Lukas Parsons
7f7699ff94
- FastAPI backend with SQLite (ai.db) - Tables: skills, snippets, conventions, cache, memory - MCP servers: homelab, gameservers, skills - Docker Compose setup - Seed data with 8 skills, 2 conventions, 2 snippets - Token savings patterns via context bundles and caching
99 lines
2.9 KiB
Markdown
99 lines
2.9 KiB
Markdown
# AI Skills API
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Local infrastructure for AI context management. Store skills, snippets, conventions, and cache responses to reduce token consumption.
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## Quick Start
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```bash
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# Copy env file
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cp .env.example .env
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# Run with Docker
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docker compose up -d
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# Or run locally
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pip install -r requirements.txt
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uvicorn main:app --reload
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```
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API available at `http://localhost:8080`
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Docs at `http://localhost:8080/docs`
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## Endpoints
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| Endpoint | Description |
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|----------|-------------|
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| `GET /skills` | List all skills |
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| `GET /skills/{id}` | Get skill (increments usage_count) |
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| `POST /skills` | Create skill |
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| `PUT /skills/{id}` | Update skill |
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| `DELETE /skills/{id}` | Delete skill |
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| `GET /skills/search?q=query` | Search skills |
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| `GET /snippets` | List snippets |
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| `GET /snippets/{id}` | Get snippet |
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| `POST /snippets` | Create snippet |
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| `DELETE /snippets/{id}` | Delete snippet |
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| `GET /conventions` | List conventions |
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| `GET /conventions?project=/path` | Get conventions for project |
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| `POST /conventions` | Create convention |
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| `PUT /conventions/{id}` | Update convention |
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| `DELETE /conventions/{id}` | Delete convention |
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| `POST /cache/lookup` | Check cache for prompt |
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| `POST /cache/store` | Store response in cache |
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| `GET /cache/stats` | Cache statistics |
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| `GET /memory` | List memory entries |
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| `GET /memory?project=name` | Get memory for project |
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| `POST /memory` | Create memory entry |
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| `PUT /memory/{id}` | Update memory |
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| `DELETE /memory/{id}` | Delete memory |
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| `GET /context?project=/path&skills=id1,id2` | Get full context bundle |
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## Example Usage
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### Create a skill
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```bash
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curl -X POST http://localhost:8080/skills \
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-H "Content-Type: application/json" \
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-d '{
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"id": "homelab-docker-compose",
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"name": "Docker Compose Standard",
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"category": "homelab",
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"content": "Always use docker-compose v3.8+. Include health checks, restart policies, and resource limits.",
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"tags": ["docker", "compose", "infrastructure"]
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}'
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```
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### Get context bundle
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```bash
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curl "http://localhost:8080/context?project=/home/server/apps/media-server&skills=homelab-docker-compose,react-v2"
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```
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### Check cache
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```bash
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curl -X POST http://localhost:8080/cache/lookup \
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-H "Content-Type: application/json" \
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-d '{
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"prompt": "How do I configure traefik?",
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"model": "claude-3-opus"
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}'
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```
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## Integration Pattern
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In your agent's system prompt or pre-request hook:
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1. Call `GET /context?project={current_project}&skills={skill_ids}`
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2. Inject returned content into the prompt
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3. Before sending to LLM, check `POST /cache/lookup`
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4. After receiving response, optionally `POST /cache/store`
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This avoids re-sending your standards every request and caches repeated queries.
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## Database
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SQLite database `ai.db` with tables:
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- `skills` - Reusable patterns and instructions
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- `snippets` - Code snippets
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- `conventions` - Project-specific conventions
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- `cache` - LRU cache of LLM responses
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- `memory` - Project memory/notes
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