AI Skills API

Local infrastructure for AI context management. Store skills, snippets, conventions, and cache responses to reduce token consumption.

Quick Start

# Copy env file
cp .env.example .env

# Run with Docker
docker compose up -d

# Or run locally
pip install -r requirements.txt
uvicorn main:app --reload

API available at http://helm:8675 Docs at http://helm:8675/docs

Endpoints

Endpoint	Description
GET /skills	List all skills
GET /skills/{id}	Get skill (increments usage_count)
POST /skills	Create skill
PUT /skills/{id}	Update skill
DELETE /skills/{id}	Delete skill
GET /skills/search?q=query	Search skills
GET /snippets	List snippets
GET /snippets/{id}	Get snippet
POST /snippets	Create snippet
DELETE /snippets/{id}	Delete snippet
GET /conventions	List conventions
GET /conventions?project=/path	Get conventions for project
POST /conventions	Create convention
PUT /conventions/{id}	Update convention
DELETE /conventions/{id}	Delete convention
POST /cache/lookup	Check cache for prompt
POST /cache/store	Store response in cache
GET /cache/stats	Cache statistics
GET /memory	List memory entries
GET /memory?project=name	Get memory for project
POST /memory	Create memory entry
PUT /memory/{id}	Update memory
DELETE /memory/{id}	Delete memory
GET /context?project=/path&skills=id1,id2	Get full context bundle

Example Usage

Create a skill

curl -X POST http://helm:8675/skills \
  -H "Content-Type: application/json" \
  -d '{
    "id": "homelab-docker-compose",
    "name": "Docker Compose Standard",
    "category": "homelab",
    "content": "Always use docker-compose v3.8+. Include health checks, restart policies, and resource limits.",
    "tags": ["docker", "compose", "infrastructure"]
  }'

Get context bundle

curl "http://helm:8675/context?project=/home/server/apps/media-server&skills=homelab-docker-compose,react-v2"

Check cache

curl -X POST http://helm:8675/cache/lookup \
  -H "Content-Type: application/json" \
  -d '{
    "prompt": "How do I configure traefik?",
    "model": "claude-3-opus"
  }'

Integration Pattern

In your agent's system prompt or pre-request hook:

1. Call GET /context?project={current_project}&skills={skill_ids}
2. Inject returned content into the prompt
3. Before sending to LLM, check POST /cache/lookup
4. After receiving response, optionally POST /cache/store

This avoids re-sending your standards every request and caches repeated queries.

Database

SQLite database ai.db with tables:

• skills - Reusable patterns and instructions
• snippets - Code snippets
• conventions - Project-specific conventions
• cache - LRU cache of LLM responses
• memory - Project memory/notes