETTINGS.md §8](../../client-ndk/docs/SETTINGS.md):
+
+> When Didactyl wants to know the user's LLM preferences:
+> `kind:30078, authors:[admin_pubkey], #d:[user-settings]`
+> Parse the `global_llm` field.
+
+This implies the agent CAN read it. But the content is NIP-44 self-encrypted by the admin. The agent would need the admin to encrypt a copy for the agent, OR the admin publishes their settings unencrypted (unlikely for API keys), OR there is a shared-secret mechanism.
+
+**Resolution**: The practical cross-project path is:
+- The **web UI** reads the user's own `global_llm` from `d:user-settings` (it can decrypt its own data)
+- The **web UI** can optionally push config to the agent's `d:llm_config` event (encrypt to agent pubkey)
+- The **agent** reads its own `d:llm_config` as today
+- If the agent needs admin LLM config at startup, the genesis.jsonc provides it, and the setup wizard persists it to `d:llm_config`
+
+So Phase 3 simplifies to: **no C-side recall changes needed for reading admin settings**. The alignment is about ensuring the JSON field names are compatible so the web UI can bridge the two.
+
+### Phase 4: skills-edit.html — migrate to centralized global_llm
+
+**File**: [`../client-ndk/www/skills-edit.html`](../../client-ndk/www/skills-edit.html:1076)
+
+Current behavior:
+- [`fetchNostrLlmConfig()`](../../client-ndk/www/skills-edit.html:1076) subscribes to `kind:30078, #d:['llm_config']` under user pubkey
+- Decrypts and parses the standalone event
+- Merges into `aiConfig`
+
+New behavior:
+- Remove `fetchNostrLlmConfig()` entirely
+- In [`initializeLlmHelperFromSettings()`](../../client-ndk/www/skills-edit.html:1195), read from `pageSettings.global_llm` (with fallback to `pageSettings.ai` for v1 compat)
+- The `pageSettings` object is already populated by `getUserSettings()` and kept live by `onUserSettings()`
+- When the user changes provider/model via the LLM helper, patch back via `patchUserSettings({ global_llm: { ... } })`
+
+**Specific changes**:
+
+1. Remove `fetchNostrLlmConfig()` function (~120 lines)
+2. Remove `llmConfigLoadNonce` variable
+3. Update `initializeLlmHelperFromSettings()`:
+
+```js
+async function initializeLlmHelperFromSettings() {
+  aiConfig = loadAiConfigLocal(aiConfig || getDefaultAiConfig(), AI_STORAGE_KEY);
+
+  // Read from centralized settings (global_llm with ai fallback)
+  const nostrLlm = pageSettings?.global_llm || pageSettings?.ai;
+  if (nostrLlm && typeof nostrLlm === 'object') {
+    aiConfig = mergeAiConfigFromSettings(aiConfig, nostrLlm);
+  }
+
+  aiConfig = saveAiConfigLocal(aiConfig, AI_STORAGE_KEY);
+  renderLlmProviderOptions();
+  renderLlmModelOptions();
+  syncLlmHelperFromSkillInput();
+  fetchLlmModels();
+}
+```
+
+4. Update `selectLlmProvider()` and `toggleCurrentModelFavorite()` to also patch centralized settings:
+
+```js
+// After saving to local storage, also persist to centralized settings
+await patchUserSettings({
+  global_llm: {
+    provider: aiConfig.provider,
+    api_key: aiConfig.api_key,
+    model: aiConfig.model,
+    base_url: aiConfig.base_url,
+    max_tokens: aiConfig.max_tokens,
+    temperature: aiConfig.temperature,
+    providers: aiConfig.providers,
+    favorites: aiConfig.favorites
+  }
+});
+```
+
+5. Add `patchUserSettings` to the imports from `init-ndk.mjs`
+
+### Phase 5: ai-ui.mjs — support global_llm namespace
+
+**File**: [`../client-ndk/www/js/ai-ui.mjs`](../../client-ndk/www/js/ai-ui.mjs:72)
+
+The [`mergeAiConfigFromSettings()`](../../client-ndk/www/js/ai-ui.mjs:72) function already accepts any object with the right flat fields. It works with both `settings.ai` and `settings.global_llm` — the caller just passes the right sub-object.
+
+**No changes needed to ai-ui.mjs itself.** The callers (skills-edit.html and other pages) just need to read from `global_llm` instead of `ai`.
+
+### Phase 6: Update SETTINGS.md
+
+Mark Issue 1 as resolved. Update the audit findings section to reflect that skills-edit.html now uses centralized `global_llm`.
+
+---
+
+## Files Modified
+
+| File | Project | Change |
+|------|---------|--------|
+| `genesis.jsonc` | didactyl | Fix `provider` to short name |
+| `genesis.jsonc.example` | didactyl | Fix `provider` to short name |
+| `../client-ndk/www/skills-edit.html` | client-ndk | Remove `fetchNostrLlmConfig`, read from `pageSettings.global_llm`, add `patchUserSettings` import and calls |
+| `../client-ndk/docs/SETTINGS.md` | client-ndk | Mark Issue 1 resolved |
+
+---
+
+## What Does NOT Change
+
+| Component | Why |
+|-----------|-----|
+| `llm_config_t` C struct | Field names already match `global_llm` flat fields |
+| `persist_llm_config_nostr()` in tool_model.c | Already writes compatible flat fields |
+| `apply_recalled_llm_config()` in main.c | Already reads compatible flat fields, ignores extras |
+| `persist_runtime_config_to_nostr()` in main.c | Already writes compatible flat fields |
+| Agent's own `d:llm_config` event | Stays as agent runtime config under agent pubkey |
+| `ai-ui.mjs` | `mergeAiConfigFromSettings()` already handles the right shape |
+| `ndk-worker.js` | Settings infrastructure unchanged — just a new namespace key |
+
+---
+
+## Migration Safety
+
+- **v1 → v2 compat**: skills-edit.html reads `global_llm || ai` so it works with both old and new settings
+- **Local storage fallback**: `loadAiConfigLocal()` still provides defaults if no Nostr settings exist
+- **No breaking change for agent**: Agent continues reading its own `d:llm_config` — the flat field names are already compatible
+- **Standalone `d:llm_config` under user pubkey**: Becomes orphaned but harmless. Can be cleaned up later.
+
+---
+
+## Encryption Constraint
+
+The agent **cannot** decrypt the admin's `d:user-settings` because it is NIP-44 self-encrypted by the admin. Cross-project LLM config sharing works through:
+
+1. **Genesis file** — admin provides initial LLM config
+2. **Setup wizard** — persists to agent's own `d:llm_config`
+3. **model_set tool** — admin sends DM to update agent LLM config at runtime
+4. **Future**: Web UI could publish a `d:llm_config` event encrypted to the agent's pubkey
+
+This is the correct architecture — the agent should not need to read the admin's private settings directly.

plans/markdown_context_window.md

@@ -0,0 +1,401 @@
+# Markdown Context Window — Implementation Plan
+
+The context window that the agent sees should be a nicely formatted markdown
+document. Skills are the building blocks, and the runtime assembles them into
+a coherent document with proper heading hierarchy, role markers for the LLM
+API, and clean formatting throughout.
+
+See also: [example_context_v2.md](example_context_v2.md) for visual examples.
+
+---
+
+## Design Decisions
+
+### 1. One generic JSON-to-markdown converter
+
+A single function converts any JSON value into readable markdown. No
+per-tool formatters. Tools continue to return JSON; the template engine
+calls the converter when injecting tool output into the markdown document.
+
+```c
+char* json_to_markdown(const char* json_string);
+```
+
+Conversion rules:
+- **String**: output as-is (inline)
+- **Number**: output as-is (inline)
+- **Boolean**: `true` / `false`
+- **Null**: *(empty)*
+- **Object**: bullet list with bold keys
+  - `- **key**: value`
+  - Nested objects indent one level
+- **Array of primitives**: comma-separated inline or bullet list
+- **Array of objects**: bullet list, each item shows its fields
+
+#### Examples
+
+**Simple object**:
+```json
+{"name":"Simon","npub":"npub1kfc89...","nip05":"simon@nostr"}
+```
+```markdown
+- **name**: Simon
+- **npub**: npub1kfc89...
+- **nip05**: simon@nostr
+```
+
+**Nested object**:
+```json
+{"agent":{"name":"Simon","npub":"npub1kfc89..."},"version":"v0.2.12"}
+```
+```markdown
+- **agent**:
+  - **name**: Simon
+  - **npub**: npub1kfc89...
+- **version**: v0.2.12
+```
+
+**Array of objects**:
+```json
+[{"content":"Deployed v0.2.12","created_at":1774263529},{"content":"Working on skills","created_at":1774263600}]
+```
+```markdown
+- **content**: Deployed v0.2.12
+  - **created_at**: 1774263529
+- **content**: Working on skills
+  - **created_at**: 1774263600
+```
+
+**Plain string** (already markdown):
+```json
+"You are Simon, a sovereign AI agent."
+```
+```markdown
+You are Simon, a sovereign AI agent.
+```
+
+#### Edge case: tool content that is already markdown
+
+Some tools may return markdown text in their `content` field (e.g., a skill
+that already formats its own output). The converter detects this: if the
+input is a plain JSON string (not an object/array), it passes through
+unchanged. This means skill authors can return pre-formatted markdown from
+tools if they want precise control.
+
+#### Edge case: very large JSON
+
+For large arrays (e.g., 50 Nostr events from a query), the converter should
+truncate with a note: `*(... and 42 more items)*`. A reasonable default
+limit is 8 items for arrays.
+
+### 2. Runtime owns h1, heading bump for skills
+
+- The runtime emits `# Agent Name` as the document title
+- All headings in skill content get bumped one level: `#` → `##`, `##` → `###`
+- Template variables are resolved BEFORE the heading bump
+- Layer 2 skills (embedded via `{{skill_d_tag}}`) get the same bump as their
+  containing layer 1 skill (because they are resolved inline first)
+
+### 3. Role markers parsed
+
+Skill content can contain `system:` and `user:` markers at the start of a
+line. The runtime splits on these markers to produce the API messages array.
+
+- `system:` — content goes into the system message
+- `user:` — content goes into the user message
+- `assistant:` — content goes into an assistant message (rare, for few-shot)
+- No marker — defaults to `system:`
+- Multiple skills can contribute `system:` sections (concatenated in order)
+- Multiple `user:` sections get concatenated
+
+### 4. Skills separated by horizontal rules
+
+Layer 1 skills are separated by `---` in the assembled document. This gives
+visual structure and helps the LLM distinguish between different instruction
+blocks.
+
+### 5. Context log shows the markdown document
+
+The `append_context_log()` function logs the assembled markdown document
+so you can inspect exactly what the agent sees, formatted as readable
+markdown rather than raw JSON message arrays.
+
+---
+
+## Implementation Steps
+
+### Step 1: Create json_to_markdown converter
+
+**File**: `src/json_to_markdown.c` / `src/json_to_markdown.h`
+
+New module with a single public function:
+
+```c
+// Convert a JSON string to markdown text.
+// Returns a malloc'd string. Caller frees.
+// If input is a plain string (not object/array), passes through unchanged.
+// Objects become bullet lists with bold keys.
+// Arrays become bullet lists of items.
+// Nested structures indent appropriately.
+// Large arrays truncate at max_items with a note.
+char* json_to_markdown(const char* json_string, int max_array_items);
+```
+
+Implementation:
+- Parse JSON with cJSON
+- Recursive walk of the JSON tree
+- Build output string with the append_text pattern used elsewhere
+- Handle: string, number, bool, null, object, array
+- Indent nested structures with 2-space indentation
+- Truncate arrays beyond max_array_items
+
+### Step 2: Create context_roles splitter
+
+**File**: `src/context_roles.c` / `src/context_roles.h`
+
+New module that splits a markdown document on role markers:
+
+```c
+typedef struct {
+    char* system_content;    // Everything under system: markers
+    char* user_content;      // Everything under user: markers
+    char* assistant_content; // Everything under assistant: markers (rare)
+} context_roles_t;
+
+// Split markdown text on role markers (system:, user:, assistant:)
+// at the start of a line. Unmarked content defaults to system.
+// Returns 0 on success, -1 on error.
+int context_roles_split(const char* markdown, context_roles_t* out);
+
+void context_roles_free(context_roles_t* roles);
+```
+
+### Step 3: Create heading_bump utility
+
+**File**: `src/context_format.c` / `src/context_format.h`
+
+Utility functions for markdown formatting in context assembly:
+
+```c
+// Bump all markdown headings in text by one level (# -> ##, ## -> ###, etc.)
+// Returns a malloc'd string. Caller frees.
+char* context_bump_headings(const char* text);
+
+// Build the runtime document title from agent identity.
+// Returns "# Agent Name\n\n" as a malloc'd string.
+char* context_build_title(tools_context_t* ctx);
+```
+
+### Step 4: Modify prompt_template_resolve_inline_variables
+
+**File**: `src/prompt_template.c`
+
+After extracting the `content` field from a tool result (line ~179), pass it
+through `json_to_markdown()` before inserting into the template:
+
+Current code (simplified):
+```c
+cJSON* content = cJSON_GetObjectItemCaseSensitive(root, "content");
+if (content && cJSON_IsString(content)) {
+    replacement_owned = strdup(content->valuestring);
+}
+```
+
+New code:
+```c
+cJSON* content = cJSON_GetObjectItemCaseSensitive(root, "content");
+if (content && cJSON_IsString(content)) {
+    // Try to convert JSON content to markdown
+    char* md = json_to_markdown(content->valuestring, 8);
+    replacement_owned = md ? md : strdup(content->valuestring);
+}
+```
+
+If the content is already a plain string (not JSON), `json_to_markdown`
+passes it through unchanged. If it is a JSON object/array, it gets
+converted to a markdown bullet list.
+
+### Step 5: Modify build_context_from_triggers
+
+**File**: `src/agent.c`
+
+Update the context assembly function to:
+
+1. Resolve template variables (already done)
+2. Bump headings in each skill's content
+3. Build the document title
+4. Concatenate with `---` separators
+5. Return the complete markdown document (not yet split by roles)
+
+Current flow:
+```
+for each matching skill:
+    expand = resolve_skill_references(skill.content)
+    append expand to output with --- separator
+```
+
+New flow:
+```
+title = context_build_title(tools_ctx)
+output = title
+
+for each matching skill:
+    expanded = resolve_skill_references(skill.content)
+    resolved = prompt_template_resolve_inline_variables(expanded)
+    bumped = context_bump_headings(resolved)
+    append bumped to output with --- separator
+```
+
+### Step 6: Modify agent_on_message and agent_on_trigger
+
+**File**: `src/agent.c`
+
+Update the callers of `build_context_from_triggers` to use role splitting:
+
+Current flow:
+```
+dm_context = build_context_from_triggers(DM, ...)
+// dm_context goes entirely into system message
+// user message is the raw DM text
+llm_chat(dm_context, message)
+```
+
+New flow:
+```
+markdown_doc = build_context_from_triggers(DM, ...)
+context_roles_split(markdown_doc, &roles)
+// roles.system_content -> system message
+// roles.user_content -> user message (or fall back to raw DM text)
+llm_chat(roles.system_content, roles.user_content ?: message)
+```
+
+### Step 7: Update append_context_log
+
+**File**: `src/agent.c`
+
+Log the assembled markdown document instead of (or in addition to) the raw
+JSON messages array. The log entry should show the readable markdown so you
+can open `context.log.md` and see exactly what the agent sees.
+
+Current format:
+```
+[{"role":"system","content":"...escaped..."},{"role":"user","content":"..."}]
+```
+
+New format:
+```markdown
+system:
+# Simon — Didactyl Agent
+
+You are Simon...
+
+## Rules
+- ...
+
+---
+
+## Chat
+Respond helpfully...
+
+user:
+Hey Simon, who mentioned me today?
+```
+
+### Step 8: Update DM history formatting
+
+**File**: `src/nostr_handler.c` (or wherever DM history is built)
+
+The conversation history that gets injected into the context should be
+formatted as a markdown list instead of inline JSON:
+
+Current:
+```
+[{"role":"assistant","content":"Started up..."},{"role":"user","content":"Hello"}]
+```
+
+New:
+```markdown
+## Conversation History
+
+- **You**: Started up and is online at 2026-03-23 09:22:48.
+- **Admin**: Hello
+- **You**: Hello! How can I help?
+```
+
+This could be handled by the generic `json_to_markdown` converter if the
+history is passed as JSON, or by a dedicated history formatter if we want
+the cleaner `**You**` / `**Admin**` labels instead of `**role**`.
+
+Note: This is the one place where a small specialized formatter adds real
+value — the generic converter would produce `**role**: assistant` /
+`**content**: Started up...` which is less readable than `**You**: Started up...`.
+A simple function that maps role names to display labels handles this.
+
+### Step 9: Update documentation
+
+**Files**: `docs/SKILLS.md`, `docs/CONTEXT.md`
+
+Update the documentation to reflect:
+- Skill content is markdown with role markers
+- The runtime assembles a coherent markdown document
+- Heading bump behavior
+- The generic JSON-to-markdown conversion for template variables
+- Updated examples showing the new format
+
+---
+
+## Files Changed
+
+| File | Change |
+|------|--------|
+| `src/json_to_markdown.c` | **NEW** — Generic JSON to markdown converter |
+| `src/json_to_markdown.h` | **NEW** — Header for converter |
+| `src/context_roles.c` | **NEW** — Role marker splitter |
+| `src/context_roles.h` | **NEW** — Header for role splitter |
+| `src/context_format.c` | **NEW** — Heading bump and title builder |
+| `src/context_format.h` | **NEW** — Header for context formatting |
+| `src/prompt_template.c` | **MODIFY** — Use json_to_markdown for template variable output |
+| `src/agent.c` | **MODIFY** — Use heading bump, role splitting, updated context log |
+| `src/nostr_handler.c` | **MODIFY** — Format DM history as markdown list |
+| `Makefile` | **MODIFY** — Add new source files to build |
+| `docs/SKILLS.md` | **MODIFY** — Update skill content format documentation |
+| `docs/CONTEXT.md` | **MODIFY** — Update context assembly documentation |
+
+---
+
+## What Does NOT Change
+
+- **Tool implementations** — All tools continue to return JSON. No changes
+  to any tool_*.c files.
+- **LLM API interface** — Still sends OpenAI-compatible messages array.
+  The markdown is the content within the messages, not the transport format.
+- **Runtime tool results** — Tool call/result messages during multi-turn
+  loops stay as JSON. Only template variable injection gets markdown conversion.
+- **Skill event format on Nostr** — Skills are still stored as kind 31123/31124
+  events. The content field is still markdown with role markers. No protocol change.
+- **Adoption list** — No changes to kind 10123 or skill ordering.
+
+---
+
+## Risk Assessment
+
+### Low risk
+- `json_to_markdown` is a pure function with no side effects
+- `context_roles_split` is a simple string splitter
+- `context_bump_headings` is a line-by-line string operation
+- All new code is additive — existing behavior preserved for skills without
+  role markers (defaults to system)
+
+### Medium risk
+- Changing `prompt_template_resolve_inline_variables` to use json_to_markdown
+  could change the content of existing template variables. Need to verify that
+  existing skills still work correctly with markdown output instead of raw JSON.
+  Mitigation: json_to_markdown passes through plain strings unchanged, so
+  skills that already return non-JSON content are unaffected.
+
+### Testing approach
+- Unit test json_to_markdown with various JSON inputs
+- Unit test context_roles_split with various role marker patterns
+- Unit test context_bump_headings with various heading levels
+- Integration test: run existing skills and compare context.log.md output
+  before and after to verify no regressions

plans/multi_model_skill_pipelines.md

@@ -0,0 +1,283 @@
+# Multi-Model Skill Pipelines
+
+## Overview
+
+Didactyl's skill system already supports executing a series of tasks where each task uses a different LLM model — even from different providers. This document describes what works today, what the limitations are, and what improvements would unlock more powerful multi-step workflows.
+
+---
+
+## What Works Today
+
+### Per-Skill LLM Override
+
+Every skill can declare its own `llm` tag with a provider/model spec and fallback chain:
+
+```
+["llm", "anthropic/claude-sonnet-4-20250514, cheap"]
+["llm", "openai/gpt-4o-mini"]
+["llm", "best"]
+```
+
+When a triggered skill fires, the runtime applies the skill's execution parameters (model, temperature, max_tokens) before calling the LLM, then restores the agent defaults afterward. This happens in `apply_trigger_runtime_to_llm_config()` in `src/trigger_manager.c`.
+
+The `llm` tag supports the `provider/model` format. If the tag contains a slash, the runtime parses the provider name and model name separately and overrides both in the LLM config for that execution.
+
+### Chain Triggers Connect Skills Sequentially
+
+The `chain` trigger type fires when another skill completes execution. The `filter` field specifies the source skill's `d` tag:
+
+```json
+{
+  "trigger": "chain",
+  "filter": "source-skill-d-tag"
+}
+```
+
+After a triggered skill completes, `trigger_manager_fire_chains()` in `src/trigger_manager.c` looks for all adopted skills with `trigger=chain` whose `filter` matches the completed skill's d-tag, and fires them.
+
+### Combined: Multi-Model Pipeline
+
+By combining per-skill LLM overrides with chain triggers, you can build a pipeline where each step uses a different model:
+
+```
+DM arrives
+  │
+  ├─ Skill: triage
+  │  llm: openai/gpt-4o-mini (fast/cheap)
+  │  trigger: dm
+  │  → Classifies the request
+  │
+  ├─ chain fires ──→ Skill: deep-analysis
+  │                  llm: anthropic/claude-sonnet-4-20250514 (powerful)
+  │                  trigger: chain, filter: triage
+  │                  → Performs thorough analysis
+  │
+  └─ chain fires ──→ Skill: summarize
+                     llm: openai/gpt-4o-mini (cheap)
+                     trigger: chain, filter: deep-analysis
+                     → Summarizes and DMs admin
+```
+
+Each skill gets its own model, temperature, and max_tokens applied independently.
+
+### Execution Parameter Resolution Per Step
+
+For each triggered skill execution:
+
+1. Start with agent/app defaults
+2. Apply the skill's `llm` tag (parsed as `provider/model` if slash present)
+3. Apply the skill's `temperature` tag if present
+4. Apply the skill's `max_tokens` tag if present
+5. Execute with those effective settings
+6. Restore defaults after the run
+
+---
+
+## Concrete Example
+
+### Skill 1: triage (cheap fast model)
+
+```json
+{
+  "kind": 31123,
+  "content": "## Triage\n\nClassify the incoming message:\n- If it needs deep research, use the memory_save tool to store the classification and key details.\n- If trivial, respond directly.\n\n{{message}}",
+  "tags": [
+    ["d", "triage"],
+    ["description", "Fast triage of incoming messages"],
+    ["trigger", "dm"],
+    ["filter", "{\"from\":\"admin\"}"],
+    ["llm", "openai/gpt-4o-mini, cheap"],
+    ["temperature", "0"],
+    ["max_tokens", "200"]
+  ]
+}
+```
+
+### Skill 2: deep-analysis (powerful model, chains from triage)
+
+```json
+{
+  "kind": 31123,
+  "content": "## Deep Analysis\n\n{{identity}}\n\nRecall the triage classification from memory. Perform thorough analysis using available tools. Save your findings to memory for the next step.\n\nOriginal request context:\n{{message}}",
+  "tags": [
+    ["d", "deep-analysis"],
+    ["description", "Thorough analysis with powerful model"],
+    ["trigger", "chain"],
+    ["filter", "triage"],
+    ["llm", "anthropic/claude-sonnet-4-20250514, best"],
+    ["max_tokens", "2000"],
+    ["requires_tool", "memory_recall"],
+    ["requires_tool", "memory_save"],
+    ["requires_tool", "nostr_query"],
+    ["requires_skill", "identity"]
+  ]
+}
+```
+
+### Skill 3: summarize (cheap model, chains from deep-analysis)
+
+```json
+{
+  "kind": 31123,
+  "content": "## Summarize\n\nRecall the analysis findings from memory. Write a concise summary and DM it to admin.",
+  "tags": [
+    ["d", "summarize"],
+    ["description", "Summarize analysis and notify admin"],
+    ["trigger", "chain"],
+    ["filter", "deep-analysis"],
+    ["llm", "openai/gpt-4o-mini, cheap"],
+    ["max_tokens", "500"],
+    ["requires_tool", "memory_recall"],
+    ["requires_tool", "nostr_dm_send"]
+  ]
+}
+```
+
+---
+
+## Current Capabilities
+
+| Capability | Status | Notes |
+|---|---|---|
+| Different model per skill | ✅ Works | Via `llm` tag on each skill |
+| Different provider per skill | ✅ Works | `provider/model` format in `llm` tag |
+| Sequential multi-step pipelines | ✅ Works | Via `chain` trigger type |
+| Per-step temperature | ✅ Works | Via `temperature` tag |
+| Per-step max_tokens | ✅ Works | Via `max_tokens` tag |
+| Fallback chains per skill | ✅ Works | `provider/model, provider/model, cheap` |
+| LLM config restore after each step | ✅ Works | Runtime saves/restores global config |
+
+---
+
+## Current Limitations
+
+### 1. No Direct Data Passing Between Chain Steps
+
+**Problem:** Chain triggers fire with the *original* triggering event, not the output of the previous skill. Skill B doesn't automatically receive Skill A's output.
+
+**Current workaround:** Use `memory_save` at the end of each step and `memory_recall` at the start of the next. This works but is fragile — memory is a shared scratchpad, not a structured pipeline bus.
+
+**Potential improvement:** Extend the chain trigger event to include the previous skill's final LLM response text. In `trigger_manager_fire_chains()`, the chain event could carry a `"previous_output"` field that the next skill accesses via `{{triggering_event}}`.
+
+### 2. No Conditional Branching
+
+**Problem:** All chain skills matching a source d-tag fire unconditionally. You can't say "if triage classifies as X, run skill A; if Y, run skill B."
+
+**Current workaround:** The chained skill can check the triggering event or memory and decide to do nothing if the condition doesn't match. But it still fires and consumes an LLM call.
+
+**Potential improvement:** Add an optional `chain_condition` tag that the runtime evaluates before firing. Could be a simple JSON match against the previous output, or a keyword presence check.
+
+### 3. No Parallel Fan-Out
+
+**Problem:** Multiple chain skills matching the same source fire sequentially, not in parallel.
+
+**Current workaround:** This is fine for most use cases. True parallelism would require thread-safe LLM config management.
+
+### 4. Chain Depth Limit of 5
+
+**Problem:** `s_chain_depth` in `trigger_manager_fire_chains()` caps at 5 levels to prevent runaway chains.
+
+**Current workaround:** 5 steps is usually sufficient. For longer pipelines, the last step could use a tool to trigger a new chain externally.
+
+**Potential improvement:** Make the depth limit configurable via genesis config.
+
+### 5. Provider Credentials Are Global
+
+**Problem:** The runtime has one set of API keys per provider. If Skill A uses `anthropic/claude-sonnet-4-20250514` and Skill B uses `openai/gpt-4o`, both providers must be configured in the agent's LLM config. There's no per-skill credential storage.
+
+**Current workaround:** Configure all needed providers in the agent's genesis config or via `model_set` tool. The runtime already supports provider switching via the `provider` field in `llm_config_t`.
+
+**Potential improvement:** None needed for most cases — agents typically have a small number of providers configured globally.
+
+---
+
+## Architecture: How It Works in Code
+
+### Trigger Execution Flow
+
+```
+trigger_manager fires skill
+  │
+  ├─ Save current llm_config (old_cfg)
+  │
+  ├─ apply_trigger_runtime_to_llm_config(trigger, &next_cfg)
+  │    ├─ Parse llm tag: "anthropic/claude-sonnet-4-20250514"
+  │    │    ├─ Set cfg->provider = "anthropic"
+  │    │    └─ Set cfg->model = "claude-sonnet-4-20250514"
+  │    ├─ Apply max_tokens if present
+  │    └─ Apply temperature if present
+  │
+  ├─ llm_set_config(&next_cfg)
+  │
+  ├─ Execute skill (agent_on_trigger)
+  │    ├─ Build context from triggered skills
+  │    ├─ Call llm_chat_with_tools_messages()
+  │    └─ Tool loop until completion
+  │
+  ├─ Restore llm_set_config(&old_cfg)
+  │
+  └─ trigger_manager_fire_chains(source_d_tag)
+       ├─ Find chain skills where filter == source_d_tag
+       ├─ For each matching chain skill:
+       │    ├─ Save config again
+       │    ├─ Apply chain skill's llm override
+       │    ├─ Execute chain skill
+       │    ├─ Restore config
+       │    └─ Recursively fire chains (depth < 5)
+       └─ Done
+```
+
+### Key Source Files
+
+| File | Role |
+|---|---|
+| `src/trigger_manager.c` | Trigger matching, chain firing, LLM config override/restore |
+| `src/agent.c` | `agent_on_trigger()` — builds context and runs LLM loop |
+| `src/llm.c` | `llm_chat_with_tools_messages()` — actual LLM API call |
+| `docs/SKILLS.md` | Skill spec including `llm` tag format and chain triggers |
+
+---
+
+## Future Enhancements (Not Yet Implemented)
+
+### Priority 1: Chain Output Forwarding
+
+Pass the previous skill's output to the next chain step via the triggering event:
+
+```c
+// In trigger_manager_fire_chains():
+cJSON_AddStringToObject(event, "previous_output", last_response_text);
+```
+
+The chained skill would access this via `{{triggering_event}}` in its template, seeing:
+
+```json
+{
+  "type": "chain",
+  "source_d_tag": "triage",
+  "previous_output": "Classification: needs deep analysis. Key topics: ..."
+}
+```
+
+### Priority 2: Conditional Chain Firing
+
+Add an optional `chain_condition` tag:
+
+```json
+["chain_condition", "{\"previous_output_contains\":\"needs deep analysis\"}"]
+```
+
+The runtime would check this before firing the chain skill.
+
+### Priority 3: Configurable Chain Depth
+
+```json
+// In genesis.jsonc:
+"trigger_chain_max_depth": 10
+```
+
+---
+
+## Summary
+
+Multi-model skill pipelines work today using per-skill `llm` tags and `chain` triggers. The main gap is data flow between steps (currently requires memory_save/recall workaround). The system is designed for this use case — each skill execution gets its own model config applied and restored — it just needs better inter-step communication to be truly seamless.

plans/nip17_messaging.md

@@ -2,7 +2,7 @@
 
 ## Background
 
-Didactyl currently uses NIP-04 (kind 4) for all DM communication. NIP-17 send support exists via `nostr_handler_send_dm_nip17()` and the `nostr_dm_send_nip17` tool, but there is **no ability to receive NIP-17 messages** and **all agent responses always use NIP-04**.
+Didactyl now supports both NIP-04 and NIP-17 DM flows (including receive path for kind 1059 and protocol-aware auto-reply routing). Remaining work in this plan tracks spec-hardening and bootstrap/discovery details.
 
 The `nostr_core_lib` already has full NIP-17/NIP-59 support including:
 - `nostr_nip17_create_chat_event()` — create kind 14 rumor
@@ -113,28 +113,35 @@ Replace all `nostr_handler_send_dm()` calls with `nostr_handler_send_dm_auto()`.
 
 ### 7. Add kind 10050 startup event support
 
-**File**: `config.json.example`
+**Status**: ✅ Implemented
 
-Add a kind 10050 startup event example so NIP-17 clients can discover the agent's DM relay preferences:
+**Files**:
+- `src/default_events.h` (default startup kinds now include `10050`)
+- `src/setup_wizard.c` (builds kind `10050` tags from current configured relays)
+- `genesis.jsonc.example` (includes kind `10050` startup event)
+- `genesis.lt.didactyl.jsonc` (includes kind `10050` startup event)
 
-```json
-{
-  "kind": 10050,
-  "content": "",
-  "tags": [
-    ["relay", "wss://relay.damus.io"],
-    ["relay", "wss://nos.lol"]
-  ]
-}
-```
-
-**File**: `config.c` — startup event parsing already handles arbitrary kinds, so this should work with no code changes.
+Implementation detail: kind `10050` reuses the same relay set as kind `10002`/current config relays, with `relay` tags as required by NIP-17.
 
 ### 8. Update startup DM to use auto-routing
 
-**File**: `main.c`
+**Status**: ✅ Implemented
 
-The startup status DM at line 255 currently calls `nostr_handler_send_dm()`. Update to `nostr_handler_send_dm_auto()`.
+`main.c` uses `nostr_handler_send_dm_auto()` for startup DM flow.
+
+### 9. Add NIP-17 seal/rumor pubkey verification
+
+**Status**: ✅ Implemented (in core lib)
+
+**File**: `nostr_core_lib/nostr_core/nip017.c`
+
+`nostr_nip17_receive_dm()` now verifies that seal pubkey matches rumor pubkey and rejects mismatches to prevent sender impersonation.
+
+### 10. Recipient kind-10050 relay targeting for sends
+
+**Status**: ⏸ Deferred
+
+Current NIP-17 send path publishes to connected configured relays. Querying and targeting recipient-advertised kind `10050` relays is kept as a future enhancement while debugging current interoperability.
 
 ## Architecture Diagram
 

plans/remove_role_markers_from_skills.md

@@ -0,0 +1,94 @@
+# Remove Role Markers from Skill Content
+
+## Summary
+
+Skill content should always be **system-role instructions** — pure markdown. The `system:`, `user:`, and `assistant:` role markers should not appear in skill content authored by users or the LLM agent.
+
+The runtime already handles `user:` injection internally (appending the DM message or trigger event as the user message). Skill authors writing `user:` in their content is either redundant or creates conflicts (double `user:` markers).
+
+## What Changes
+
+### Principle
+
+- **Skill content = system instructions.** Always. No role markers.
+- **The runtime owns role assignment.** It injects `user:` before the actual input (DM, trigger event).
+- **`context_roles_split()` stays** — the runtime still uses it internally to split the assembled markdown (which includes the runtime-injected `user:` marker) into API messages.
+
+### Files to Change
+
+#### Documentation
+
+| File | Change |
+|------|--------|
+| `docs/SKILLS.md` | Remove role markers from skill content spec and examples. Clarify that skill content is always system instructions. |
+| `docs/CONTEXT.md` | Clarify that `user:` is injected by the runtime, not by skill authors. Update examples. |
+| `docs/TOOLS.md` | Remove role marker references from `skill_create` and `skill_edit` descriptions. |
+
+#### Source Code
+
+| File | Change |
+|------|--------|
+| `src/tools/tools_schema.c` | Remove role marker references from `skill_create` and `skill_edit` tool descriptions and parameter descriptions. |
+| `src/context_roles.c` | **No change** — still needed for runtime-level `user:` splitting. |
+| `src/context_roles.h` | **No change**. |
+| `src/agent.c` | **No change** — already injects `user:` correctly. |
+
+#### Web Pages
+
+| File | Change |
+|------|--------|
+| `~/lt/client-ndk/www/skills-edit.html` | Remove `system:`/`user:`/`assistant:` syntax highlighting from the editor overlay. Update placeholder text. |
+
+#### Plans (historical docs, lower priority)
+
+| File | Change |
+|------|--------|
+| `plans/skills_edit_page.md` | Remove role marker references from content description and highlighting spec. |
+| `plans/markdown_context_window.md` | Update role marker section to clarify runtime-only usage. |
+| `plans/example_context.md` | Update examples to show skill content without role markers. |
+| `plans/example_context_v2.md` | Update examples to show skill content without role markers. |
+| `plans/skills_demo_page.md` | Update template examples to remove `system:`/`user:` from skill templates. |
+
+### What Does NOT Change
+
+- **`context_roles_split()`** — The runtime still assembles a full markdown string with `user:` injected at the boundary, then splits it. This is an internal runtime mechanism, not a skill authoring concern.
+- **skills-tv.html** — This is a separate client-side execution model that builds its own messages array. It can keep its `system:`/`user:` template format since it is not going through the Didactyl agent. However, it could be simplified later.
+- **DM history** — Already uses proper role assignment in the messages array, not role markers.
+
+## Detailed Changes
+
+### docs/SKILLS.md
+
+**Before:**
+```json
+{
+  "content": "system:\n## Spelling and Grammar Checker\n\nYou are a spelling and grammar checker.\n\n### Rules\n...\n\nuser:\n{{message}}"
+}
+```
+
+**After:**
+```json
+{
+  "content": "## Spelling and Grammar Checker\n\nYou are a spelling and grammar checker.\n\n### Rules\n..."
+}
+```
+
+Remove the bullet points about role markers. Add a note:
+
+> Skill content is always system-role instructions. The runtime handles user message injection (DM content, trigger events) automatically. Do not include `system:`, `user:`, or `assistant:` markers in skill content.
+
+### src/tools/tools_schema.c
+
+**skill_create description** — Remove "use role markers (system:/user:/assistant:) at line start when needed; unmarked content defaults to system."
+
+Replace with: "Write content as markdown system instructions. The runtime handles user message injection. Prefer ## for top-level sections."
+
+**skill_create content param** — Remove "May include role markers at line start (system:/user:/assistant:)"
+
+Replace with: "Markdown system instructions for the skill. May include markdown headings/lists/code fences and template variables."
+
+Same pattern for **skill_edit**.
+
+### skills-edit.html
+
+Remove the `.hl-role` CSS class and the regex that highlights `system:`, `user:`, `assistant:` in the editor overlay. Update the textarea placeholder from `system:\nYou are...\n\nuser:\n{{message}}` to just `## My Skill\n\nYou are a helpful assistant.\n\nRespond to {{message}} with...`.

plans/run_skill_architecture.md

@@ -0,0 +1,629 @@
+# Run Skill Architecture — Tradeoff Analysis
+
+## The Question
+
+Skills today are **adopted** — their content is injected into the agent's context window for every matching trigger. This is "always on" and costs context tokens on every LLM call, even when the skill's capability isn't needed.
+
+What if an agent could **use a skill once and be done with it** — like calling a tool?
+
+Two approaches exist. This document analyzes both.
+
+---
+
+## Approach A: Sub-Invocation (Separate LLM Call)
+
+**Concept:** A `skill_run` tool that spins up an entirely separate LLM execution with the skill's instructions as the system prompt, runs its own tool loop to completion, and returns the result to the calling context.
+
+```mermaid
+sequenceDiagram
+    participant Admin
+    participant Agent as Agent - Main Context
+    participant SkillRun as skill_run Executor
+    participant LLM2 as LLM - Skill Context
+
+    Admin->>Agent: Hey, summarize this thread
+    Agent->>Agent: I need the summarize-thread skill
+    Agent->>SkillRun: skill_run d_tag=summarize-thread, args=thread_id
+    SkillRun->>SkillRun: Fetch skill content from Nostr
+    SkillRun->>SkillRun: Build isolated context window
+    SkillRun->>LLM2: System: skill instructions, User: args
+    LLM2->>SkillRun: Tool calls + reasoning
+    SkillRun->>SkillRun: Execute tool loop to completion
+    LLM2->>SkillRun: Final text response
+    SkillRun->>Agent: Return result as tool output
+    Agent->>Admin: Here is the summary...
+```
+
+### How It Works
+
+1. The LLM in the main conversation calls `skill_run` with a `d_tag` (or full `kind:pubkey:d_tag` address) and `args`
+2. The runtime fetches the skill event from Nostr (or adopted skills cache)
+3. A **new, isolated context window** is built:
+   - System prompt = skill content (with `{{...}}` template variables resolved)
+   - User message = the `args` parameter
+   - Tools = the skill's `requires_tool` tags (or full tool set for own skills)
+   - LLM config = the skill's `llm`, `temperature`, `max_tokens` tags (or agent defaults)
+4. A **separate tool loop** runs (same pattern as [`agent_on_trigger()`](src/agent.c:1696))
+5. The final LLM text response is returned as the `skill_run` tool result
+6. The calling context continues with that result
+
+### Advantages
+
+| Advantage | Detail |
+|-----------|--------|
+| **Zero context cost when idle** | The skill's instructions never enter the main context window unless invoked |
+| **Isolation** | The skill runs in its own context — it can't see or interfere with the main conversation |
+| **Per-skill model** | Each skill can use a different LLM model/provider via its `llm` tag |
+| **Sandboxing** | External skills can be restricted to safe tools only |
+| **Composability** | Skills can call other skills via nested `skill_run` (with depth limits) |
+| **Try-before-adopt** | Run someone else's skill once without permanently adopting it |
+| **Clean separation** | The skill is a black box — input in, result out |
+
+### Disadvantages
+
+| Disadvantage | Detail |
+|--------------|--------|
+| **Extra LLM call** | Every `skill_run` invocation costs at least one additional LLM API call (possibly more if the skill uses tools) |
+| **Latency** | The main conversation blocks while the sub-invocation runs its tool loop |
+| **No shared context** | The skill doesn't know about the ongoing conversation — it only sees its `args` |
+| **Complexity** | Requires managing nested execution contexts, preventing infinite recursion, handling timeouts |
+| **Token overhead** | The skill's system prompt is paid for separately — no sharing with the main context |
+
+### Existing Design
+
+This approach is already designed in detail in [`plans/tool_orchestration.md`](plans/tool_orchestration.md) as the `skill_run` tool (Section 2B), including:
+- Schema definition
+- Execution flow with sandbox for external skills
+- Tool safety classification
+- `/run` slash command for admin direct invocation
+
+---
+
+## Approach B: Lazy Injection (On-Demand Context Loading)
+
+**Concept:** A `skill_load` tool that fetches a skill's content and injects it into the **current** context window mid-conversation. The LLM then uses those instructions in subsequent reasoning within the same call.
+
+```mermaid
+sequenceDiagram
+    participant Admin
+    participant Agent as Agent - Single Context
+    participant LLM as LLM
+
+    Admin->>Agent: Hey, summarize this thread
+    Agent->>LLM: System + User message
+    LLM->>Agent: I need the summarize-thread skill. Call skill_load.
+    Agent->>Agent: Fetch skill content, append to messages
+    Agent->>LLM: ...previous messages + skill content as new system message
+    LLM->>Agent: Now executing with skill instructions in context...
+    Agent->>Admin: Here is the summary...
+```
+
+### How It Works
+
+1. The LLM calls `skill_load` with a `d_tag`
+2. The runtime fetches the skill content
+3. The skill content is returned as the tool result (or injected as a new system message)
+4. The LLM continues in the **same context window**, now with the skill instructions available
+5. No separate LLM call — the skill instructions become part of the ongoing conversation
+
+### Advantages
+
+| Advantage | Detail |
+|-----------|--------|
+| **No extra LLM call** | The skill instructions are loaded into the current context — no sub-invocation overhead |
+| **Shared context** | The skill has full access to the conversation history and can reason about it |
+| **Lower latency** | No separate tool loop — the LLM just continues with more information |
+| **Simpler implementation** | Just fetch content and return it as a tool result — no nested execution management |
+| **Natural flow** | The LLM decides when it needs more instructions and loads them dynamically |
+
+### Disadvantages
+
+| Disadvantage | Detail |
+|--------------|--------|
+| **Context window cost** | Once loaded, the skill content stays in the context for the rest of the conversation |
+| **No isolation** | The skill instructions mix with everything else — potential for instruction conflicts |
+| **No per-skill model** | The skill runs on whatever model the current conversation is using |
+| **No sandboxing** | The skill's instructions execute with full tool access (same as the main context) |
+| **Accumulation** | Loading multiple skills grows the context window — could hit token limits |
+| **No clean boundary** | The LLM might partially follow skill instructions or blend them with other context |
+
+### Implementation
+
+This is essentially what `skill_get` already does — it returns skill content. The difference would be:
+- `skill_get` returns metadata + content as a JSON blob for the LLM to read
+- `skill_load` would inject the content as a system-level instruction that the LLM should follow
+
+In practice, the LLM can already do this today by calling `skill_get` and then reasoning about the returned content. The question is whether a dedicated `skill_load` tool that injects content at the system level would be meaningfully different.
+
+---
+
+## Approach C: Hybrid — Both, With Different Use Cases
+
+The two approaches serve different needs and are not mutually exclusive:
+
+```mermaid
+graph TD
+    A[Agent needs a skill capability] --> B{What kind of need?}
+    B -->|One-shot task with clear input/output| C[skill_run - Sub-invocation]
+    B -->|Need skill knowledge in ongoing conversation| D[skill_load - Lazy injection]
+    B -->|Always need this skill| E[skill_adopt - Permanent adoption]
+
+    C --> F[Isolated execution, result returned]
+    D --> G[Instructions loaded into current context]
+    E --> H[Instructions in every matching trigger context]
+
+    style C fill:#e1f5fe
+    style D fill:#fff3e0
+    style E fill:#e8f5e9
+```
+
+### When to Use Each
+
+| Scenario | Best Approach | Why |
+|----------|--------------|-----|
+| Run a friend's skill to try it | `skill_run` | Isolation + sandbox for untrusted code |
+| Spell-check this message | `skill_run` | Clear input/output, no conversation context needed |
+| Deploy my website | `skill_run` | Task-oriented, uses different tools, benefits from isolation |
+| I need to know the formatting rules for Nostr posts | `skill_load` | Reference material for the current conversation |
+| Help me write a skill (load the skill-authoring guide) | `skill_load` | The LLM needs the instructions as ongoing context |
+| Always respond in a certain personality | `skill_adopt` | Needed in every conversation |
+
+### The Key Distinction
+
+- **`skill_run`** = "Do this task for me and give me the result" (function call semantics)
+- **`skill_load`** = "Teach me how to do this so I can do it myself" (knowledge injection)
+- **`skill_adopt`** = "I always need to know this" (permanent context)
+
+---
+
+## Relationship to Existing Architecture
+
+### Chain Triggers vs skill_run
+
+[Chain triggers](plans/multi_model_skill_pipelines.md) already provide sequential skill execution — Skill A completes, then Skill B fires. But chains are **pre-configured** (declared in skill tags) and **event-driven** (fire automatically).
+
+`skill_run` is **dynamic** — the LLM decides at runtime which skill to invoke based on the conversation. It's the difference between a cron job and a function call.
+
+### Maturity Levels
+
+The [`tool_orchestration.md`](plans/tool_orchestration.md) plan defines three maturity levels that apply to `skill_run`:
+
+| Level | Execution | LLM? |
+|-------|-----------|------|
+| `draft` | LLM interprets skill instructions | Yes |
+| `guided` | LLM with forced tool_choice | Yes, constrained |
+| `hardened` | Deterministic step executor | No |
+
+A `hardened` skill via `skill_run` is essentially a **tool** — no LLM call, just sequential tool execution. This is the ultimate "skill as tool" pattern.
+
+### Recursive skill_run
+
+`skill_run` can call `skill_run` (a skill invokes another skill). This needs a depth limit (like the chain depth limit of 5 in [`trigger_manager_fire_chains()`](src/trigger_manager.c)). Each level gets its own context window and tool loop.
+
+---
+
+## Implementation Considerations
+
+### For skill_run (Sub-Invocation)
+
+The core implementation reuses the existing [`agent_on_trigger()`](src/agent.c:1696) pattern:
+
+1. **New function:** `execute_skill_run()` in [`tool_skill.c`](src/tools/tool_skill.c)
+2. **Context building:** Reuse [`build_context_from_triggers()`](src/agent.c) pattern but for a single skill
+3. **Tool loop:** Same pattern as `agent_on_trigger()` — call LLM, execute tools, repeat
+4. **LLM config:** Save/restore pattern from [`apply_trigger_runtime_to_llm_config()`](src/trigger_manager.c)
+5. **Sandbox:** Filter tools array based on skill origin (own vs external)
+6. **Depth tracking:** Static or thread-local counter to prevent infinite recursion
+7. **Result capture:** Capture the final LLM text response and return it as the tool result
+
+### For skill_load (Lazy Injection)
+
+Minimal new code needed:
+
+1. **New tool:** `skill_load` in [`tool_skill.c`](src/tools/tool_skill.c)
+2. **Fetch:** Same as `skill_get` — fetch skill content from cache or Nostr
+3. **Return:** Return the skill content as the tool result with a wrapper indicating these are instructions to follow
+4. **Alternative:** Inject as a system message in the messages array (requires access to the messages array from within tool execution, which the current architecture doesn't support)
+
+The simpler version (return as tool result) works today with minimal changes. The LLM receives the skill content as a tool response and can reason about it. The more sophisticated version (system message injection) would require architectural changes to how tools interact with the message array.
+
+---
+
+## Approach D: Modes — Skill Packages as Agent Personalities
+
+### The Roo Code Parallel
+
+Roo Code has **modes** — Architect, Code, Ask, Debug — each with its own:
+- System prompt / personality
+- Set of allowed tools
+- File access restrictions
+- Model preferences
+
+A mode is essentially a **curated bundle of capabilities** that the agent switches into. You don't load the Architect's planning instructions into the Code mode's context — you *switch modes entirely*.
+
+This maps directly onto Didactyl's skill system.
+
+### Modes as Skill Packages
+
+A **mode** is a named collection of skills that, when activated, replaces the agent's current context window composition. It's a different adoption list — a different set of Layer 1 skills, different tools, potentially a different LLM model.
+
+```mermaid
+graph TD
+    A[Agent receives /mode architect] --> B[Look up mode: architect]
+    B --> C[Mode defines skills: planning, analysis, design-patterns]
+    C --> D[Mode defines model: best]
+    C --> E[Mode defines tools: memory_*, nostr_query, skill_search]
+    D --> F[Reconfigure agent context for this session]
+    E --> F
+    F --> G[Agent now operates as Architect]
+    G --> H[All subsequent messages use architect context]
+```
+
+### Mode Definition as a Skill
+
+A mode is itself a skill — a meta-skill that declares which other skills compose it:
+
+```json
+{
+  "kind": 31123,
+  "content": "You are operating in Architect mode.\n\nYour role is to plan, design, and analyze before implementation.\n- Break down complex problems into clear steps\n- Create technical specifications\n- Design system architecture\n- Do NOT write implementation code\n\n{{identity}}\n{{planning-guidelines}}",
+  "tags": [
+    ["d", "mode-architect"],
+    ["description", "Architect mode - planning and design"],
+    ["mode", "architect"],
+    ["llm", "best"],
+    ["temperature", "0.7"],
+    ["requires_skill", "identity"],
+    ["requires_skill", "planning-guidelines"],
+    ["requires_tool", "memory_save"],
+    ["requires_tool", "memory_recall"],
+    ["requires_tool", "nostr_query"],
+    ["requires_tool", "skill_search"],
+    ["requires_tool", "skill_list"]
+  ]
+}
+```
+
+```json
+{
+  "kind": 31123,
+  "content": "You are operating in Cheap mode.\n\nYou are a fast, cost-effective assistant.\n- Answer questions directly and concisely\n- Use minimal tokens\n- Do not over-explain\n\n{{identity}}",
+  "tags": [
+    ["d", "mode-cheap"],
+    ["description", "Cheap mode - fast answers with inexpensive model"],
+    ["mode", "cheap"],
+    ["llm", "openai/gpt-4o-mini, cheap"],
+    ["temperature", "0.3"],
+    ["max_tokens", "500"],
+    ["requires_skill", "identity"],
+    ["requires_tool", "nostr_query"],
+    ["requires_tool", "memory_recall"]
+  ]
+}
+```
+
+### How Modes Interact with skill_run
+
+Your example — `/skill_run cheap "how many files are in the directory"` — reveals two distinct use cases:
+
+**1. One-shot mode invocation (`skill_run`):**
+Run a single task using a mode's configuration, then return to normal. The mode's model, tools, and personality apply for just that one execution.
+
+```
+/skill_run mode-cheap "how many files are in the directory"
+```
+
+This uses the `skill_run` sub-invocation pattern: spin up an isolated context with the `mode-cheap` skill's instructions, use its `llm` tag (cheap model), execute the tool loop, return the result. The main conversation continues unchanged.
+
+**2. Persistent mode switch (`/mode`):**
+Switch the agent's operating mode for all subsequent messages until switched again.
+
+```
+/mode architect
+> Now operating in Architect mode. Using best model. Planning tools available.
+
+/mode cheap
+> Now operating in Cheap mode. Using gpt-4o-mini. Minimal tool set.
+
+/mode default
+> Restored default mode.
+```
+
+This changes which skills compose the agent's context window for DM conversations. Instead of the normal adoption-list-order composition, the mode's skill set takes over.
+
+### Mode Architecture
+
+```mermaid
+graph TD
+    subgraph Current Architecture
+        A[Adoption List - kind 10123] --> B[All adopted skills with dm trigger]
+        B --> C[Context window for every DM]
+    end
+
+    subgraph Mode Architecture
+        D[Adoption List - kind 10123] --> E{Active mode?}
+        E -->|No mode| F[Default: all adopted dm-triggered skills]
+        E -->|Mode active| G[Mode skill + its requires_skill dependencies]
+        F --> H[Context window]
+        G --> H
+    end
+
+    subgraph Mode Lifecycle
+        I[/mode architect] --> J[Set active mode = mode-architect]
+        J --> K[Subsequent DMs use architect context]
+        K --> L[/mode default]
+        L --> M[Clear active mode, restore normal]
+    end
+```
+
+### The Three Invocation Patterns (Updated)
+
+| Pattern | Command | Context | Persists? | Model |
+|---------|---------|---------|-----------|-------|
+| **Adopted skills** | *(automatic)* | All matching skills in adoption list | Always | Agent default |
+| **One-shot skill** | `/skill_run d_tag args` | Isolated sub-invocation | No | Skill's `llm` tag |
+| **Mode switch** | `/mode name` | Mode's skill set replaces DM context | Until changed | Mode's `llm` tag |
+| **One-shot mode** | `/skill_run mode-name args` | Isolated sub-invocation with mode config | No | Mode's `llm` tag |
+
+### Mode Storage
+
+The active mode is ephemeral runtime state — not persisted to Nostr. It's a session concept:
+
+```c
+// In agent.c or a new mode_manager
+static char g_active_mode_d_tag[TRIGGER_SKILL_D_TAG_MAX] = {0};  // empty = default mode
+
+void agent_set_mode(const char* mode_d_tag);  // NULL or empty = clear mode
+const char* agent_get_mode(void);
+```
+
+When a mode is active, [`build_context_from_triggers()`](src/agent.c) checks the mode skill first instead of scanning all DM-triggered skills. The mode skill's `requires_skill` tags define which other skills are included (Layer 2).
+
+### Mode Discovery
+
+Modes are just skills with a `["mode", "name"]` tag. They're discoverable the same way:
+
+```
+/skill_search mode                    # find all mode skills
+/skill_run mode-architect "plan X"    # try a mode one-shot
+/mode architect                       # switch to it persistently
+```
+
+Since modes are Nostr events, they're **shareable**. Someone publishes a "code-reviewer" mode with specific skills, model preferences, and tool restrictions. You adopt it and `/mode code-reviewer` to activate it.
+
+### Relationship to Roo Code Modes
+
+| Roo Code | Didactyl Equivalent |
+|----------|-------------------|
+| Mode definition (`.roomodes`) | Skill event with `["mode", "name"]` tag |
+| Mode's system prompt | Skill content (markdown instructions) |
+| Mode's allowed tools | `["requires_tool", "..."]` tags |
+| Mode's file restrictions | `["requires_tool", "local_file_read"]` presence/absence |
+| Mode's model | `["llm", "..."]` tag |
+| `/mode architect` | `/mode architect` |
+| Mode switching | `/mode name` slash command |
+| Custom modes | Create a skill with `["mode", "name"]` tag |
+
+The key difference: Roo Code modes are local config files. Didactyl modes are **Nostr events** — portable, shareable, discoverable, and adoptable across agents.
+
+---
+
+## Modes vs. Multiple Agents
+
+### The Tradeoff
+
+Instead of switching one agent between Architect and Coder modes, you could run two separate agents — one Architect, one Coder. Each has its own nsec, its own skills, its own model. This is already possible today with no code changes.
+
+| Dimension | Modes (one agent) | Multiple Agents (separate binaries) |
+|-----------|-------------------|-------------------------------------|
+| **Resource cost** | One process, one relay connection, one LLM config | N processes, N relay connections, N LLM configs |
+| **Context isolation** | Mode switch replaces context; previous mode's state is lost | Each agent has its own persistent context and memory |
+| **Shared memory** | Same agent = same memory store. Architect's notes are available to Coder. | Separate agents = separate memory. Must explicitly share via Nostr events. |
+| **Conversation continuity** | Mode switch mid-conversation is seamless — same DM thread | Different agents = different DM threads. Admin must context-switch. |
+| **Identity** | One npub. External observers see one entity. | N npubs. Each agent is a distinct Nostr identity. |
+| **Specialization depth** | Mode is a context overlay — personality + tools + model. Shallow specialization. | Full agent is deeply specialized — its own memories, its own learned patterns, its own social graph. |
+| **Operational complexity** | One binary, one systemd service, one genesis config | N binaries, N services, N configs, N API keys |
+| **Failure blast radius** | Agent crashes = all modes unavailable | One agent crashes = others still running |
+| **Skill sharing** | All modes share the same adoption list (modes just filter it) | Each agent has its own adoption list |
+| **Admin UX** | `/mode architect` — instant switch | DM a different npub — requires client support or manual switching |
+
+### When Modes Win
+
+- **Quick context switching** — "plan this, then code it" in one conversation
+- **Shared state** — the Architect's plan is in memory when you switch to Coder mode
+- **Low overhead** — one process, one set of relay connections
+- **Simple admin UX** — `/mode architect`, `/mode coder`, done
+- **Cost control** — `/mode cheap` for quick questions, `/mode deep` for complex analysis
+
+### When Separate Agents Win
+
+- **Deep specialization** — an agent that has spent weeks learning about your codebase vs. one that knows your financial data. These are fundamentally different knowledge bases.
+- **Different trust boundaries** — one agent has shell access, another only has Nostr access
+- **Different LLM providers** — one on Anthropic, one on OpenAI, one on local Ollama
+- **Concurrent operation** — both agents working simultaneously on different tasks
+- **Fault isolation** — one agent's crash doesn't affect the other
+- **Team simulation** — the admin can DM the Architect to plan, then DM the Coder to implement, and each agent maintains its own persistent context about its domain
+
+### The Hybrid: Modes + Clones
+
+The most powerful pattern combines both:
+
+1. **Start with one agent** with modes for quick context switching
+2. **Clone it** (via [`agent_clone`](plans/agent_clone.md)) when you need deep specialization
+3. **Specialize the clone** — different skills, different model, different tools
+4. **Use modes within each clone** for further flexibility
+
+```
+Admin's Agent Fleet:
+├── Agent Alpha (generalist)
+│   ├── /mode architect  — planning
+│   ├── /mode coder      — implementation
+│   └── /mode cheap      — quick questions
+├── Agent Beta (clone, specialized: infrastructure)
+│   ├── /mode deploy     — deployment workflows
+│   └── /mode monitor    — system monitoring
+└── Agent Gamma (clone, specialized: content)
+    ├── /mode writer     — long-form content
+    └── /mode social     — Nostr social engagement
+```
+
+---
+
+## Multi-Agent Didactyl: What Would It Take?
+
+### Current Single-Agent Architecture
+
+The codebase is deeply single-agent. Global state is everywhere:
+
+| Global | File | Purpose |
+|--------|------|---------|
+| `static didactyl_config_t* g_cfg` | [`agent.c`](src/agent.c:27), [`nostr_handler.c`](src/nostr_handler.c:22) | Agent config including keys |
+| `static tools_context_t g_tools_ctx` | [`agent.c`](src/agent.c:28) | Tool execution context |
+| `static trigger_manager* g_trigger_manager` | [`agent.c`](src/agent.c:29) | Trigger state |
+| `static nostr_relay_pool_t* g_pool` | [`nostr_handler.c`](src/nostr_handler.c:23) | Single relay pool |
+| `static llm_config_t g_cfg` | [`llm.c`](src/llm.c:20) | LLM configuration |
+| `static char* g_admin_kind0_json` | [`nostr_handler.c`](src/nostr_handler.c:34) | Admin context cache |
+| `static char* g_agent_kind0_json` | [`nostr_handler.c`](src/nostr_handler.c:47) | Agent self-context cache |
+| `static char* g_memory_plain` | [`tool_memory.c`](src/tools/tool_memory.c:20) | Agent memory |
+| 20+ `pthread_mutex_t` globals | Various | Thread safety for all the above |
+
+Every module assumes there is exactly one agent identity, one relay pool, one LLM config, one admin, one set of keys. The keys are used directly via `g_cfg->keys.private_key` in ~40 places across [`nostr_handler.c`](src/nostr_handler.c) for signing, encrypting, and decrypting.
+
+### Three Paths to Multi-Agent
+
+#### Path 1: Multiple Processes (Current — No Code Changes)
+
+Run N separate Didactyl binaries, each with its own genesis config and nsec. This is what [`DECENTRALIZED_DIDACTYL.md`](plans/DECENTRALIZED_DIDACTYL.md) describes.
+
+**Pros:** Works today. Zero code changes. Full isolation.
+**Cons:** N × resource cost. N × operational complexity. No shared state.
+
+```
+Server
+├── /home/didactyl-architect/  (systemd: didactyl-architect.service)
+│   ├── genesis.jsonc          (nsec1..., model: best)
+│   └── didactyl               (binary)
+├── /home/didactyl-coder/      (systemd: didactyl-coder.service)
+│   ├── genesis.jsonc          (nsec2..., model: fast)
+│   └── didactyl               (binary)
+└── /home/didactyl-cheap/      (systemd: didactyl-cheap.service)
+    ├── genesis.jsonc          (nsec3..., model: cheap)
+    └── didactyl               (binary)
+```
+
+#### Path 2: Multi-Agent Single Process (Major Refactor)
+
+Refactor all global state into an `agent_instance_t` struct. Run multiple agent instances in one process, sharing the relay pool and HTTP server.
+
+**What changes:**
+
+```c
+typedef struct agent_instance {
+    didactyl_config_t config;
+    tools_context_t tools_ctx;
+    trigger_manager_t trigger_manager;
+    llm_config_t llm_config;
+    // ... all per-agent state
+} agent_instance_t;
+
+// Instead of:
+static didactyl_config_t* g_cfg;
+// Becomes:
+agent_instance_t* agents;
+int agent_count;
+```
+
+**Scope of refactor:**
+- Every function that touches `g_cfg`, `g_tools_ctx`, `g_trigger_manager`, or any global state needs an `agent_instance_t*` parameter
+- [`nostr_handler.c`](src/nostr_handler.c) — the largest file (~4500 lines) — uses `g_cfg->keys` in ~40 places. Every signing, encryption, and subscription call needs to know which agent's keys to use.
+- [`llm.c`](src/llm.c) — the global `g_cfg` LLM config needs to become per-instance
+- [`agent.c`](src/agent.c) — all context building, message handling, tool loops need per-instance state
+- DM routing — incoming DMs need to be dispatched to the correct agent based on the `#p` tag
+- The HTTP API needs agent-scoped endpoints (`/api/agents/:id/prompt`, etc.)
+
+**Estimated scope:** Touch every `.c` file. ~2000-3000 lines of refactoring. High risk of regressions.
+
+**Pros:** Shared relay pool (fewer connections). Shared HTTP server. Single process to manage.
+**Cons:** Massive refactor. High regression risk. Shared-process failure mode (one agent's crash kills all).
+
+#### Path 3: Multi-Agent via Modes (Minimal Code — Recommended First Step)
+
+Modes give you 80% of the multi-agent benefit with 5% of the effort:
+
+- **Different personality per mode** — mode skill content
+- **Different model per mode** — mode's `llm` tag
+- **Different tools per mode** — mode's `requires_tool` tags
+- **Different temperature per mode** — mode's `temperature` tag
+- **Instant switching** — `/mode architect`
+- **Shared memory** — same agent, same memory store
+- **Shared identity** — same npub, same social graph
+
+What you lose vs. true multi-agent:
+- No concurrent operation (one mode at a time)
+- No independent persistent context per mode
+- No fault isolation between modes
+
+**This is the Roo Code model** — and it works extremely well for the single-admin use case.
+
+### Recommendation
+
+```mermaid
+graph TD
+    A[Start here] --> B[Implement modes - skill packages]
+    B --> C{Need concurrent agents?}
+    C -->|No| D[Modes are sufficient]
+    C -->|Yes| E{How many?}
+    E -->|2-3| F[Multiple processes - Path 1]
+    E -->|Many| G{Shared resources matter?}
+    G -->|No| F
+    G -->|Yes| H[Multi-agent refactor - Path 2]
+
+    style B fill:#e8f5e9
+    style D fill:#e8f5e9
+    style F fill:#fff3e0
+    style H fill:#ffebee
+```
+
+1. **Now:** Implement modes (skill packages). This covers the "Architect vs Coder" use case with minimal code.
+2. **When needed:** Run multiple Didactyl processes for true concurrent agents. Already works, just operational overhead.
+3. **If justified:** Multi-agent single-process refactor. Only if you're running 5+ agents on one server and the resource overhead matters.
+
+---
+
+## Implementation Layers
+
+The features build on each other in this order:
+
+### Layer 1: `skill_run` (Sub-Invocation)
+Already designed in [`tool_orchestration.md`](plans/tool_orchestration.md). This is the foundation — an isolated LLM execution of any skill. Enables one-shot skill usage, external skill testing, and sandboxing.
+
+**Enables:** `/skill_run cheap "how many files are in the directory"`
+
+### Layer 2: Slash Commands
+Also in [`tool_orchestration.md`](plans/tool_orchestration.md). Direct tool execution via `/tool_name args`. Required for `/mode` and `/skill_run` as admin commands.
+
+**Enables:** `/skill_run`, `/mode`, `/help`
+
+### Layer 3: Modes
+Mode skills with `["mode", "name"]` tags. `/mode` slash command to switch. Runtime tracks active mode and adjusts context composition.
+
+**Enables:** `/mode architect`, `/mode cheap`, persistent mode switching
+
+### Layer 4: Hardened Skills
+Deterministic step executor for skills that don't need LLM reasoning. A mode could include hardened skills as tools.
+
+**Enables:** Skills that execute as pure tool sequences — zero LLM cost
+
+---
+
+## Recommendation
+
+Implement in this order:
+
+1. **`skill_run`** (sub-invocation) — the foundation. Already designed. Enables one-shot skill execution with isolated context, per-skill model, and sandboxing.
+
+2. **Slash commands** (`/skill_run`, `/help`) — admin direct invocation. Required for the mode UX.
+
+3. **Modes** (`/mode name`) — skill packages that reconfigure the agent's context. Builds on `skill_run` for one-shot mode usage and slash commands for persistent switching.
+
+4. **Hardened skill execution** — deterministic tool sequences. The ultimate "skill as tool" pattern with zero LLM cost.

plans/skills_edit_page.md

@@ -0,0 +1,307 @@
+# Skills Editor Page — `skills-edit.html`
+
+## Overview
+
+A standalone web page in `~/lt/client-ndk/www/` for editing Didactyl agent skills. Built on top of [`template.html`](../../client-ndk/www/template.html) — all existing template functionality (auth, sidenav, hamburger, relay status, footer, theme toggle, logout) remains intact.
+
+The logged-in user IS the agent. The page uses Nostr subscriptions via the NDK worker to fetch skills and the adoption list, and publishes events via `publishEvent()` to save/adopt/remove skills.
+
+---
+
+## Data Model
+
+### Nostr Event Kinds
+
+| Kind | Purpose | Replaceable? |
+|------|---------|-------------|
+| `31123` | Public skill definition | Yes, by d-tag |
+| `31124` | Private skill definition (NIP-44 encrypted content) | Yes, by d-tag |
+| `10123` | Skill adoption list | Yes, single per pubkey |
+
+### Skill Event Structure
+
+```
+content: markdown template with {{variables}} (plain authored instructions; runtime handles role packaging)
+tags:
+  [d, slug]
+  [description, human-readable description]
+  [trigger, dm|cron|nostr-subscription|webhook|chain]  (optional)
+  [filter, type-specific filter JSON]                    (optional)
+  [llm, fallback chain string]                           (optional)
+  [temperature, number]                                  (optional)
+  [max_tokens, number]                                   (optional)
+  [seed, number]                                         (optional)
+  [requires_tool, tool_name]                             (0..N)
+  [requires_skill, skill_d_tag]                          (0..N)
+  [optional_tool, tool_name]                             (0..N)
+```
+
+### Adoption List (kind 10123)
+
+```json
+{
+  "kind": 10123,
+  "tags": [
+    ["a", "31123:<pubkey>:skill-slug"],
+    ["a", "31124:<pubkey>:private-skill-slug"]
+  ]
+}
+```
+
+---
+
+## Page Layout
+
+Three-column layout filling the body area between header and footer:
+
+```
+┌─────────────────────────────────────────────────────────────────────┐
+│ HEADER  [hamburger]              Skills Editor              [avatar]│
+├──────────────┬──────────────────────────────────┬───────────────────┤
+│              │                                  │                   │
+│  SKILLS      │  SKILL EDITOR                    │  TOOLS            │
+│  LIST        │                                  │                   │
+│              │  [slug] [description]             │  nostr_post       │
+│  ┌────────┐  │  [trigger] [filter]              │  nostr_query      │
+│  │ search │  │  [llm] [temp] [tokens]           │  nostr_dm_send    │
+│  └────────┘  │                                  │  local_shell_exec │
+│              │  ┌──────────────────────────────┐ │  memory_save      │
+│  My|Pub|Adpt │  │                              │ │  memory_recall    │
+│              │  │  Large textarea with          │ │  skill_create     │
+│  skill-1  ★  │  │  syntax highlighting for     │ │  ...              │
+│  skill-2     │  │  {{variables}} and tool names │ │                   │
+│  skill-3  ★  │  │                              │ │  Double-click to  │
+│  skill-4     │  │                              │ │  add as           │
+│  ...         │  │                              │ │  requires_tool    │
+│              │  └──────────────────────────────┘ │                   │
+│  ──────────  │                                  │                   │
+│  ADOPTED     │  [Save] [Save Private] [New]     │                   │
+│  identity ↕  │  [Adopt] [Remove from adopted]   │                   │
+│  chat     ↕  │                                  │                   │
+│  monitor  ↕  │                                  │                   │
+│              │                                  │                   │
+├──────────────┴──────────────────────────────────┴───────────────────┤
+│ FOOTER  [relay status]          [status]              [info]        │
+└─────────────────────────────────────────────────────────────────────┘
+```
+
+### Left Column — Skills List (~20% width)
+
+- **Search input** at top to filter skills by slug/description
+- **Filter tabs**: My | Public | Adopted
+  - **My**: skills authored by the current pubkey (kind 31123 + 31124)
+  - **Public**: public skills from anyone (kind 31123 only, no author filter) — for discovery and copying
+  - **Adopted**: skills currently in the kind 10123 adoption list
+- **Scrollable skill list**: each item shows slug, description snippet, author name/short-pubkey, ★ if adopted
+- Clicking a skill loads it into the editor
+- **Copy/Fork button**: when viewing a public skill from another author, a "Copy to My Skills" button creates a new skill under the current pubkey with the same content (new d-tag to avoid collision)
+- **Adopted Skills section** below the list:
+  - Shows current adoption list entries in order
+  - Drag-to-reorder support (or up/down buttons)
+  - Visual indicator of adoption order
+
+### Center Column — Skill Editor (~60% width)
+
+- **Metadata row**: slug (d-tag), description inputs
+- **Trigger row**: trigger type dropdown, filter input
+- **Execution params row**: llm input, temperature, max_tokens, seed
+- **Requirements display**: shows current requires_tool, requires_skill, optional_tool tags as removable chips/badges
+- **Large textarea** for skill content (markdown template)
+  - Syntax highlighting overlay for `{{variable_names}}` in accent color
+  - Tool name references highlighted when they match known tools
+- **Action buttons**:
+  - Save (publish as kind 31123)
+  - Save Private (publish as kind 31124 with NIP-44 encryption)
+  - New Skill (clear editor)
+  - Adopt (add to kind 10123 list)
+  - Remove from Adopted (remove from kind 10123 list)
+  - Delete Skill (kind 5 deletion event)
+
+### Right Column — Tools List (~20% width)
+
+- **Hardcoded list** of all Didactyl tools from TOOLS.md
+- Each tool shows name and brief description
+- **Double-click** a tool to add it as a `requires_tool` tag on the current skill
+- Visual indicator if tool is already required by current skill
+- Search/filter input at top
+
+---
+
+## Syntax Highlighting Approach
+
+Use a transparent overlay `<div>` positioned exactly over the `<textarea>`. The textarea has transparent text color; the overlay div renders the same text with highlighted spans. Both scroll together.
+
+Highlighted elements:
+- `{{variable_name}}` — accent color (e.g., `var(--accent-color)`)
+- Tool names (when matching known tools) — distinct highlight color
+
+---
+
+## Nostr Subscriptions
+
+### On page load (after auth):
+
+1. **My skills subscription**: `{ kinds: [31123, 31124], authors: [currentPubkey], limit: 200 }` — fetch agent's own skills (public + private)
+2. **Public skills subscription** (when Public filter active): `{ kinds: [31123], limit: 200 }` — fetch public skills from anyone for discovery/copying
+3. **Adoption list subscription**: `{ kinds: [10123], authors: [currentPubkey], limit: 1 }` — fetch current adoption list
+
+The Public subscription is activated on demand when the user switches to the Public tab, to avoid fetching large amounts of data unnecessarily. My skills and adoption list are always loaded.
+
+### Event handling:
+
+- `ndkEvent` listener processes incoming skill events and adoption list events
+- Skills are parsed and stored in a local array, keyed by `pubkey:d-tag`
+- Adoption list tags are parsed to determine which skills are adopted and their order
+
+---
+
+## Skill Publishing
+
+### Save Public Skill (kind 31123)
+
+```javascript
+const event = {
+  kind: 31123,
+  content: editorContent,  // raw markdown template
+  tags: [
+    [d, slug],
+    [description, descriptionText],
+    // trigger tags if set
+    // execution param tags
+    // requires_tool tags
+    // requires_skill tags
+    // optional_tool tags
+  ],
+  created_at: Math.floor(Date.now() / 1000)
+};
+await publishEvent(event);
+```
+
+### Save Private Skill (kind 31124)
+
+Same structure but content is NIP-44 encrypted. The `d` tag stays in plaintext; other metadata moves into the encrypted payload per SKILLS.md spec.
+
+### Update Adoption List (kind 10123)
+
+```javascript
+const event = {
+  kind: 10123,
+  content: '',
+  tags: adoptedSkillAddresses.map(addr => [a, addr]),
+  created_at: Math.floor(Date.now() / 1000)
+};
+await publishEvent(event);
+```
+
+---
+
+## Hardcoded Tools List
+
+Extracted from TOOLS.md — all tool names with descriptions:
+
+```javascript
+const DIDACTYL_TOOLS = [
+  { name: 'nostr_post', description: 'Publish a Nostr event to connected relays' },
+  { name: 'nostr_delete', description: 'Request deletion of events - NIP-09 kind 5' },
+  { name: 'nostr_react', description: 'React to a Nostr event - NIP-25 kind 7' },
+  { name: 'nostr_query', description: 'Query events from relays using a Nostr filter' },
+  { name: 'nostr_my_events', description: 'Query recent events authored by this agent' },
+  { name: 'nostr_dm_send', description: 'Send a NIP-04 encrypted DM' },
+  { name: 'nostr_dm_send_nip17', description: 'Send a private DM using NIP-17 gift wrap' },
+  { name: 'nostr_profile_get', description: 'Look up a Nostr profile by pubkey' },
+  { name: 'nostr_nip05_lookup', description: 'Look up or verify a NIP-05 identifier' },
+  { name: 'nostr_encode', description: 'Encode a Nostr entity into nostr: URI' },
+  { name: 'nostr_decode', description: 'Decode a Nostr bech32/nostr: URI' },
+  { name: 'nostr_relay_status', description: 'Get relay connection status and statistics' },
+  { name: 'nostr_relay_info', description: 'Fetch NIP-11 relay information document' },
+  { name: 'nostr_subscription_status', description: 'List managed runtime Nostr subscriptions' },
+  { name: 'nostr_subscription_set', description: 'Update a managed runtime subscription' },
+  { name: 'nostr_encrypt', description: 'Encrypt plaintext using NIP-44' },
+  { name: 'nostr_decrypt', description: 'Decrypt NIP-44 ciphertext' },
+  { name: 'nostr_list_manage', description: 'Add/remove tags in replaceable list events' },
+  { name: 'nostr_block_list', description: 'View blocked pubkeys/events/hashtags' },
+  { name: 'nostr_block_edit', description: 'Add or remove blocked tuples in kind-10000' },
+  { name: 'nostr_pubkey', description: 'Return agent pubkey in hex' },
+  { name: 'nostr_npub', description: 'Return agent pubkey as npub bech32' },
+  { name: 'agent_identity', description: 'Build agent identity context block' },
+  { name: 'admin_identity', description: 'Build admin identity context block' },
+  { name: 'nostr_admin_profile', description: 'Build admin profile context block' },
+  { name: 'nostr_admin_contacts', description: 'Build admin contacts context block' },
+  { name: 'nostr_admin_relays', description: 'Build admin relay context block' },
+  { name: 'nostr_admin_notes', description: 'Build admin notes context block' },
+  { name: 'nostr_agent_profile', description: 'Build agent profile context block' },
+  { name: 'nostr_agent_contacts', description: 'Build agent contacts context block' },
+  { name: 'nostr_agent_relays', description: 'Build agent relay context block' },
+  { name: 'nostr_agent_notes', description: 'Build agent notes context block' },
+  { name: 'skill_create', description: 'Create or update a skill definition' },
+  { name: 'skill_edit', description: 'Edit an existing self skill by d tag' },
+  { name: 'skill_list', description: 'List available skills with adoption status' },
+  { name: 'skill_adopt', description: 'Adopt a skill into kind 10123 list' },
+  { name: 'skill_remove', description: 'Remove a skill from kind 10123 list' },
+  { name: 'skill_search', description: 'Search public skills by query/author' },
+  { name: 'trigger_list', description: 'List active triggered skills and status' },
+  { name: 'task_list', description: 'Build current task list context block' },
+  { name: 'task_manage', description: 'Manage agent task memory on Nostr' },
+  { name: 'memory_save', description: 'Prepend entry to encrypted agent memory' },
+  { name: 'memory_recall', description: 'Recall encrypted agent memory' },
+  { name: 'model_get', description: 'Get current LLM runtime configuration' },
+  { name: 'model_set', description: 'Update active LLM configuration' },
+  { name: 'model_list', description: 'List available model IDs' },
+  { name: 'config_store', description: 'Encrypt and publish agent config' },
+  { name: 'config_recall', description: 'Fetch and decrypt agent config' },
+  { name: 'agent_version', description: 'Return current Didactyl version' },
+  { name: 'local_http_fetch', description: 'Fetch HTTP/S resources' },
+  { name: 'local_shell_exec', description: 'Execute a shell command' },
+  { name: 'local_file_read', description: 'Read a local file as text' },
+  { name: 'local_file_write', description: 'Write text content to a local file' },
+  { name: 'tool_list', description: 'List available tools with schemas' },
+  { name: 'cashu_wallet_balance', description: 'Return wallet balances by mint' },
+  { name: 'cashu_wallet_info', description: 'Fetch mint info' },
+  { name: 'cashu_wallet_mint_quote', description: 'Request a mint quote' },
+  { name: 'cashu_wallet_mint_check', description: 'Check mint quote status' },
+  { name: 'cashu_wallet_mint_claim', description: 'Claim minted proofs' },
+  { name: 'cashu_wallet_melt_quote', description: 'Request a melt quote' },
+  { name: 'cashu_wallet_melt_pay', description: 'Pay a melt quote' },
+  { name: 'cashu_wallet_check_proofs', description: 'Check proof states' },
+  { name: 'cashu_wallet_receive_token', description: 'Receive an ecash token' },
+  { name: 'cashu_wallet_send_token', description: 'Create outbound ecash token' },
+  { name: 'cashu_wallet_mints_set', description: 'Set wallet/public mints' },
+  { name: 'blossom_upload', description: 'Upload a file to Blossom server' },
+  { name: 'blossom_download', description: 'Download a Blossom blob' },
+  { name: 'blossom_head', description: 'Fetch Blossom blob metadata' },
+  { name: 'blossom_delete', description: 'Delete a Blossom blob' },
+  { name: 'blossom_list', description: 'List Blossom blobs for a pubkey' },
+  { name: 'nostr_post_readme', description: 'Publish README.md as kind 30023' },
+  { name: 'nostr_file_md_to_longform_post', description: 'Publish markdown file as kind 30023' },
+];
+```
+
+---
+
+## Implementation Sequence
+
+```mermaid
+flowchart TD
+    A[Copy template.html to skills-edit.html] --> B[Add page-specific CSS for 3-column layout]
+    B --> C[Build HTML structure: left/center/right columns]
+    C --> D[Implement Nostr subscriptions for skills + adoption list]
+    D --> E[Build skills list rendering with search and filter]
+    E --> F[Build skill editor form with metadata fields]
+    F --> G[Build large textarea with syntax highlighting overlay]
+    G --> H[Build tools list column with double-click to add]
+    H --> I[Implement skill save/publish for kind 31123]
+    I --> J[Implement private skill save for kind 31124]
+    J --> K[Implement adoption list management - adopt/remove/reorder]
+    K --> L[Implement skill deletion via kind 5]
+    L --> M[Wire up all event listeners and UI interactions]
+    M --> N[Test and refine]
+```
+
+---
+
+## Files Modified
+
+| File | Change |
+|------|--------|
+| `~/lt/client-ndk/www/skills-edit.html` | **NEW** — the entire skill editor page |
+| `~/lt/client-ndk/www/index.html` | Add navigation entry for skills-edit page (optional) |

restart_anvil_local.sh

@@ -0,0 +1,42 @@
+#!/bin/bash
+
+set -euo pipefail
+
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+SERVICE_NAME="anvil.service"
+SERVICE_SRC="$SCRIPT_DIR/anvil.service"
+SERVICE_DST="/etc/systemd/system/$SERVICE_NAME"
+
+if ! command -v systemctl >/dev/null 2>&1; then
+    echo "ERROR: systemctl is not available on this system"
+    exit 1
+fi
+
+if [ "$EUID" -ne 0 ] && ! command -v sudo >/dev/null 2>&1; then
+    echo "ERROR: sudo is required when not running as root"
+    exit 1
+fi
+
+echo "=========================================="
+echo "  RESTART ANVIL LOCAL"
+echo "=========================================="
+
+echo "Building debug static binary..."
+"$SCRIPT_DIR/build_static.sh" --debug
+
+echo "Syncing service file to $SERVICE_DST..."
+if [ "$EUID" -eq 0 ]; then
+    install -m 0644 "$SERVICE_SRC" "$SERVICE_DST"
+    systemctl daemon-reload
+    systemctl restart "$SERVICE_NAME"
+    systemctl --no-pager --full status "$SERVICE_NAME" || true
+else
+    sudo install -m 0644 "$SERVICE_SRC" "$SERVICE_DST"
+    sudo systemctl daemon-reload
+    sudo systemctl restart "$SERVICE_NAME"
+    sudo systemctl --no-pager --full status "$SERVICE_NAME" || true
+fi
+
+echo
+echo "Anvil restarted."
+echo "Context logs: /home/user/anvil/context.logs"

src/agent.c

@@ -10,6 +10,8 @@
 #include <pthread.h>
 #include <stdarg.h>
 #include <ctype.h>
+#include <errno.h>
+#include <sys/stat.h>
 
 #include "llm.h"
 #include "nostr_handler.h"
@@ -19,6 +21,8 @@
 #include "cjson/cJSON.h"
 #include "debug.h"
 #include "../../nostr_core_lib/nostr_core/nostr_core.h"
+#include "context_format.h"
+#include "context_roles.h"
 
 static didactyl_config_t* g_cfg = NULL;
 static tools_context_t g_tools_ctx;
@@ -248,7 +252,37 @@ static int append_tool_result_message(cJSON* messages, const char* tool_call_id,
     return 0;
 }
 
-static void append_context_log(const char* sender_pubkey_hex, const char* phase, const char* context_payload);
+static int maybe_append_loaded_skill_instructions(cJSON* messages,
+                                                  const char* tool_name,
+                                                  const char* tool_result_json) {
+    if (!messages || !tool_name || strcmp(tool_name, "skill_load") != 0 || !tool_result_json) {
+        return 0;
+    }
+
+    cJSON* root = cJSON_Parse(tool_result_json);
+    if (!root || !cJSON_IsObject(root)) {
+        cJSON_Delete(root);
+        return 0;
+    }
+
+    cJSON* ok = cJSON_GetObjectItemCaseSensitive(root, "success");
+    cJSON* type = cJSON_GetObjectItemCaseSensitive(root, "type");
+    cJSON* instructions = cJSON_GetObjectItemCaseSensitive(root, "instructions");
+
+    int should_apply = (ok && cJSON_IsBool(ok) && cJSON_IsTrue(ok) &&
+                        type && cJSON_IsString(type) && type->valuestring &&
+                        strcmp(type->valuestring, "skill_instructions") == 0 &&
+                        instructions && cJSON_IsString(instructions) && instructions->valuestring &&
+                        instructions->valuestring[0] != '\0');
+
+    int rc = 0;
+    if (should_apply) {
+        rc = append_simple_message(messages, "system", instructions->valuestring);
+    }
+
+    cJSON_Delete(root);
+    return rc;
+}
 
 static char* local_json_error(const char* msg) {
     cJSON* out = cJSON_CreateObject();
@@ -290,6 +324,51 @@ static char* build_slash_args_json(const char* raw_args) {
     return out;
 }
 
+static char* build_run_args_json_local(const char* raw_args) {
+    const char* s = skip_spaces_local(raw_args);
+    if (!s || s[0] == '\0') {
+        return local_json_error("/run requires a skill identifier");
+    }
+
+    const char* ident_start = s;
+    while (*s && !is_space_char(*s)) s++;
+
+    size_t ident_len = (size_t)(s - ident_start);
+    if (ident_len == 0 || ident_len >= 256U) {
+        return local_json_error("/run skill identifier is invalid");
+    }
+
+    char ident[256];
+    memcpy(ident, ident_start, ident_len);
+    ident[ident_len] = '\0';
+
+    const char* remaining = skip_spaces_local(s);
+
+    cJSON* out = cJSON_CreateObject();
+    if (!out) return NULL;
+
+    const char* c1 = strchr(ident, ':');
+    const char* c2 = c1 ? strchr(c1 + 1, ':') : NULL;
+    if (c1 && c2 && c1 != ident && (size_t)(c2 - c1) == 65U && c2[1] != '\0') {
+        size_t pubkey_len = (size_t)(c2 - (c1 + 1));
+        char pubkey[65];
+        memcpy(pubkey, c1 + 1, pubkey_len);
+        pubkey[pubkey_len] = '\0';
+        cJSON_AddStringToObject(out, "pubkey", pubkey);
+        cJSON_AddStringToObject(out, "d_tag", c2 + 1);
+    } else {
+        cJSON_AddStringToObject(out, "d_tag", ident);
+    }
+
+    if (remaining && remaining[0] != '\0') {
+        cJSON_AddStringToObject(out, "args", remaining);
+    }
+
+    char* json = cJSON_PrintUnformatted(out);
+    cJSON_Delete(out);
+    return json;
+}
+
 static int append_textf_local(char** buf, size_t* cap, size_t* used, const char* fmt, ...) {
     if (!buf || !cap || !used || !fmt) return -1;
 
@@ -792,7 +871,8 @@ static int handle_slash_command(const char* sender_pubkey_hex, const char* messa
 
     char* result_json = NULL;
     if (raw_args && (strcmp(raw_args, "-h") == 0 || strcmp(raw_args, "--help") == 0)) {
-        result_json = build_slash_help_tool_json(tool_name);
+        const char* help_name = (strcmp(tool_name, "run") == 0) ? "skill_run" : tool_name;
+        result_json = build_slash_help_tool_json(help_name);
     } else if (strcmp(tool_name, "help") == 0) {
         if (!raw_args || raw_args[0] == '\0') {
             result_json = build_slash_help_all_json();
@@ -804,14 +884,24 @@ static int handle_slash_command(const char* sender_pubkey_hex, const char* messa
                 i++;
             }
             help_tool[i] = '\0';
-            result_json = build_slash_help_tool_json(help_tool);
+            const char* help_name = (strcmp(help_tool, "run") == 0) ? "skill_run" : help_tool;
+            result_json = build_slash_help_tool_json(help_name);
         }
     } else {
-        char* args_json = build_slash_args_json(raw_args);
+        char* args_json = NULL;
+        const char* exec_tool_name = tool_name;
+
+        if (strcmp(tool_name, "run") == 0) {
+            args_json = build_run_args_json_local(raw_args);
+            exec_tool_name = "skill_run";
+        } else {
+            args_json = build_slash_args_json(raw_args);
+        }
+
         if (!args_json) {
             result_json = local_json_error("failed to parse slash args");
         } else {
-            result_json = tools_execute(&g_tools_ctx, tool_name, args_json);
+            result_json = tools_execute(&g_tools_ctx, exec_tool_name, args_json);
             free(args_json);
         }
     }
@@ -833,7 +923,6 @@ static int handle_slash_command(const char* sender_pubkey_hex, const char* messa
                  message,
                  result_json ? result_json : "",
                  dm_payload ? dm_payload : "");
-        append_context_log(sender_pubkey_hex, "direct_tool_exec", log_payload);
         free(log_payload);
     }
 
@@ -948,128 +1037,6 @@ static __attribute__((unused)) void template_emit_hook(const char* section_name,
     set_context_part_name(message_index, section_name ? section_name : "context_part");
 }
 
-static void append_context_log(const char* sender_pubkey_hex, const char* phase, const char* context_payload) {
-    if (!phase) {
-        return;
-    }
-    if (strcmp(phase, "llm_chat_with_tools_messages") != 0 &&
-        strcmp(phase, "llm_trigger_with_tools_messages") != 0) {
-        return;
-    }
-
-    time_t now = time(NULL);
-    struct tm tm_info;
-    localtime_r(&now, &tm_info);
-    char timestamp[32] = {0};
-    strftime(timestamp, sizeof(timestamp), "%Y-%m-%d %H:%M:%S", &tm_info);
-
-    const char* safe_phase = phase ? phase : "unknown";
-    const char* safe_sender = sender_pubkey_hex ? sender_pubkey_hex : "unknown";
-    const char* safe_payload = context_payload ? context_payload : "";
-    size_t context_bytes = context_payload ? strlen(context_payload) : 0U;
-    size_t approx_tokens = context_bytes / 4U;
-
-    llm_config_t active_llm_cfg = {0};
-    const char* safe_model = (g_cfg && g_cfg->llm.model[0] != '\0') ? g_cfg->llm.model : "unknown";
-    if (llm_get_config(&active_llm_cfg) == 0 && active_llm_cfg.model[0] != '\0') {
-        safe_model = active_llm_cfg.model;
-    }
-
-    int entry_len = snprintf(NULL,
-                             0,
-                             "```text\n"
-                             "Context Log - seen by model\n"
-                             "timestamp=%s\n"
-                             "phase=%s\n"
-                             "sender=%s\n"
-                             "model=%s\n"
-                             "context_bytes=%zu\n"
-                             "approx_tokens=%zu\n"
-                             "```\n\n"
-                             "%s\n\n---\n\n",
-                             timestamp,
-                             safe_phase,
-                             safe_sender,
-                             safe_model,
-                             context_bytes,
-                             approx_tokens,
-                             safe_payload);
-    if (entry_len < 0) {
-        return;
-    }
-
-    char* entry = (char*)malloc((size_t)entry_len + 1U);
-    if (!entry) {
-        return;
-    }
-
-    snprintf(entry,
-             (size_t)entry_len + 1U,
-             "```text\n"
-             "Context Log - seen by model\n"
-             "timestamp=%s\n"
-             "phase=%s\n"
-             "sender=%s\n"
-             "model=%s\n"
-             "context_bytes=%zu\n"
-             "approx_tokens=%zu\n"
-             "```\n\n"
-             "%s\n\n---\n\n",
-             timestamp,
-             safe_phase,
-             safe_sender,
-             safe_model,
-             context_bytes,
-             approx_tokens,
-             safe_payload);
-
-    char* old_data = NULL;
-    size_t old_len = 0;
-
-    FILE* in = fopen("context.log.md", "rb");
-    if (in) {
-        if (fseek(in, 0, SEEK_END) == 0) {
-            long sz = ftell(in);
-            if (sz > 0 && fseek(in, 0, SEEK_SET) == 0) {
-                old_len = (size_t)sz;
-                old_data = (char*)malloc(old_len);
-                if (old_data) {
-                    size_t n = fread(old_data, 1, old_len, in);
-                    if (n != old_len) {
-                        free(old_data);
-                        old_data = NULL;
-                        old_len = 0;
-                    }
-                } else {
-                    old_len = 0;
-                }
-            }
-        }
-        fclose(in);
-    }
-
-    FILE* out = fopen("context.log.md", "wb");
-    if (!out) {
-        free(old_data);
-        free(entry);
-        return;
-    }
-
-    (void)fwrite(entry, 1, (size_t)entry_len, out);
-    if (old_data && old_len > 0) {
-        (void)fwrite(old_data, 1, old_len, out);
-    }
-
-    fclose(out);
-    free(old_data);
-    free(entry);
-}
-
-void agent_append_context_log(const char* sender_pubkey_hex, const char* phase, const char* context_payload) {
-    append_context_log(sender_pubkey_hex, phase, context_payload);
-}
-
-
 static __attribute__((unused)) char* build_sender_verification_text(didactyl_sender_tier_t sender_tier) {
     if (sender_tier == DIDACTYL_SENDER_ADMIN) {
         return strdup("This message has been cryptographically verified as coming from your administrator.");
@@ -1273,6 +1240,28 @@ static char* resolve_skill_references_local(const char* input) {
     return prompt_template_resolve_inline_variables(input ? input : "", &g_tools_ctx);
 }
 
+static char* strip_system_role_prefix_local(const char* input) {
+    const char* s = input ? input : "";
+
+    if (strncmp(s, "system:", 7) != 0) {
+        return strdup(s);
+    }
+
+    const char* p = s + 7;
+    while (*p == ' ' || *p == '\t') {
+        p++;
+    }
+
+    if (*p == '\r') {
+        p++;
+    }
+    if (*p == '\n') {
+        p++;
+    }
+
+    return strdup(p);
+}
+
 static char* build_context_from_triggers(trigger_type_t trigger_type,
                                          const char* trigger_filter,
                                          cJSON* trigger_event,
@@ -1286,14 +1275,34 @@ static char* build_context_from_triggers(trigger_type_t trigger_type,
 
     (void)refresh_adopted_skills_cache_if_needed();
 
+    char* title = context_build_title(&g_tools_ctx);
     size_t cap = 4096;
     size_t used = 0;
     char* out = (char*)malloc(cap);
     if (!out) {
+        free(title);
         return NULL;
     }
     out[0] = '\0';
 
+    if (title) {
+        size_t title_len = strlen(title);
+        if (title_len + 1U > cap) {
+            cap = title_len + 1024;
+            char* grown = (char*)realloc(out, cap);
+            if (!grown) {
+                free(title);
+                free(out);
+                return NULL;
+            }
+            out = grown;
+        }
+        memcpy(out, title, title_len);
+        used = title_len;
+        out[used] = '\0';
+        free(title);
+    }
+
     int matched = 0;
 
     didactyl_sender_tier_t tier = DIDACTYL_SENDER_STRANGER;
@@ -1328,13 +1337,20 @@ static char* build_context_from_triggers(trigger_type_t trigger_type,
     for (int i = 0; i < matched_skill_count; i++) {
         char* expanded = resolve_skill_references_local(matched_skill_contents[i] ? matched_skill_contents[i] : "");
         const char* skill_text = expanded ? expanded : (matched_skill_contents[i] ? matched_skill_contents[i] : "");
+        char* normalized = strip_system_role_prefix_local(skill_text);
+        const char* normalized_text = normalized ? normalized : skill_text;
 
-        size_t need = strlen("\n\n---\n\n") + strlen(skill_text) + 1U;
+        char* bumped = context_bump_headings(normalized_text);
+        const char* final_text = bumped ? bumped : normalized_text;
+
+        size_t need = strlen("\n\n---\n\n") + strlen(final_text) + 1U;
         if (used + need >= cap) {
             size_t next = cap;
             while (used + need >= next) next *= 2U;
             char* grown = (char*)realloc(out, next);
             if (!grown) {
+                free(bumped);
+                free(normalized);
                 free(expanded);
                 for (int j = i; j < matched_skill_count; j++) {
                     free(matched_skill_contents[j]);
@@ -1350,19 +1366,24 @@ static char* build_context_from_triggers(trigger_type_t trigger_type,
             memcpy(out + used, "\n\n---\n\n", 7);
             used += 7;
         }
-        size_t sl = strlen(skill_text);
-        memcpy(out + used, skill_text, sl);
+        size_t sl = strlen(final_text);
+        memcpy(out + used, final_text, sl);
         used += sl;
         out[used] = '\0';
         matched++;
 
+        free(bumped);
+        free(normalized);
         free(expanded);
         free(matched_skill_contents[i]);
     }
 
     if (matched == 0) {
-        const char* fallback = "You are an AI agent. Respond to the message.";
-        size_t need = strlen(fallback) + 1U;
+        const char* fallback = "## Default Instructions\n\nYou are an AI agent. Respond to the message.";
+        const char* sep = (used > 0) ? "\n\n---\n\n" : "";
+        size_t sep_len = strlen(sep);
+        size_t fb_len = strlen(fallback);
+        size_t need = used + sep_len + fb_len + 1U;
         if (need > cap) {
             char* grown = (char*)realloc(out, need);
             if (!grown) {
@@ -1372,8 +1393,13 @@ static char* build_context_from_triggers(trigger_type_t trigger_type,
             out = grown;
             cap = need;
         }
-        memcpy(out, fallback, need);
-        used = need - 1U;
+        if (sep_len > 0) {
+            memcpy(out + used, sep, sep_len);
+            used += sep_len;
+        }
+        memcpy(out + used, fallback, fb_len);
+        used += fb_len;
+        out[used] = '\0';
     }
 
     return out;
@@ -1393,6 +1419,12 @@ static void clear_adopted_skills_cache_locked(void) {
     g_adopted_skills_count = 0;
 }
 
+void agent_invalidate_adopted_skills_cache(void) {
+    pthread_mutex_lock(&g_adopted_skills_mutex);
+    g_adopted_skills_last_refresh_at = 0;
+    pthread_mutex_unlock(&g_adopted_skills_mutex);
+}
+
 static int refresh_adopted_skills_cache_if_needed(void) {
     if (!g_cfg) {
         return -1;
@@ -1809,40 +1841,38 @@ void agent_on_trigger(const char* skill_d_tag,
         composed_context = strdup("You are an AI agent. Respond to the trigger event.");
     }
 
-    size_t system_len = strlen(composed_context ? composed_context : "") + 2 + strlen(trigger_prefix) + strlen(skill_d_tag) +
+    size_t full_len = strlen(composed_context ? composed_context : "") + 2 + strlen(trigger_prefix) + strlen(skill_d_tag) +
                         strlen("\nRelay: ") + strlen(relay) + strlen("\n\nSkill instructions:\n") +
-                        strlen(skill_content) + 1U;
-    char* system_prompt = (char*)malloc(system_len);
-    if (!system_prompt) {
+                        strlen(skill_content) + strlen("\n\nuser:\nTriggering event JSON:\n") + strlen(event_json) + 1U;
+    char* full_markdown = (char*)malloc(full_len);
+    if (!full_markdown) {
         free(composed_context);
         free(event_json);
         return;
     }
 
-    snprintf(system_prompt,
-             system_len,
-             "%s\n\n%s%s\nRelay: %s\n\nSkill instructions:\n%s",
+    snprintf(full_markdown,
+             full_len,
+             "%s\n\n%s%s\nRelay: %s\n\nSkill instructions:\n%s\n\nuser:\nTriggering event JSON:\n%s",
              composed_context ? composed_context : "",
              trigger_prefix,
              skill_d_tag,
              relay,
-             skill_content);
+             skill_content,
+             event_json);
     free(composed_context);
 
-    size_t user_len = strlen("Triggering event JSON:\n") + strlen(event_json) + 1U;
-    char* user_prompt = (char*)malloc(user_len);
-    if (!user_prompt) {
-        free(system_prompt);
+    context_roles_t roles;
+    if (context_roles_split(full_markdown, &roles) != 0) {
+        free(full_markdown);
         free(event_json);
         return;
     }
 
-    snprintf(user_prompt, user_len, "Triggering event JSON:\n%s", event_json);
-
     char* tools_json = tools_build_openai_schema_json(&g_tools_ctx);
     if (!tools_json) {
-        free(system_prompt);
-        free(user_prompt);
+        context_roles_free(&roles);
+        free(full_markdown);
         g_tools_ctx.template_trigger_event_json = prev_trigger_event_json;
         free(event_json);
         (void)nostr_handler_send_dm_auto(g_cfg->admin.pubkey, "Triggered skill execution failed: tools unavailable.");
@@ -1851,21 +1881,20 @@ void agent_on_trigger(const char* skill_d_tag,
 
     cJSON* messages = cJSON_CreateArray();
     if (!messages || !cJSON_IsArray(messages) ||
-        append_simple_message(messages, "system", system_prompt) != 0 ||
-        append_simple_message(messages, "user", user_prompt) != 0) {
+        append_simple_message(messages, "system", roles.system_content) != 0 ||
+        append_simple_message(messages, "user", roles.user_content) != 0) {
         cJSON_Delete(messages);
         free(tools_json);
-        free(system_prompt);
-        free(user_prompt);
+        context_roles_free(&roles);
+        free(full_markdown);
         g_tools_ctx.template_trigger_event_json = prev_trigger_event_json;
         free(event_json);
         (void)nostr_handler_send_dm_auto(g_cfg->admin.pubkey, "Triggered skill execution failed: prompt initialization failed.");
         return;
     }
 
-    append_context_log(g_cfg->admin.pubkey, "llm_trigger", user_prompt);
-    free(system_prompt);
-    free(user_prompt);
+    context_roles_free(&roles);
+    free(full_markdown);
 
     int max_turns = g_cfg->tools.trigger_max_turns > 0
                         ? g_cfg->tools.trigger_max_turns
@@ -1880,8 +1909,6 @@ void agent_on_trigger(const char* skill_d_tag,
             break;
         }
 
-        append_context_log(g_cfg->admin.pubkey, "llm_trigger_with_tools_messages", messages_json);
-
         llm_response_t resp;
         int rc = llm_chat_with_tools_messages(messages_json, tools_json, "auto", &resp);
         free(messages_json);
@@ -1925,6 +1952,17 @@ void agent_on_trigger(const char* skill_d_tag,
                 return;
             }
 
+            if (maybe_append_loaded_skill_instructions(messages, tc->name, tool_result) != 0) {
+                free(tool_result);
+                llm_response_free(&resp);
+                cJSON_Delete(messages);
+                free(tools_json);
+                g_tools_ctx.template_trigger_event_json = prev_trigger_event_json;
+                free(event_json);
+                (void)nostr_handler_send_dm_auto(g_cfg->admin.pubkey, "Triggered skill execution failed: loaded skill instruction append failed.");
+                return;
+            }
+
             free(tool_result);
         }
 
@@ -1981,7 +2019,10 @@ int agent_build_admin_messages_json(const char* current_user_message,
                             (sender_tier == DIDACTYL_SENDER_WOT ? "wot" : "stranger"));
     cJSON_AddNumberToObject(dm_event, "created_at", (double)time(NULL));
 
+    const char* prev_current_user_message = g_tools_ctx.template_current_user_message;
+    g_tools_ctx.template_current_user_message = current_user_message ? current_user_message : "";
     char* composed_context = build_context_from_triggers(TRIGGER_TYPE_DM, NULL, dm_event, "api");
+    g_tools_ctx.template_current_user_message = prev_current_user_message;
     cJSON_Delete(dm_event);
     if (!composed_context) {
         composed_context = strdup("You are an AI agent. Respond to the message.");
@@ -2049,7 +2090,6 @@ void agent_on_message(const char* sender_pubkey_hex,
     if (message[0] == '/') {
         if (!allow_tools) {
             const char* denied = "{\"success\":false,\"error\":\"slash commands are disabled for this sender tier\"}";
-            append_context_log(sender_pubkey_hex, "direct_tool_exec", denied);
             (void)nostr_handler_send_dm_auto_with_role(sender_pubkey_hex,
                                                        denied,
                                                        DIDACTYL_DM_HISTORY_TOOL_RESPONSE);
@@ -2072,36 +2112,36 @@ void agent_on_message(const char* sender_pubkey_hex,
                             (tier == DIDACTYL_SENDER_WOT ? "wot" : "stranger"));
     cJSON_AddNumberToObject(dm_event, "created_at", (double)time(NULL));
 
+    const char* prev_current_user_message = g_tools_ctx.template_current_user_message;
+    g_tools_ctx.template_current_user_message = message ? message : "";
     char* dm_context = build_context_from_triggers(TRIGGER_TYPE_DM, NULL, dm_event, "dm");
+    g_tools_ctx.template_current_user_message = prev_current_user_message;
     cJSON_Delete(dm_event);
     if (!dm_context) {
         dm_context = strdup("You are an AI agent. Respond to the message.");
     }
+    if (!dm_context) {
+        return;
+    }
 
     if (!allow_tools) {
         const char* tier_prefix = (tier == DIDACTYL_SENDER_WOT)
                                     ? "You are responding to a web-of-trust contact. Keep the response helpful and concise. Tool use is disabled for this tier."
                                     : "You are responding in chat-only mode. Tool use is disabled.";
 
-        size_t ctx_len = strlen(dm_context ? dm_context : "") + strlen("\n\n") + strlen(tier_prefix) + 1U;
+        size_t ctx_len = strlen(dm_context) + strlen("\n\n") + strlen(tier_prefix) + 1U;
         char* system_for_chat = (char*)malloc(ctx_len);
         if (!system_for_chat) {
             free(dm_context);
             return;
         }
 
-        snprintf(system_for_chat, ctx_len, "%s\n\n%s", dm_context ? dm_context : "", tier_prefix);
-
-        size_t context_len = strlen("system:\n\nuser:\n") + strlen(system_for_chat) + strlen(message) + 1U;
-        char* plain_context = (char*)malloc(context_len);
-        if (plain_context) {
-            snprintf(plain_context, context_len, "system:\n%s\n\nuser:\n%s", system_for_chat, message);
-            append_context_log(sender_pubkey_hex, "llm_chat", plain_context);
-            free(plain_context);
-        }
+        snprintf(system_for_chat, ctx_len, "%s\n\n%s", dm_context, tier_prefix);
 
         char* response = llm_chat(system_for_chat, message);
         free(system_for_chat);
+        free(dm_context);
+
         if (!response) {
             const char* fallback = "I could not get a response from the LLM right now.";
             fprintf(stdout, "[didactyl] llm response unavailable, sending fallback\n");
@@ -2114,19 +2154,19 @@ void agent_on_message(const char* sender_pubkey_hex,
                 strlen(response) > 240 ? "..." : "");
         (void)nostr_handler_send_dm_auto(sender_pubkey_hex, response);
         free(response);
-        free(dm_context);
         return;
     }
 
     char* tools_json = tools_build_openai_schema_json(&g_tools_ctx);
     if (!tools_json) {
+        free(dm_context);
         (void)nostr_handler_send_dm_auto(sender_pubkey_hex, "Tool schema generation failed.");
         return;
     }
 
     cJSON* messages = cJSON_CreateArray();
     if (!messages ||
-        append_simple_message(messages, "system", dm_context ? dm_context : "You are an AI agent. Respond to the message.") != 0 ||
+        append_simple_message(messages, "system", dm_context) != 0 ||
         append_simple_message(messages, "user", message) != 0) {
         cJSON_Delete(messages);
         free(tools_json);
@@ -2140,7 +2180,7 @@ void agent_on_message(const char* sender_pubkey_hex,
         cJSON_AddNumberToObject(live_user_msg, "_ts", (double)time(NULL));
     }
 
-    int max_turns = g_cfg->tools.max_turns > 0 ? g_cfg->tools.max_turns : 20;
+    int max_turns = g_cfg->tools.max_turns > 0 ? g_cfg->tools.max_turns : 40;
     int stall_repeat_threshold = g_cfg->tools.stall_repeat_threshold > 1 ? g_cfg->tools.stall_repeat_threshold : 3;
     uint64_t last_tool_fp = 0;
     int repeated_tool_turns = 0;
@@ -2156,8 +2196,6 @@ void agent_on_message(const char* sender_pubkey_hex,
             break;
         }
 
-        append_context_log(sender_pubkey_hex, "llm_chat_with_tools_messages", messages_json);
-
         llm_response_t resp;
         int rc = llm_chat_with_tools_messages(messages_json, tools_json, "auto", &resp);
         free(messages_json);
@@ -2222,6 +2260,15 @@ void agent_on_message(const char* sender_pubkey_hex,
                 (void)nostr_handler_send_dm_auto(sender_pubkey_hex, "Failed to append tool result.");
                 return;
             }
+            if (maybe_append_loaded_skill_instructions(messages, tc->name, tool_result) != 0) {
+                free(tool_result);
+                llm_response_free(&resp);
+                cJSON_Delete(messages);
+                free(tools_json);
+                free(dm_context);
+                (void)nostr_handler_send_dm_auto(sender_pubkey_hex, "Failed to apply loaded skill instructions.");
+                return;
+            }
             free(tool_result);
         }
 
@@ -2249,10 +2296,22 @@ void agent_on_message(const char* sender_pubkey_hex,
     if (!final_answer_owned) {
         final_answer_owned = strdup(exited_on_stall
                                         ? "I stopped repeated tool calls after detecting a loop and could not produce a final summary."
-                                        : "I hit my tool-use limit for this request.");
+                                        : "I reached the tool-turn limit before getting a final response from the model.");
     }
 
-    const char* final_answer = final_answer_owned ? final_answer_owned : "I hit my tool-use limit for this request.";
+    if (exhausted_on_max_turns) {
+        const char* base = final_answer_owned ? final_answer_owned : "I reached the tool-turn limit before getting a final response from the model.";
+        const char* suffix = "\n\nI have paused to avoid running forever. Reply with 'continue' and I will continue from the current context.";
+        size_t need = strlen(base) + strlen(suffix) + 1U;
+        char* with_prompt = (char*)malloc(need);
+        if (with_prompt) {
+            snprintf(with_prompt, need, "%s%s", base, suffix);
+            free(final_answer_owned);
+            final_answer_owned = with_prompt;
+        }
+    }
+
+    const char* final_answer = final_answer_owned ? final_answer_owned : "I reached the tool-turn limit before getting a final response from the model.";
 
     if (exited_on_stall || exhausted_on_max_turns) {
         notify_admin_limit_diagnostic("dm_agent_loop",

src/agent.h

@@ -23,7 +23,7 @@ int agent_build_admin_messages_json(const char* current_user_message,
                                     char** out_messages_json);
 tools_context_t* agent_tools_context(void);
 const char* agent_classify_message_part(cJSON* msg, int idx);
-void agent_append_context_log(const char* sender_pubkey_hex, const char* phase, const char* context_payload);
+void agent_invalidate_adopted_skills_cache(void);
 void agent_cleanup(void);
 
 #endif