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: scraper-mcp
Author: jessaminesimple608 · Source status: Clear source
🌐 Streamline web scraping with Scraper MCP, a server that optimizes content for AI by filtering data and reducing token usage for LLMs.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
Search to add skills, or paste 2–4 comma-separated slugs.
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
Ai-Resume-Analyzer
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
skill-seekers
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
crawlee
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | Ai-Resume-Analyzer | skill-seekers | crawlee |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 82 · Manual review | 88 · Evidence missing | 81 · Manual review |
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, data exfiltration, memory context poisoning |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Command |
Evidence completeness | 65% | 71% | 68% |
| Source & provenance | |||
Provenance | SagarBiswas-MultiHAT/Ai-Resume-Analyzer | openclaw/skills | apify/crawlee |
Category | Web & Automation | Dev & Engineering | Web & Automation |
Freshness | |||
| Risk & permission signals | |||
Audit signals | network access, writes files | needs credentials, network access, runs shell, writes files | network access, writes files |
Permission hints | repository clone, local runtime dependencies | verify source provenance before install | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Claude, Cursor, Windsurf | Universal |
Install friction | 40 | 65 | 40 |
| Community | |||
Stars | 13 | 0 | 22.7K |
Repository: openclaw/skills
Author: krishnakumarmahadevan-cmd · Source status: Clear source
Generate professional resumes and cover letters from structured data with optional resume parsing capabilities.
Score basis：Clear source · High execution risk · OpenClaw · Evidence completeness 65%
Repository: openclaw/skills
Author: apscrapes · Source status: Clear source
Extract structured product data from e-commerce URLs using the Zyte API and generate side-by-side comparison tables with intelligent purchase recommendations.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: ChatAutomation-AI-Assistant
Author: SagarBiswas-MultiHAT · Source status: Clear source
Automates chat replies for Messenger and WhatsApp using PyAutoGUI for UI actions, Pyperclip for clipboard capture, and Groq for AI responses.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2026-02-25 |
2026-04-04 |
2026-04-01 |
