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%
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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
rox
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
oss-llmops-stack
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 | oss-llmops-stack | mcp-for-beginners |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 82 · Manual review | 83 · Manual review | 85 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | 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, Command | Permission review, Network, Command |
Evidence completeness | 65% | 67% | 67% |
| Source & provenance | |||
Provenance | BeWelcome/rox | langfuse/oss-llmops-stack | microsoft/mcp-for-beginners |
Category | Dev & Engineering | Automation & Workflows | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | metadata-only | No explicit signals |
Permission hints | repository clone, local runtime dependencies | repository clone | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 210 | 136 | 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 |
2025-02-16 |
2026-04-08 |
40 |
75 |
40 |
