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: agentic-ai-engineering
Author: agenticloops-ai · Source status: Clear source
Hands-on tutorials for building AI agents from scratch.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
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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
pydantic-ai
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
1Panel
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
ruflo
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | pydantic-ai | 1Panel | ruflo |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 67 · Manual review | 67 · Manual review | 66 · 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 | unexpected code execution, data exfiltration, human approval gap | 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% | 65% | 67% |
| Source & provenance | |||
Provenance | pydantic/pydantic-ai | 1Panel-dev/1Panel | ruvnet/ruflo |
Category | Dev & Engineering | Agent & Tools | Agent & Tools |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | metadata-only | metadata-only |
Permission hints | repository clone, local runtime dependencies | repository clone | repository clone |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 16.2K | 35K | 31.7K |
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: genai-prices
Author: pydantic · Source status: Clear source
Calculate prices for calling LLM inference APIs.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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%
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: 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%
2026-04-08 |
2026-04-14 |
2026-04-11 |
40 |
75 |
75 |
