skill-vetter
Security gate that scans skills for malicious code, vulnerabilities, and suspicious patterns before installation.
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Install Skill
Run in your terminal
3
installs
3
this week
17
stars
What it does
Runs four integrated scanners: aguara (prompt injection detection), skill-analyzer (malicious patterns and CVE database), secrets-scan (hardcoded credentials), and structure-check (malformed files and dangerous configurations)
Accepts ClawHub skill names, GitHub URLs, or local paths as input and returns a three-tier verdict: BLOCKED (critical/high findings), REVIEW (medium f
Installation Guide
How to use skill-vetter on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your machine
- ›Node.js 16+ with npm — verify with
node --version - ›Active project directory where you want to add
skill-vetter
Run the install command
Execute the skills CLI command in your project's root directory to begin installation:
Fetches skill-vetter from app-incubator-xyz/skill-vetter and configures it for Cursor.
Select Cursor when prompted
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate skill-vetter. Access via /skill-vetter in your agent's command palette.
Security Notice
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Documentation
Skill Vetter
Security gate that runs multiple scanners against a skill before installation.
When to Use
Use before installing ANY skill to Claude Code, OpenClaw, or your other favorite AI agent — whether from ClawHub, GitHub, or any external source.
Ask the user: "Should I run skill-vetter on this before installing?" whenever they mention installing a new skill.
How to Run
Check dependencies first
bash {baseDir}/scripts/check-deps.sh
Fix any missing dependencies before proceeding.
Run the full scan
bash {baseDir}/scripts/vett.sh "<skill-name-or-path>"
The argument can be:
- A ClawHub skill name:
youtube-summarize - A GitHub URL:
https://github.com/user/repo - A local path:
/tmp/my-skill/
Interpret Results
| Verdict | Meaning | Action |
|---|---|---|
| BLOCKED | CRITICAL or HIGH findings | Do NOT install. Show findings. |
| REVIEW | Medium severity findings | Show findings, ask user to decide. |
| SAFE | All scanners passed | Proceed with installation. |
After Verdict
Always show the user:
- Which scanners ran
- Which passed/failed
- Specific findings for anything flagged
- Your recommendation
Never install the skill automatically. Always confirm with the user after showing results.
Scanners Used
| Scanner | What It Checks |
|---|---|
| aguara | Prompt injection, obfuscation, suspicious LLM calls |
| skill-analyzer | Known malicious patterns, CVE database |
| secrets-scan | Hardcoded API keys, tokens, credentials |
| structure-check | Missing SKILL.md, malformed YAML, dangerous files |
Example Output
════════════════════════════════════════════════════════════
SKILL VETTER — Security Scan: malicious-skill
Path: /tmp/skill-vetter-abc123/malicious-skill
════════════════════════════════════════════════════════════
[1/4] aguara............. ✅ PASS
[2/4] skill-analyzer..... ❌ FAIL (HIGH: prompt injection pattern)
[3/4] secrets-scan....... ⚠️ WARN (Medium: base64 encoded string)
[4/4] structure-check.... ✅ PASS
════════════════════════════════════════════════════════════
VERDICT: BLOCKED
Reasons: 1 HIGH, 1 MEDIUM
════════════════════════════════════════════════════════════
Do NOT install this skill. It contains:
- HIGH: Prompt injection in SKILL.md (line 47)
- MEDIUM: Base64 encoded string in scripts/run.sh (line 12)
Dependencies
aguara— Go-based prompt scannerskill-analyzer— Cisco AI skill scanner (Python)python3— For additional checkscurl,jq— For API calls and JSON parsing
Run check-deps.sh to verify all tools are installed.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
Use Cases
User Story & Requirements Generation
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Competitive Analysis
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Roadmap Prioritization
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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Reviews
- RRen Bhatia★★★★★Dec 28, 2024
Keeps context tight: skill-vetter is the kind of skill you can hand to a new teammate without a long onboarding doc.
- SSakshi Patil★★★★★Dec 12, 2024
skill-vetter is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- HHiroshi Ghosh★★★★★Dec 4, 2024
skill-vetter has been reliable in day-to-day use. Documentation quality is above average for community skills.
- YYuki Harris★★★★★Nov 23, 2024
Solid pick for teams standardizing on skills: skill-vetter is focused, and the summary matches what you get after install.
- LLucas Diallo★★★★★Nov 19, 2024
We added skill-vetter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- YYuki Singh★★★★★Oct 14, 2024
We added skill-vetter from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- DDiego Tandon★★★★★Oct 10, 2024
Solid pick for teams standardizing on skills: skill-vetter is focused, and the summary matches what you get after install.
- NNoor Gupta★★★★★Sep 25, 2024
skill-vetter is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- NNoor Menon★★★★★Sep 21, 2024
I recommend skill-vetter for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- YYuki Diallo★★★★★Sep 21, 2024
skill-vetter fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
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