coderClaw

LLM Task

llm-task is an optional plugin tool that runs a JSON-only LLM task and returns structured output (optionally validated against JSON Schema).

This is ideal for workflow engines like Lobster: you can add a single LLM step without writing custom CoderClaw code for each workflow.

Enable the plugin

1. Enable the plugin:

{
  "plugins": {
    "entries": {
      "llm-task": { "enabled": true }
    }
  }
}

1. Allowlist the tool (it is registered with optional: true):

{
  "agents": {
    "list": [
      {
        "id": "main",
        "tools": { "allow": ["llm-task"] }
      }
    ]
  }
}

Config (optional)

{
  "plugins": {
    "entries": {
      "llm-task": {
        "enabled": true,
        "config": {
          "defaultProvider": "openai-codex",
          "defaultModel": "gpt-5.2",
          "defaultAuthProfileId": "main",
          "allowedModels": ["openai-codex/gpt-5.3-codex"],
          "maxTokens": 800,
          "timeoutMs": 30000
        }
      }
    }
  }
}

allowedModels is an allowlist of provider/model strings. If set, any request outside the list is rejected.

Tool parameters

• prompt (string, required)
• input (any, optional)
• schema (object, optional JSON Schema)
• provider (string, optional)
• model (string, optional)
• authProfileId (string, optional)
• temperature (number, optional)
• maxTokens (number, optional)
• timeoutMs (number, optional)

Output

Returns details.json containing the parsed JSON (and validates against schema when provided).

Example: Lobster workflow step

coderclaw.invoke --tool llm-task --action json --args-json '{
  "prompt": "Given the input email, return intent and draft.",
  "input": {
    "subject": "Hello",
    "body": "Can you help?"
  },
  "schema": {
    "type": "object",
    "properties": {
      "intent": { "type": "string" },
      "draft": { "type": "string" }
    },
    "required": ["intent", "draft"],
    "additionalProperties": false
  }
}'

Safety notes

• The tool is JSON-only and instructs the model to output only JSON (no code fences, no commentary).
• No tools are exposed to the model for this run.
• Treat output as untrusted unless you validate with schema.
• Put approvals before any side-effecting step (send, post, exec).

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