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
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
harmony
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Agent Sync
Execution risk：High
Threat tags：prompt injection, tool poisoning, unexpected code execution
Control gaps：missing license, broad permissions, shell without guardrails
openai-python
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | harmony | Agent Sync | openai-python |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 92 · Manual review | 84 · Manual review | 92 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Claude | Clear source, High execution risk, Universal |
Execution risk | High | High | High |
Threat tags | unexpected code execution, data exfiltration, human approval gap | prompt injection, tool poisoning, unexpected code execution | unexpected code execution, 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, shell without guardrails |
Permission summary | Permission review, Network, Command | Permission review, Network, Command | Permission review, Network, Command |
Evidence completeness | 65% | 67% | 65% |
| Source & provenance | |||
Provenance | openai/harmony | openclaw/skills | openai/openai-python |
Category | Automation & Workflows | Dev & Engineering | Automation & Workflows |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | network access, runs shell, writes files | metadata-only |
Permission hints | repository clone | target os: macos, target os: linux, verify source provenance before install | repository clone |
| Install & compatibility | |||
Supported tools | Universal | Claude, Codex, Cursor, OpenClaw, Windsurf, GitHub Copilot | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 4.3K | 0 | 30.5K |
Repository: CLIP
Author: openai · Source status: Clear source
CLIP (Contrastive Language-Image Pretraining), Predict the most relevant text snippet given an image
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: whisper
Author: openai · Source status: Clear source
Robust Speech Recognition via Large-Scale Weak Supervision
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: 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%
2026-04-09 |
2026-04-05 |
2026-04-14 |
75 |
50 |
75 |
