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: 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%
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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
llm-app
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, memory context poisoning
Control gaps：missing license, broad permissions, shell without guardrails
docker-selenium
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Binance P2p
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | llm-app | docker-selenium | Binance P2p |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 65 · Manual review | 65 · Manual review | 64 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, memory context poisoning | unexpected code execution, data exfiltration, human approval gap | 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, Command | Permission review, Network, Secrets, Command |
Evidence completeness | 67% | 67% | 67% |
| Source & provenance | |||
Provenance | pathwaycom/llm-app | SeleniumHQ/docker-selenium | openclaw/skills |
Category | Automation & Workflows | Operations & Infra | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | No explicit signals | needs credentials, network access, writes files |
Permission hints | repository clone | repository clone, local runtime dependencies | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 60K | 8.6K | 0 |
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%
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: 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%
2026-01-07 |
2026-04-05 |
2026-04-01 |
75 |
40 |
55 |
