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: Claude-Code-Agent-Monitor
Author: hoangsonww · Source status: Clear source
A real-time monitoring dashboard for Claude Code agents, built with SQLite3, Node.js, Express, React, Vite, TailwindCSS, and WebSockets.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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
rox
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Icebreaker Openers. 破冰。Rompehielos.
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
mcp-for-beginners
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | rox | Icebreaker Openers. 破冰。Rompehielos. | mcp-for-beginners |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 82 · Manual review | 85 · Evidence missing | 85 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, Universal |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, memory context poisoning |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Command |
Evidence completeness | 65% | 69% | 67% |
| Source & provenance | |||
Provenance | BeWelcome/rox | openclaw/skills | microsoft/mcp-for-beginners |
Category | Dev & Engineering | Integrations & Connectors | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | needs credentials, network access, runs shell, writes files | No explicit signals |
Permission hints | repository clone, local runtime dependencies | verify source provenance before install | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Claude, OpenClaw | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 210 | 0 | 15.8K |
Repository: DemoGPT
Author: melih-unsal · Source status: Clear source
🤖 Everything you need to create an LLM Agent—tools, prompts, frameworks, and models—all in one place.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: tool_calling_api
Author: Shuyib · Source status: Clear source
This project demonstrates function-calling with Python and Ollama, utilizing the Africa's Talking API to send airtime and messages to phone numbers using natural language prompts.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: OpenTokenMonitor
Author: Hitheshkaranth · Source status: Clear source
OpenTokenMonitor is a lightweight, local-first desktop widget for tracking AI CLI usage across Claude, Codex, and Gemini.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: Promet
Author: Tripadh · Source status: Clear source
Promet is an AI-powered prompt engineering and optimization platform that helps users generate, refine, and evaluate high-quality prompts for better AI outputs.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-03-30 |
2026-04-01 |
2026-04-08 |
40 |
65 |
40 |
