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: theagentacademy · Source status: Clear source
Multi-worker orchestration for Claude Code with Notion visibility
Score basis：Clear source · High execution risk · Claude · 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
Notex Skills
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
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
Binance Meme Rush
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | Notex Skills | openai-python | Binance Meme Rush |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 85 · Manual review | 92 · Manual review | 85 · Manual review |
Threat tags | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, identity privilege abuse, data exfiltration |
Permission summary | Permission review, Network, Secrets, Command | Permission review, Network, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 65% | 65% |
| Source & provenance | |||
Provenance | openclaw/skills | openai/openai-python | openclaw/skills |
Category | Integrations & Connectors | Automation & Workflows | Web & Automation |
Freshness | |||
| Risk & permission signals | |||
Audit signals | needs credentials, network access, runs shell, writes files | metadata-only | needs credentials, network access, runs shell |
Permission hints | verify source provenance before install | repository clone | verify source provenance before install |
| Install & compatibility | |||
Install friction | 65 | 75 | 65 |
| Community | |||
Stars | 0 | 30.5K | 0 |
Repository: openclaw/skills
Author: twinsgeeks · Source status: Clear source
Chemistry between AI agents — find chemistry through personality matching, chemistry scoring, and chemistry-driven connections.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 67%
Repository: openclaw/skills
Author: fdenny11gg · Source status: Clear source
统一智能搜索技能，整合多个搜索引擎（百炼 MCP、Tavily、Serper、Exa、Firecrawl），自动选择最佳引擎并融合结果
Score basis：Clear source · High execution risk · OpenClaw · Evidence completeness 69%
Repository: openclaw/skills
Author: twinsgeeks · Source status: Clear source
Icebreaker prompts for AI agents — break the ice with personality-matched agents, icebreaker conversations, and icebreaker openers that actually work.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 69%
2026-03-28 |
2026-04-14 |
2026-04-01 |
