Compare skills
Pick 2–4 skills and compare what really matters: fit, risk, install effort, and community signal.
Comparison matrix
Highlights show current best; tooltip explains diff/best rules.
Score-basis diff rules / risk tag notes
Start with the matrix. Open this section when you need to understand audit grades, top threats, control gaps, and best-value highlights.
Suggested baseline
Repository: awesome-copilot
Author: github · Source status: Clear source
Comprehensive guide for creating professional UI/UX designs in Penpot using MCP tools.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 65%
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How differences are detected
A row is marked different when selected skills have distinct values. Only-differences mode hides rows that are identical.
How best values are highlighted
Pre-install score, evidence completeness, and community signal prefer higher values; execution risk and install friction prefer lower values.
How to read risk tags
Risk tags come from SAS-v2.1 public-evidence signals and point to command, network, secret, context, or supply-chain items to review before install.
Selected audit signals
crw
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
OMNI — All-In-One Master Skill
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
scraper-mcp
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | crw | OMNI — All-In-One Master Skill | scraper-mcp |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 88 · Manual review | 82 · Evidence missing |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, Universal |
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, identity privilege abuse, data exfiltration |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 67% | 69% |
| Source & provenance | |||
Provenance | us/crw | openclaw/skills | jessaminesimple608/scraper-mcp |
Category | Web & Automation | Data & Analytics | Web & Automation |
Freshness | |||
| Risk & permission signals | |||
Audit signals | network access | needs credentials, network access, runs shell, writes files | needs credentials, network access |
Permission hints | repository clone, local runtime dependencies | verify source provenance before install | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, OpenClaw | Universal |
Install friction | 40 | 65 | 55 |
| Community | |||
Stars | 38 | 0 | 3 |
Repository: crawlee
Author: apify · Source status: Clear source
Crawlee—A web scraping and browser automation library for Node.js to build reliable crawlers.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
Repository: chatgpt-scraper
Author: daily-coding-problem · Source status: Clear source
A Selenium-based ChatGPT interaction automation tool.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: Ai-Resume-Analyzer
Author: SagarBiswas-MultiHAT · Source status: Clear source
A local-first Flask web app that analyzes PDF/DOCX resumes using a Groq/OpenAI-compatible LLM.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: plaidify
Author: meetpandya27 · Source status: Clear source
Open-source API for authenticated web data extraction.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-08 |
2026-04-02 |
2026-04-07 |
