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: openclaw/skills
Author: jared-goering · Source status: Clear source
Structured AI agent memory with temporal versioning, relational tracking, and semantic search.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 69%
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
neurodivergent-memory
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
Vercel
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | neurodivergent-memory | openai-python | Vercel |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 81 · Manual review | 92 · Manual review | 85 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Cursor |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, identity privilege abuse, data exfiltration |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 65% | 65% |
| Source & provenance | |||
Provenance | jmeyer1980/neurodivergent-memory | openai/openai-python | openclaw/skills |
Category | Knowledge & RAG | Automation & Workflows | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | runs shell | metadata-only | needs credentials, network access, runs shell |
Permission hints | repository clone, local runtime dependencies | repository clone | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Universal | Cursor |
Install friction | 50 | 75 | 65 |
| Community | |||
Stars | 6 | 30.5K | 0 |
Repository: serena
Author: oraios · Source status: Clear source
A powerful MCP toolkit for coding, providing semantic retrieval and editing capabilities - the IDE for your agent Topics: agent, ai, ai-coding, claude, claude-code, codex, ide, jetbrains, language-server, mcp-server, pr
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: Buddy
Author: is-leeroy-jenkins · Source status: Clear source
An AI for federal financial management designed to support Financial Analysts, Managers, and Policy Professionals.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: MCPLuceneServer
Author: mirkosertic · Source status: Clear source
MCP Lucene Server is a Model Context Protocol (MCP) server that exposes Apache Lucene's full-text search capabilities through a conversational interface.
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%
Repository: AI-QA-Retrieval-Assistant-API
Author: mvsaran · Source status: Clear source
A complete, end-to-end **Retrieval-Augmented Generation (RAG)** API server built with **Python**, **FastAPI**, **LangChain**, and **ChromaDB**.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-05 |
2026-04-14 |
2026-03-29 |
