No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by ucsandman · MCP Server · ★ 306
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is DashClaw safe to install? View the security audit →
DashClaw Govern AI agents before they act. 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. Plugs into the agents you already run: Claude Code, Codex, Hermes Agent, OpenClaw, Claude Desktop, and Claude Managed Agents. Framework integrations for LangChain, CrewAI, AutoGen, LangGraph, and OpenAI Agents SDK. Any other runtime over MCP, the Node/Python SDK, or direct REST. <img src="https://img.shields.io/b
| Stars | 306 |
| Forks | 52 |
| Language | TypeScript |
| Category | MCP Server |
| License | MIT |
| Quality Score | 63.3612813500721/100 |
| Open Issues | 12 |
| Last Updated | 2026-09-19 |
| Created | 2026-02-08 |
| Platforms | claude-code, mcp, node |
| Est. Tokens | ~20k |
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DashClaw is Remote approvals, policy checks, and execution evidence for unattended AI agents.. It is categorized as a MCP Server with 306 GitHub stars.
DashClaw is primarily written in TypeScript. It covers topics such as agent-framework, agent-governance, agent-runtime.
You can find installation instructions and usage details in the DashClaw GitHub repository at github.com/ucsandman/DashClaw. The project has 306 stars and 52 forks, indicating an active community.
DashClaw is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to DashClaw on Agent Skills Hub include Network-AI, swarmclaw, forge-orchestrator. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
Sources & who's responsible: