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: mcp-for-beginners
Author: microsoft · Source status: Clear source
This open-source curriculum introduces the fundamentals of Model Context Protocol (MCP) through real-world, cross-language examples in .NET, Java, TypeScript, JavaScript, Rust and Python.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
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
agentic-ai-engineering
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
git-commit
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
ultimate-prompt-engineering-playbook
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | agentic-ai-engineering | git-commit | ultimate-prompt-engineering-playbook |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 82 · Manual review | 79 · Manual review | 82 · 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 | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, data exfiltration, human approval gap | prompt injection, tool poisoning, unexpected code execution |
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 | 68% | 65% | 67% |
| Source & provenance | |||
Provenance | agenticloops-ai/agentic-ai-engineering | github/awesome-copilot/tree/main/skills/git-commit | amerob/ultimate-prompt-engineering-playbook |
Category | Dev & Engineering | Operations & Infra | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | runs shell, writes files | No explicit signals |
Permission hints | repository clone, local runtime dependencies | registry access, remote metadata pull, runtime dependencies may be required | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, Cursor, Universal | Universal |
Install method | script-backed | registry-install | script-backed |
Install friction | |||
| Community | |||
Stars | 49 | 20.6K | 19 |
Repository: NVIDIA-Nemotron-3-Super
Author: cobusgreyling · Source status: Clear source
Controllable reasoning demos for NVIDIA Nemotron 3 Super (120B/12B MoE) — chat UI, CLI, API server, tool calling, budget sweep, and adaptive routing Topics: gradio, llm, mixture-of-experts, moe, nemotron, nim, nvidia, re
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: splitrail
Author: Piebald-AI · Source status: Clear source
Fast, cross-platform, real-time token usage tracker and cost monitor for Gemini CLI / Claude Code / Codex CLI / Qwen Code / Cline / Roo Code / Kilo Code / GitHub Copilot / OpenCode / Pi Agent / Piebald.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: entroly
Author: juyterman1000 · Source status: Clear source
Entroly helps AI coding tools like Cursor, Copilot, and Claude Code use the right context from your entire codebase—improving output quality while reducing token usage.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
2026-04-07 |
2026-02-04 |
2026-04-06 |
40 |
60 |
40 |
