agentleFS
weininghuiweininghui/skills/skill-vetter/SKILL.md

skill-vetter

Skill1 starsChanged 13 days agoMITView on GitHub

Security-first skill vetting for AI agents. Use before installing any skill from ClawdHub, GitHub, or other sources. Checks for red flags, permission scope, and suspicious patterns.

  • Reads credentials

27 other projects use a version of this, 17 of them edited.

Adopted by

Most recently taken up by

Used with

Projects with it also use aminer-data-search, auto-target-tracker and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
  • dav-niu474Skill

    aminer-data-search

    使用 AMiner 开放平台 API 进行学术数据查询与分析。当用户需要查询学者信息、论文详情、机构数据、期刊内容或专利信息时使用此 skill。 触发场景:提到 AMiner、学术数据查询、查论文/学者/机构/期刊/专利、学术问答搜索、引用分析、科研机构分析、学者画像、论文引用链、期刊投稿分析等。 支持 6 大组合工作流(学者全景分析、论文深度挖掘、机构研究力分析、期刊论文监控、学术智能问答、专利链分析)以及 28 个独立 API 的直接调用。 即使用户只说"帮我查一下 XXX 学者"或"找找关于 XXX 的论文",也应主动使用此 skill。

    Also in 4 projects that use this

  • dav-niu474Skill

    auto-target-tracker

    自动目标进度追踪器。在对话中检测到目标相关图片(笔记、进度、截图、记录)时,自动调用 VLM 识别关键信息并记录到目标日记。适用于学习管理、健身追踪、工作进度、习惯养成、创作记录等所有目标管理场景。

    Also in 4 projects that use this

  • Liang-MHSkill

    stock_analysis

    获取 A 股、基金和港股的实时行情和技术分析数据

    Also in 4 projects that use this

  • +9 more from dav-niu474

The file

We split this skill into the rules it gives an agent

9 rules. 7 are common, 0 are distinctive, and 2 are very specific to this project.

  • 7

    Common

    Most of the 27 edited versions we read keep these.

  • 0

    Distinctive

    This author's own choices. Worth a look.

  • 2

    Very specific

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

  • Common
  • Distinctive
  • Very specific
  1. Common:

    Before installing any skill from ClawdHub

    line 14
  2. Very specific:

    Before running skills from GitHub repos

    line 15
  3. Very specific:

    When evaluating skills shared by other agents

    line 16
  4. Common:

    Read ALL files in the skill.

    line 34
  5. Common:

    After vetting, produce this report:

    line 78
  6. Common:

    No skill is worth compromising security

    line 132
  7. Common:

    When in doubt, don't install

    line 133
  8. Common:

    Ask your human for high-risk decisions

    line 134
  9. Common:

    Document what you vet for future reference

    line 135

“Common” is measured: kept by at least 6 in 10 of the 27 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 Vetter 🔒

Security-first vetting protocol for AI agent skills. Never install a skill without vetting it first.

When to Use

  • Before installing any skill from ClawdHub
  • Before running skills from GitHub repos
  • When evaluating skills shared by other agents
  • Anytime you're asked to install unknown code

Vetting Protocol

Step 1: Source Check

Questions to answer:
- [ ] Where did this skill come from?
- [ ] Is the author known/reputable?
- [ ] How many downloads/stars does it have?
- [ ] When was it last updated?
- [ ] Are there reviews from other agents?

Step 2: Code Review (MANDATORY)

Read ALL files in the skill. Check for these RED FLAGS:

🚨 REJECT IMMEDIATELY IF YOU SEE:
─────────────────────────────────────────
• curl/wget to unknown URLs
• Sends data to external servers
• Requests credentials/tokens/API keys
• Reads ~/.ssh, ~/.aws, ~/.config without clear reason
• Accesses MEMORY.md, USER.md, SOUL.md, IDENTITY.md
• Uses base64 decode on anything
• Uses eval() or exec() with external input
• Modifies system files outside workspace
• Installs packages without listing them
• Network calls to IPs instead of domains
• Obfuscated code (compressed, encoded, minified)
• Requests elevated/sudo permissions
• Accesses browser cookies/sessions
• Touches credential files
─────────────────────────────────────────

Step 3: Permission Scope

Evaluate:
- [ ] What files does it need to read?
- [ ] What files does it need to write?
- [ ] What commands does it run?
- [ ] Does it need network access? To where?
- [ ] Is the scope minimal for its stated purpose?

Step 4: Risk Classification

Risk LevelExamplesAction
🟢 LOWNotes, weather, formattingBasic review, install OK
🟡 MEDIUMFile ops, browser, APIsFull code review required
🔴 HIGHCredentials, trading, systemHuman approval required
⛔ EXTREMESecurity configs, root accessDo NOT install

Output Format

After vetting, produce this report:

SKILL VETTING REPORT
═══════════════════════════════════════
Skill: [name]
Source: [ClawdHub / GitHub / other]
Author: [username]
Version: [version]
───────────────────────────────────────
METRICS:
• Downloads/Stars: [count]
• Last Updated: [date]
• Files Reviewed: [count]
───────────────────────────────────────
RED FLAGS: [None / List them]

PERMISSIONS NEEDED:
• Files: [list or "None"]
• Network: [list or "None"]  
• Commands: [list or "None"]
───────────────────────────────────────
RISK LEVEL: [🟢 LOW / 🟡 MEDIUM / 🔴 HIGH / ⛔ EXTREME]

VERDICT: [✅ SAFE TO INSTALL / ⚠️ INSTALL WITH CAUTION / ❌ DO NOT INSTALL]

NOTES: [Any observations]
═══════════════════════════════════════

Quick Vet Commands

For GitHub-hosted skills:

# Check repo stats
curl -s "https://api.github.com/repos/OWNER/REPO" | jq '{stars: .stargazers_count, forks: .forks_count, updated: .updated_at}'

# List skill files
curl -s "https://api.github.com/repos/OWNER/REPO/contents/skills/SKILL_NAME" | jq '.[].name'

# Fetch and review SKILL.md
curl -s "https://raw.githubusercontent.com/OWNER/REPO/main/skills/SKILL_NAME/SKILL.md"

Trust Hierarchy

  1. Official OpenClaw skills → Lower scrutiny (still review)
  2. High-star repos (1000+) → Moderate scrutiny
  3. Known authors → Moderate scrutiny
  4. New/unknown sources → Maximum scrutiny
  5. Skills requesting credentials → Human approval always

Remember

  • No skill is worth compromising security
  • When in doubt, don't install
  • Ask your human for high-risk decisions
  • Document what you vet for future reference

Paranoia is a feature. 🔒🦀

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.