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: haystack
Author: deepset-ai · Source status: Clear source
Open-source AI orchestration framework for building context-engineered, production-ready LLM applications.
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
neuron-ai
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
openpencil
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
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
| Dimension | neuron-ai | openpencil | create-an-asset |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 79 · Manual review | 80 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | prompt injection, tool poisoning, unexpected code execution | data exfiltration, memory context poisoning, human approval gap |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, network without allowlist |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Network, Command |
Evidence completeness | 67% | 68% | 67% |
| Source & provenance | |||
Provenance | neuron-core/neuron-ai | ZSeven-W/openpencil | anthropics/knowledge-work-plugins/tree/main/sales/skills/create-an-asset |
Freshness | 2026-04-03 | 2026-04-05 | 2026-02-04 |
| Risk & permission signals | |||
Permission hints | repository clone, local runtime dependencies | repository clone, local runtime dependencies | registry access, remote metadata pull, runtime dependencies may be required |
| Install & compatibility | |||
Supported tools | Universal | Universal | Claude, Codex, Cursor, Universal |
Install method | script-backed | script-backed | registry-install |
Install friction | |||
| Community | |||
Stars | 1.8K | 2K | 6.6K |
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: generative-ai-for-beginners-project-based-guide-to-building-rag-agents
Author: hereandnowai · Source status: Clear source
A beginner-friendly, project-driven tutorial on generative AI and LangChain agents.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 68%
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: openclaw/skills
Author: moltbotmolty-del · Source status: Clear source
Your agent forgets everything after compaction?
Score basis：Clear source · High execution risk · OpenClaw · 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%
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