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.
SAS-v2.1 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
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
Audit score, evidence confidence, trust score, 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
agentlego
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
agentic-ai-engineering
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
claude-ide-tools
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | agentlego | agentic-ai-engineering | claude-ide-tools |
|---|---|---|---|
| SAS-v2.1 pre-install audit | |||
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 |
Evidence confidence | 65% | 68% | 67% |
| Source & provenance | |||
Provenance | InternLM/agentlego | agenticloops-ai/agentic-ai-engineering | YousifAshwal/claude-ide-tools |
Freshness | 2024-09-13 | 2026-04-07 | 2026-04-06 |
| Risk & trust | |||
Trust score | 60 | 82 | 82 |
| Community | |||
Stars | 411 | 49 | 2 |
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 · Low risk signals · Universal
Repository: HuixiangDou
Author: InternLM · Source status: Clear source
HuixiangDou: Overcoming Group Chat Scenarios with LLM-based Technical Assistance
Score basis：Clear source · Risk needs review · Universal
