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
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
Chemistry & Sparks. 化学反应。Química.
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
skill-seekers
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 | Chemistry & Sparks. 化学反应。Química. | skill-seekers |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 79 · Manual review | 85 · Manual review | 88 · Evidence missing |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, Claude |
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 | 67% | 67% | 71% |
| Source & provenance | |||
Provenance | IBM/ELM-Python-Client | openclaw/skills | openclaw/skills |
Category | Automation & Workflows | Integrations & Connectors | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | needs credentials, network access, runs shell, writes files | needs credentials, network access, runs shell, writes files |
Permission hints | repository clone | verify source provenance before install | verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Claude, OpenClaw | Claude, Cursor, Windsurf |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 46 | 0 | 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 |
2026-04-01 |
2026-04-04 |
75 |
65 |
65 |
