DashClaw

Source#

• Repository: ucsandman/DashClaw
• Last source update: 2026-05-16
• Last verified: 2026-05-17

Tags#

Integration notes#

Framework-level abstraction; derive bounded worker contracts from concrete tasks and APIs in docs/examples.

worker.md example#

Starter worker.md contract mapped from this registry entry. Copy this file and adapt schemas, constraints, and statuses for your task.

---
id: dashclaw-repo-derived-worker
name: DashClaw Repo-Derived Worker
version: 1.0.0
source_registry_url: https://worker.md/registry/dashclaw/
source_repository: https://github.com/ucsandman/DashClaw
repository_default_branch: main
repository_language: JavaScript
repository_license: MIT
repository_updated_at: 2026-05-16
worker_mode: agent-orchestration-worker
derivation_method: github_repository_metadata_plus_raw_readme
derivation_confidence: 0.95
derived_on: 2026-05-17
tags:
  - agent-framework
  - agent-governance
  - agent-runtime
  - ai-agents
  - ai-infrastructure
  - ai-ops
---

# DashClaw Repo-Derived Worker

## Repo-derived summary
- Registry summary: 🛡️Decision infrastructure for AI agents. Intercept actions, enforce guard policies, require approvals, and produce audit-ready decision trails.
- Repository description: 🛡️Decision infrastructure for AI agents. Intercept actions, enforce guard policies, require approvals, and produce audit-ready decision trails.
- Stars (snapshot): 264
- Primary language: JavaScript
- Worker mode classification: agent-orchestration-worker

## Extracted from
- https://github.com/ucsandman/DashClaw
- https://github.com/ucsandman/DashClaw/blob/main/README.md
- https://dashclaw.io/downloads
- https://clawhub.ai/@dashclaw

## Evidence notes (from repository text)
- README summary paragraph: DashClaw is the governance layer for AI agents that touch real systems. It sits between agents and the world, evaluates policy on every risky action, routes human approval where it is required, records verifiable evidence, and tracks terminal outcomes so a retried agent never silently double-executes.
- DashClaw is the governance layer for AI agents that touch real systems.
- It sits between agents and the world, evaluates policy on every risky action,
- and tracks terminal outcomes so a retried agent never silently double-executes.
- | **Intercept** | Risky agent actions are evaluated before they execute. Block, warn, or hold for approval, by policy. |
- | **Govern external systems** | The capability registry wraps real HTTP APIs with per-agent access rules, rate limits, and audit. Workflows compose these into multi-step governed runs. |

## Installation hints found in README
- `pip install dashclaw` |`
- `pip install dashclaw # Python 3.7+`
- `pip install dashclaw`
- `npm install @dashclaw/openclaw-plugin` |`

## worker.md contract (derived starter)
Purpose: Execute one orchestrated agent task as a bounded worker step.

### Input schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "task",
    "context"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "task": {
      "type": "string"
    },
    "context": {
      "type": "object"
    }
  }
}
```

### Output schema
```json
{
  "type": "object",
  "additionalProperties": false,
  "required": [
    "run_id",
    "status",
    "result"
  ],
  "properties": {
    "run_id": {
      "type": "string"
    },
    "status": {
      "type": "string",
      "enum": [
        "ok",
        "retryable_error",
        "invalid_request",
        "invalid_output"
      ]
    },
    "result": {
      "type": "object"
    }
  }
}
```

### Constraints
- timeout_seconds: 30
- max_attempts: 2
- idempotency_key: run_id
- status_enum: [ok, retryable_error, invalid_request, invalid_output]
- notes: adapt to concrete APIs/classes documented in this repository before production use

## How this should be used
1. Treat this file as a repo-derived starter profile, not a claim of an official repository API contract.
2. Replace schemas with exact interfaces from code/docs you adopt.
3. Keep execution bounded and auditable using worker protocol constraints.

How to use#

• Save this as a worker spec file (for example: dashclaw-my-task.worker.md).
• Replace the input/output schemas and purpose with your real bounded task.
• Enforce schema validation + timeout + retry policy in your runtime before production use.

Citation#

Reference URL: https://worker.md/registry/dashclaw/

Source URL: https://github.com/ucsandman/DashClaw