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: spring-ai-playground
Author: JM-Lab · Source status: Clear source
A self-hosted web UI that simplifies AI experimentation and testing for Java developers.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
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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
langchain4j
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
llm-app
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
pathway
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | langchain4j | llm-app | pathway |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 66 · Manual review | 65 · Manual review | 65 · Manual review |
| Source & provenance | |||
Provenance | langchain4j/langchain4j | pathwaycom/llm-app | pathwaycom/pathway |
Category | Knowledge & RAG | Automation & Workflows | Automation & Workflows |
Freshness | 2026-04-08 | 2026-01-07 | 2026-04-14 |
| 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 | |||
Install friction | 40 | 75 | 75 |
| Community | |||
Stars | 11.5K | 60K | 63.5K |
Repository: openpencil
Author: ZSeven-W · Source status: Clear source
The world's first open-source AI-native vector design tool and the first to feature concurrent Agent Teams.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
Repository: claude-code-templates
Author: davila7 · Source status: Clear source
Memory is the cornerstone of intelligent agents.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 65%
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%
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: 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%
