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
UnifAI
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
Ultramemory
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
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
| Dimension | UnifAI | Ultramemory | prompt-in-context-learning |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 85 · Evidence missing | 79 · 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, memory context poisoning | unexpected code execution, identity privilege abuse, data exfiltration | 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, Secrets, Command | Permission review, Network, Command |
Evidence completeness | 69% | 69% | 68% |
| Source & provenance | |||
Provenance | redhat-community-ai-tools/UnifAI | openclaw/skills | EgoAlpha/prompt-in-context-learning |
Category | Knowledge & RAG | Knowledge & RAG | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | runs shell | needs credentials, network access, runs shell, writes files | No explicit signals |
Permission hints | repository clone, local runtime dependencies | verify source provenance before install | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 35 | 0 | 2.1K |
Repository: scholar-rag
Author: PangHu1020 · Source status: Clear source
A beginner-friendly and extensible Agentic RAG project that demonstrates the full pipeline of document parsing, retrieval, reranking, workflow orchestration, tool calling, and answer generation, designed for both learnin
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: haystack
Author: deepset-ai · Source status: Clear source
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
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: rag_demos
Author: dappros · Source status: Clear source
Examples of RAG (Retrieval-Augmented Generation) with Ethora, LangChain, and OpenAI.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-06 |
2026-03-30 |
2026-04-02 |
50 |
65 |
40 |
