# RLM Skill

The RLM (Reflexion Language Model) skill provides computational analysis via code execution. It writes and runs Python code in an isolated Docker sandbox to answer questions that require computation, aggregation, or data traversal.

!!! warning "Requires Docker"
    The `analyze` tool executes code in a Docker sandbox. Docker must be running on the host machine. This skill is not suitable for Docker-deployed applications — use the [`rag`](rag.md) skill alone in those environments.

## `create_skill(db_path?, config?)`

```python
from haiku.rag.skills.rlm import create_skill

skill = create_skill(db_path=db_path, config=config)
```

| Parameter | Default | Description |
|-----------|---------|-------------|
| `db_path` | `None` | Path to LanceDB database. Falls back to `HAIKU_RAG_DB` env var, then config default. |
| `config` | `None` | `AppConfig` instance. If None, uses `get_config()`. |

## Tools

| Tool | Purpose |
|------|---------|
| `analyze(question, document?, filter?)` | Answer analytical questions using code execution |

**Parameters:**

- `question` — The analytical question to answer.
- `document` — Optional document ID or title to pre-load for analysis.
- `filter` — Optional SQL WHERE clause to filter documents.

## State

The skill manages an `RLMState` under the `"rlm"` namespace:

```python
class RLMState(BaseModel):
    analyses: list[AnalysisEntry] = []

class AnalysisEntry(BaseModel):
    question: str
    answer: str
    program: str | None = None
```

Each `analyze` call appends an `AnalysisEntry` with the question, answer, and executed program.

## Usage with RAG Skill

Combine both skills to give the agent full RAG + analysis capabilities:

```python
from haiku.rag.skills.rag import create_skill as create_rag_skill
from haiku.rag.skills.rlm import create_skill as create_rlm_skill
from haiku.skills.agent import SkillToolset
from pydantic_ai import Agent

rag = create_rag_skill(db_path=db_path)
rlm = create_rlm_skill(db_path=db_path)
toolset = SkillToolset(skills=[rag, rlm])

agent = Agent(
    "openai:gpt-4o",
    instructions=toolset.system_prompt,
    toolsets=[toolset],
)
```

See the [RLM Agent](../agents/rlm.md) documentation for details on how the underlying agent works.