# AI Skills API

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

## Quick Start

```bash
# 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://localhost:8080`
Docs at `http://localhost:8080/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
```bash
curl -X POST http://localhost:8080/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
```bash
curl "http://localhost:8080/context?project=/home/server/apps/media-server&skills=homelab-docker-compose,react-v2"
```

### Check cache
```bash
curl -X POST http://localhost:8080/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