Skill audit report
Deep Reinforcement Stock Trading audit report.
A light-weight deep reinforcement learning framework for portfolio management. This project explores the possibility of applying deep reinforcement learning algorithms to stock trading in a highly modular and scalable framework.
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
692 GitHub stars
Recent maintenance
FAIL38
2y 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 Albert-Z-Guo/Deep-Reinforcement-Stock-Trading
Repository evidence
PASS86
https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add Albert-Z-Guo/Deep-Reinforcement-Stock-Trading
Repository
88
https://github.com/Albert-Z-Guo/Deep-Reinforcement-Stock-Trading
License
86
GPL-3.0
Maintenance
38
2y 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
692 GitHub stars
Warnings
- Repository appears stale
- Repository looks stale
- Quality score needs review
- Recent maintenance: 2y since push
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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