Skill audit report
Machine learning games. Use combination of genetic algorithms and neural networks to control the behaviour of in-game objects.
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
285 GitHub stars
Stars/forks activity
WARN57
285 stars, 31 forks; issue activity unavailable in current metadata
Recent maintenance
FAIL22
7y since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency/runtime risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add TomaszRewak/ML-games
Install command safety
PASS92
standard package or runtime install path
Permission surface
INFO72
filesystem or document access, network or browser access
Repository evidence
PASS86
https://github.com/TomaszRewak/ML-games
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add TomaszRewak/ML-games
Repository
88
https://github.com/TomaszRewak/ML-games
License
86
MIT
Maintenance
20
7y 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
Install command safety
92
standard package or runtime install path
Permission surface
72
filesystem or document access, network or browser access
Stars/forks activity
57
285 stars, 31 forks; issue activity unavailable in current metadata
Adoption
68
285 GitHub stars
Financial decision safety
58
Research-only use: do not treat output as financial advice or execute a position without human approval.
Warnings
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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