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: 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%
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
serena
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
create-an-asset
Execution risk：High
Threat tags：data exfiltration, memory context poisoning, human approval gap
Control gaps：missing license, broad permissions, network without allowlist
security-ownership-map
Execution risk：High
Threat tags：data exfiltration, human approval gap
Control gaps：missing license, broad permissions, network without allowlist
| Dimension | serena | create-an-asset | security-ownership-map |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 80 · Manual review | 82 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, Claude |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | data exfiltration, memory context poisoning, human approval gap | data exfiltration, human approval gap |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, network without allowlist | missing license, broad permissions, network without allowlist |
Permission summary | Permission review, Network, Command | Network, Command | Permission review, Network, Command |
| Source & provenance | |||
Provenance | oraios/serena | anthropics/knowledge-work-plugins/tree/main/sales/skills/create-an-asset | openai/skills/tree/main/skills/.curated/security-ownership-map |
Category | Knowledge & RAG | Knowledge & RAG | Data & Analytics |
Freshness | |||
| Risk & permission signals | |||
Audit signals | No explicit signals | No explicit signals | writes files |
Permission hints | repository clone, local runtime dependencies | registry access, remote metadata pull, runtime dependencies may be required | registry access, remote metadata pull, runtime dependencies may be required |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, Cursor, Universal | Claude, Codex, Cursor, Universal |
Install method | script-backed | registry-install | registry-install |
Install friction | |||
| Community | |||
Stars | 22.6K | 6.6K | 7.3K |
Repository: openclaw/skills
Author: cinience · Source status: Clear source
Use when working with OpenSearch vector search edition via the Python SDK (ha3engine) to push documents and run HA/SQL searches.
Score basis：Clear source · High execution risk · Claude · Evidence completeness 69%
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: awesome-LLM-resources
Author: WangRongsheng · Source status: Clear source
🧑🚀 全世界最好的LLM资料总结（多模态生成、Agent、辅助编程、AI审稿、数据处理、模型训练、模型推理、o1 模型、MCP、小语言模型、视觉语言模型） | Summary of the world's best LLM resources.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: prompt-in-context-learning
Author: EgoAlpha · Source status: Clear source
Awesome resources for in-context learning and prompt engineering: Mastery of the LLMs such as ChatGPT, GPT-3, and FlanT5, with up-to-date and cutting-edge updates.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
Repository: neurodivergent-memory
Author: jmeyer1980 · Source status: Clear source
A TypeScript-based MCP server implementing a memory system inspired by neurodivergent cognitive styles.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-08 |
2026-02-04 |
2026-02-11 |
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