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: innoshop
Author: innocommerce · Source status: Clear source
InnoShop is an AI-powered open source e-commerce system built on Laravel 12, designed for global commerce.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
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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
fossology
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：broad permissions, shell without guardrails, network without allowlist
openai-python
Execution risk：High
Threat tags：unexpected code execution, data exfiltration, human approval gap
Control gaps：missing license, broad permissions, shell without guardrails
Graveyard Protocol CLI
Execution risk：High
Threat tags：unexpected code execution, identity privilege abuse, data exfiltration
Control gaps：missing license, broad permissions, shell without guardrails
| Dimension | fossology | openai-python | Graveyard Protocol CLI |
|---|---|---|---|
| Pre-install decision | |||
Pre-install score | 67 · Manual review | 66 · Manual review | 60 · Manual review |
Score basis | Clear source, High execution risk, Universal | Clear source, High execution risk, Universal | Clear source, High execution risk, OpenClaw |
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, identity privilege abuse, data exfiltration |
Control gaps | broad permissions, shell without guardrails, network without allowlist | 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% | 65% | 67% |
| Source & provenance | |||
Provenance | fossology/fossology | openai/openai-python | openclaw/skills |
Category | Operations & Infra | Automation & Workflows | Dev & Engineering |
Freshness | |||
| Risk & permission signals | |||
Audit signals | network access, runs shell | metadata-only | needs credentials, network access, runs shell, writes files |
Permission hints | repository clone, local runtime dependencies | repository clone | requires binary: node, requires binary: npx, requires binary: gp-cli, verify source provenance before install |
| Install & compatibility | |||
Supported tools | Universal | Universal | OpenClaw |
Install method | script-backed | script-backed | script-backed |
Install friction | |||
| Community | |||
Stars | 980 | 30.5K | 0 |
Repository: Stream-Daemon
Author: ChiefGyk3D · Source status: Clear source
Stream Daemon is an open-source automation tool for live streamers and creators.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: Multi-Agent-Custom-Automation-Engine-Solution-Accelerator
Author: microsoft · Source status: Clear source
The Multi-Agent Custom Automation Engine Solution Accelerator is an AI-driven system that manages a group of AI agents to accomplish tasks based on user input.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 65%
Repository: project-nova
Author: PradeepaRW · Source status: Clear source
A multi-agent AI architecture that connects 25+ specialized agents through n8n and MCP servers.
Score basis：Clear source · High execution risk · Universal · Evidence completeness 67%
2026-04-06 |
2026-04-14 |
2026-03-31 |
50 |
75 |
65 |
