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: llm-app
Author: pathwaycom · Source status: Clear source
Ready-to-run cloud templates for RAG, AI pipelines, and enterprise search with live data.
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
pathway
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
semantic-kernel
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
security-ownership-map
Execution risk：High
Threat tags：data exfiltration, human approval gap
Control gaps：missing license, broad permissions, network without allowlist
| Dimension | pathway | semantic-kernel | security-ownership-map |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 92 · Manual review | 92 · Manual review | 82 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | data exfiltration, 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 | Permission review, Network, Command |
Evidence completeness | 67% | 67% | 67% |
| Source & provenance | |||
Provenance | pathwaycom/pathway | microsoft/semantic-kernel | openai/skills/tree/main/skills/.curated/security-ownership-map |
Category | Automation & Workflows | Automation & Workflows | Data & Analytics |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | metadata-only | writes files |
Permission hints | repository clone | repository clone | 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 | 63.5K | 27.7K | 7.3K |
Repository: FastGPT
Author: labring · Source status: Clear source
FastGPT is a knowledge-based platform built on the LLMs, offers a comprehensive suite of out-of-the-box capabilities such as data processing, RAG retrieval, and visual AI workflow orchestration, letting you easily devel…
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: playwright
Author: microsoft · Source status: Clear source
Playwright is a framework for Web Testing and Automation.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: litellm
Author: BerriAI · Source status: Clear source
Python SDK, Proxy Server (AI Gateway) to call 100+ LLM APIs in OpenAI (or native) format, with cost tracking, guardrails, loadbalancing and logging.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: quivr
Author: QuivrHQ · Source status: Clear source
Opiniated RAG for integrating GenAI in your apps 🧠 Focus on your product rather than the RAG.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: langextract
Author: google · Source status: Clear source
A Python library for extracting structured information from unstructured text using LLMs with precise source grounding and interactive visualization.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2026-04-14 |
2026-04-14 |
2026-02-11 |
75 |
75 |
50 |
