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%
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
ELM-Python-Client
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
Full run checklist.md tasks in Claude Code skill
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | ELM-Python-Client | security-ownership-map | Full run checklist.md tasks in Claude Code skill |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 67 · Manual review | 70 · Manual review | 64 · 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, human approval gap | 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, network without allowlist | missing license, broad permissions, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Secrets, Command |
| Source & provenance | |||
Provenance | IBM/ELM-Python-Client | openai/skills/tree/main/skills/.curated/security-ownership-map | openclaw/skills |
Category | Automation & Workflows | Data & Analytics | Operations & Infra |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | writes files | needs credentials, network access, runs shell, writes files |
Permission hints | repository clone | registry access, remote metadata pull, runtime dependencies may be required | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, Cursor, Universal | Claude |
Install method | script-backed | registry-install | script-backed |
Install friction | |||
| Community | |||
Stars | 46 | 7.3K | 0 |
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: 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%
Repository: claude-code-router
Author: musistudio · Source status: Clear source
Use Claude Code as the foundation for coding infrastructure, allowing you to decide how to interact with the model while enjoying updates from Anthropic.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: new-api
Author: QuantumNous · Source status: Clear source
A unified AI model hub for aggregation & distribution.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: dapr
Author: dapr · Source status: Clear source
Dapr is a portable runtime for building distributed applications across cloud and edge, combining event-driven architecture with workflow orchestration.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
2026-03-03 |
2026-02-11 |
2026-03-31 |
75 |
50 |
65 |
