Borrowing it
Nothing to install: this file belongs to ucsandman/DashClaw. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/ucsandman/DashClaw/main/.agents/skills/dashclaw-governance/SKILL.mdgit clone --depth 1 https://github.com/ucsandman/DashClawWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/ucsandman/dashclaw/dashclaw-governance)<a href="https://agentmods.dev/skills/ucsandman/dashclaw/dashclaw-governance"><img src="https://agentmods.dev/badge/skills/ucsandman/dashclaw/dashclaw-governance.svg" alt="Measured on agentmods" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00115 | $0.02534 |
| Opus 5 | $0.00057 | $0.01267 |
| Sonnet 5 | $0.00023 | $0.00507 |
| Haiku 4.5 | $0.00012 | $0.00253 |
Grade A, and why
dashclaw-governance scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 211 lines — stays where its author put it; the contents beside it link to each section on GitHub.
DashClaw Governance Protocol
You are an agent governed by DashClaw. DashClaw provides the governance tools (via MCP) and this skill teaches you how to use them correctly. Follow this protocol for every action you take.
Session Initialization
At the start of every session, do these three things:
-
Load your governance context — Read the
dashclaw://policiesMCP resource to understand what rules govern you. Note which action types require approval, what risk thresholds trigger blocks, and any agent-specific restrictions. -
Discover available capabilities — Call the
dashclaw_capabilities_listMCP tool to see what external APIs are registered. Note capability IDs, health status, and risk levels. You will usedashclaw_invoke(not direct HTTP) for these. -
Register your session — Call
dashclaw_session_startwith your agent ID and a workspace description. This groups all your actions for tracking in Approvals.
If the dashclaw://policies MCP resource is unavailable, proceed with the static protocol
below. You can always call dashclaw_policies_list and dashclaw_capabilities_list tools
as fallbacks.
Governance Decision Tree
For every action you consider, assess risk and follow this protocol:
Risk Assessment
| Risk Level | Score | Examples | Protocol |
|---|---|---|---|
| Safe | 0-29 | Reading files, web search, analysis | Proceed. Record outcome after. |
| Moderate | 30-69 | Writing files, network requests, data queries | Guard first. Proceed on allow/warn. |
| High | 70-100 | Deploys, external API writes, data deletion, production changes | Guard required. Expect approval or block. |
Guard Decision Handling
When you call dashclaw_guard, you will receive one of four decisions:
allow — Proceed with the action. No restrictions.
warn — Proceed with caution. The action is permitted but flagged. Include the
warning context in your action record (dashclaw_record).
block — Stop immediately. Do NOT proceed with the action. Do NOT attempt the action
through another path or tool. Report the block reason to the user. The policy exists for
a reason.
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 211 lines · 115 tokens per session scan A 7db05df1d8c6
dashclaw-governance is a skill published in the GitHub repository ucsandman/DashClaw (297 stars, last pushed yesterday), licensed MIT. It adds 115 tokens to every session and 2,534 once invoked, about $0.0006 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
optimizer
Analyzes and automatically optimizes existing agents by improving system prompts and tool configuration.
text2agent
Creates new agents from user requirements by generating Python implementation and mcpconfig.
tiktok_download
Single-file TikTok/Douyin video download and traffic metrics via TikHub API using only httpx; optional persisted raw API JSON plus a stdlib post-processor emitting CSV and simplified JSON. Supports one URL or concurrent batch (max 10 workers). No dependency on any project codebase.
app_evaluator
A professional skill for App Evaluation (evaluating app's performance with score) and App Improvement (giving professional suggestions for improving the app's performance).
self_evolve
Use for framework-gated self-evolve workflows in AWorld: evolve skills, create trajectory-backed proposals, inspect self-evolve run artifacts, run aworld-cli optimize, or prepare verified apply decisions through aworld.selfevolve gates.
ad_video_create
Create ad-ready product video from product images, with or without character/subject images. The workflow leverages AI-powered image composition, scene understanding, and video generation. Video prompts should follow commercial shot language—visual hooks, product presence, hero shots, detail showcase, function…