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
memory-journal-mcp
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
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 | memory-journal-mcp | prompt-in-context-learning |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 81 · 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 | prompt injection, tool poisoning, unexpected code execution | 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% | 71% | 68% |
| Source & provenance | |||
Provenance | redhat-community-ai-tools/UnifAI | neverinfamous/memory-journal-mcp | EgoAlpha/prompt-in-context-learning |
Category | Knowledge & RAG | Knowledge & RAG | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | runs shell | needs credentials, network access | No explicit signals |
Permission hints | repository clone, local runtime dependencies | repository clone, local runtime dependencies | 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 | 13 | 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 |
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2026-04-02 |
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