Skill audit report
Factor Research audit report.
Advanced Quantitative Factor Research: ML-powered stock return prediction with 72% performance improvement. Features comprehensive alpha factor library, systematic feature selection, and deep learning models (LSTM+ResNet achieving IC=0.06476).
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
INFO62
406 GitHub stars
Recent maintenance
INFO62
10mo since push
License clarity
WARN42
Unknown
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 nuglifeleoji/Factor-Research
Repository evidence
PASS86
https://github.com/nuglifeleoji/Factor-Research
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add nuglifeleoji/Factor-Research
Repository
88
https://github.com/nuglifeleoji/Factor-Research
License
45
Unknown
Maintenance
62
10mo 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
68
406 GitHub stars
Warnings
- License is unclear
- Quality score needs review
- License clarity: Unknown
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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