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
openai-python
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
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
| Dimension | langchain4j | openai-python | memory-journal-mcp |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 92 · Manual review | 81 · Evidence missing |
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, data exfiltration, human approval gap | 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, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 65% | 71% |
| Source & provenance | |||
Provenance | langchain4j/langchain4j | openai/openai-python | neverinfamous/memory-journal-mcp |
Category | Knowledge & RAG | Automation & Workflows | Knowledge & RAG |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | metadata-only | needs credentials, network access |
Permission hints | repository clone, local runtime dependencies | repository clone | repository clone, local runtime dependencies |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 11.5K | 30.5K | 13 |
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%
2026-04-08 |
2026-04-14 |
2026-04-06 |
40 |
75 |
55 |
