Skill audit report
VectorizedMultiAgentSimulator audit report.
VMAS is a vectorized differentiable simulator designed for efficient Multi-Agent Reinforcement Learning benchmarking. It is comprised of a vectorized 2D physics engine written in PyTorch and a set of challenging multi-robot scenarios. Additional scenarios can be implemented through a simple and modular interface.
OpenAgentSkill Trust Score
Stars, maintenance, license, docs, dependency risk, and installability.
The Trust Score is OpenAgentSkill's adoption layer. It is designed to help an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO76
577 GitHub stars
Recent maintenance
PASS100
27d since push
License clarity
PASS86
GPL-3.0
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add proroklab/VectorizedMultiAgentSimulator
Repository evidence
PASS86
https://github.com/proroklab/VectorizedMultiAgentSimulator
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add proroklab/VectorizedMultiAgentSimulator
Repository
88
https://github.com/proroklab/VectorizedMultiAgentSimulator
License
86
GPL-3.0
Maintenance
100
27d since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
90
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Adoption
88
577 GitHub stars
Warnings
- Quality score needs review
Method
This report combines public metadata, AI review output, repository freshness, install readiness, OpenAgentSkill events, quality scoring, trust checks, and the agent safety gate. It is not a full source-code security review.
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