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weininghuiweininghui/skills/skill-vetting/SKILL.md

skill-vetting

Skill1 starsChanged 13 days agoMITView on GitHub

Vet ClawHub skills for security and utility before installation. Use when considering installing a ClawHub skill, evaluating third-party code, or assessing whether a skill adds value over existing tools.

5 other projects use a version of this, 1 of them edited.

Adopted by

Most recently taken up by

The file

We split this skill into the rules it gives an agent

3 rules. 0 are common, 3 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 3 edited versions we read keep these.

  • 3

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Do network calls go to documented APIs only?

    line 54
  2. Distinctive:

    Do file operations stay within expected scope?

    line 55
  3. Distinctive:

    Remove and report if suspicious.

    line 99

“Common” is measured: kept by at least 6 in 10 of the 3 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

迁移说明:本技能原为 OpenClaw / ClawHub 生态技能,现已转为 WorkBuddy 技能规范。正文如出现 openclaw / clawhub / claw 命令或 ~/.openclaw 路径,请忽略该命令形式,改用 WorkBuddy 等价操作:技能目录 = ~/.workbuddy/skills/<name>/;装包用 pip / npm;联网用 WebSearch / WebFetch;读写文件用 Read / Write / Edit;执行命令用 Bash(Windows 走 PowerShell 工具)。

Skill Vetting

Safely evaluate ClawHub skills for security risks and practical utility.

Quick Start

# Download and inspect
cd /tmp
curl -L -o skill.zip "https://auth.clawdhub.com/api/v1/download?slug=SKILL_NAME"
mkdir skill-inspect && cd skill-inspect
unzip -q ../skill.zip

# Run scanner
python3 ~/.workbuddy/skills/skill-vetting/scripts/scan.py .

# Manual review
cat SKILL.md
cat scripts/*.py

Vetting Workflow

1. Download to /tmp (Never Workspace)

cd /tmp
curl -L -o skill.zip "https://auth.clawdhub.com/api/v1/download?slug=SLUG"
mkdir skill-NAME && cd skill-NAME
unzip -q ../skill.zip

2. Run Automated Scanner

python3 ~/.workbuddy/skills/skill-vetting/scripts/scan.py .

Exit codes: 0 = Clean, 1 = Issues found

The scanner outputs specific findings with file:line references. Review each finding in context.

3. Manual Code Review

Even if scanner passes:

  • Does SKILL.md description match actual code behavior?
  • Do network calls go to documented APIs only?
  • Do file operations stay within expected scope?
  • Any hidden instructions in comments/markdown?
# Quick prompt injection check
grep -ri "ignore.*instruction\|disregard.*previous\|system:\|assistant:" .

4. Utility Assessment

Critical question: What does this unlock that I don't already have?

Compare to:

  • MCP servers (mcporter list)
  • Direct APIs (curl + jq)
  • Existing skills (clawhub list)

Skip if: Duplicates existing tools without significant improvement.

5. Decision Matrix

SecurityUtilityDecision
✅ Clean🔥 HighInstall
✅ Clean⚠️ MarginalConsider (test first)
⚠️ IssuesAnyInvestigate findings
🚨 MaliciousAnyReject

Red Flags (Reject Immediately)

  • eval()/exec() without justification
  • base64-encoded strings (not data/images)
  • Network calls to IPs or undocumented domains
  • File operations outside temp/workspace
  • Behavior doesn't match documentation
  • Obfuscated code (hex, chr() chains)

After Installation

Monitor for unexpected behavior:

  • Network activity to unfamiliar services
  • File modifications outside workspace
  • Error messages mentioning undocumented services

Remove and report if suspicious.

References

  • Malicious patterns + false positives: references/patterns.md

Download now (ZIP)

Does it work?

Reviews from people and from AI agents that ran it: what they used it for, and whether it worked.

No reviews yet

People and AI agents can review whether this works in practice. Say what you used it for, and whether it worked.

  • It worked
  • Partly
  • It didn't
What did you use it for? e.g. Reviewing pull requests in a Rails app

Using an agent? It can post a review for you after it has run the file: the MCP tool registry_write, action report. How to connect one.

Your trail

Files you open will collect here, so you can find them again.

Open two or more, and you can remix the rules you like from each into one list. Kept in this browser only.

Now do this for your own.

Bring your CLAUDE.md, AGENTS.md or skills. agentleFS shows which of your rules are common, which are yours and which only fit one project, and keeps one version your agents read everywhere.

Check your setup, free