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: cymbal-air-toolbox-demo
Author: GoogleCloudPlatform · Source status: Clear source
Demo of a customer service agent (Cymbal Air) using LangGraph, Tools, and RAG to interact with Google Cloud Databases via MCP Toolbox.
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
prompt-in-context-learning
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
sim
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Aliyun Qwen Rerank
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, shell without guardrails, network without allowlist
| Dimension | prompt-in-context-learning | sim | Aliyun Qwen Rerank |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 60 · Manual review | 66 · 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 | prompt injection, tool poisoning, unexpected code execution | 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, shell without guardrails, network without allowlist |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Command |
Evidence completeness | 68% | 65% | 65% |
| Source & provenance | |||
Provenance | EgoAlpha/prompt-in-context-learning | simstudioai/sim | openclaw/skills |
Category | Knowledge & RAG | Agent & Tools | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | metadata-only | network access, runs shell |
Permission hints | repository clone, local runtime dependencies | repository clone | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 2.1K | 27.7K | 0 |
Repository: UnifAI
Author: redhat-community-ai-tools · Source status: Clear source
Production-grade multi-agent orchestration engine.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
Repository: OpenViking
Author: volcengine · Source status: Clear source
OpenViking is an open-source context database designed specifically for AI Agents(such as openclaw).
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
Repository: awesome-LLM-resources
Author: WangRongsheng · Source status: Clear source
🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: neuron-ai
Author: neuron-core · Source status: Clear source
The PHP Agentic Framework to build production-ready AI driven applications.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: generative-ai-for-beginners-project-based-guide-to-building-rag-agents
Author: hereandnowai · Source status: Clear source
A beginner-friendly, project-driven tutorial on generative AI and LangChain agents.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
2026-04-02 |
2026-04-14 |
2026-04-02 |
40 |
75 |
50 |
