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: zeroclaw
Author: zeroclaw-labs · Source status: Clear source
Fast, small, and fully autonomous AI personal assistant infrastructure, ANY OS, ANY PLATFORM — deploy anywhere, swap anything 🦀
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
NextChat
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
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
1Panel
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | NextChat | openai-python | 1Panel |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 66 · Manual review | 66 · Manual review | 67 · 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, human approval gap | unexpected code execution, data exfiltration, human approval gap | 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% | 65% | 65% |
| Source & provenance | |||
Provenance | ChatGPTNextWeb/NextChat | openai/openai-python | 1Panel-dev/1Panel |
Category | Agent & Tools | Automation & Workflows | Agent & Tools |
Freshness | |||
| Risk & permission signals | |||
Audit signals | metadata-only | metadata-only | metadata-only |
Permission hints | repository clone | repository clone | repository clone |
| Install & compatibility | |||
Supported tools | Universal | Universal | Universal |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 87.7K | 30.5K | 35K |
Repository: Open-Assistant
Author: LAION-AI · Source status: Clear source
OpenAssistant is a chat-based assistant that understands tasks, can interact with third-party systems, and retrieve information dynamically to do so.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: firecrawl
Author: firecrawl · Source status: Clear source
🔥 The Web Data API for AI - Power AI agents with clean web data
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
Repository: awesome-llm-apps
Author: Shubhamsaboo · Source status: Clear source
Collection of awesome LLM apps with AI Agents and RAG using OpenAI, Anthropic, Gemini and opensource models.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2025-12-03 |
2026-04-14 |
2026-04-14 |
75 |
75 |
75 |
