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-course
Author: mlabonne · Source status: Clear source
Course to get into Large Language Models (LLMs) with roadmaps and Colab notebooks.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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
langfuse-python
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
OMNI — All-In-One Master Skill
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
Agent Trading Bot
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | langfuse-python | OMNI — All-In-One Master Skill | Agent Trading Bot |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 66 · Manual review | 66 · Manual review | 61 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, OpenClaw |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | unexpected code execution, identity privilege abuse, data exfiltration | unexpected code execution, identity privilege abuse, data exfiltration |
Control gaps | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Secrets, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 65% | 67% | 67% |
| Source & provenance | |||
Provenance | langfuse/langfuse-python | openclaw/skills | openclaw/skills |
Category | Automation & Workflows | Data & Analytics | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | needs credentials, network access, runs shell, writes files | needs credentials, runs shell, writes files |
Permission hints | repository clone | verify source provenance before install | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, OpenClaw | OpenClaw |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 378 | 0 | 0 |
Repository: pathway
Author: pathwaycom · Source status: Clear source
Python ETL framework for stream processing, real-time analytics, LLM pipelines, and RAG.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: anything-llm
Author: Mintplex-Labs · Source status: Clear source
The all-in-one AI productivity accelerator.
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: 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%
2026-04-13 |
2026-04-02 |
2026-04-05 |
75 |
65 |
65 |
