Skill audit report

BenchMARL audit report.

BenchMARL is a library for benchmarking Multi-Agent Reinforcement Learning (MARL). BenchMARL allows to quickly compare different MARL algorithms, tasks, and models while being systematically grounded in its two core tenets: reproducibility and standardization.

REVIEWED · REVIEWSafe to tryGenerated Jun 16, 2026Heuristic metadata audit
84
Audit
85
Trust
75
Quality
97
Security
76
Maintain
92
Install

OpenAgentSkill Trust Score

85
Strong shortlist

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

INFO

76

636 GitHub stars

Recent maintenance

INFO

76

4mo since push

License clarity

PASS

86

MIT

README/SKILL.md completeness

PASS

90

Metadata includes enough usage and workflow context

Dependency risk

PASS

90

no major dependency risk hints in public metadata

Install availability

PASS

92

npx skills add facebookresearch/BenchMARL

Repository evidence

PASS

86

https://github.com/facebookresearch/BenchMARL

Review status

PASS

88

AI review data available

Checks

Install and adoption review

7 passed · 2 review

Install path

92

PASS

npx skills add facebookresearch/BenchMARL

Repository

88

PASS

https://github.com/facebookresearch/BenchMARL

License

86

PASS

MIT

Maintenance

76

CHECK

4mo since push

AI review

88

PASS

Approved with no listed issues

README/SKILL.md completeness

90

PASS

Usable description available

Dependency risk

90

PASS

no major dependency risk hints in public metadata

Adoption

88

PASS

636 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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