Skill audit report
Python package for AutoML on Tabular Data with Feature Engineering, Hyper-Parameters Tuning, Explanations and Automatic Documentation
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
PASS86
3.3K GitHub stars
Stars/forks activity
PASS83
3.3K stars, 446 forks; issue activity unavailable in current metadata
Recent maintenance
INFO76
3mo since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency/runtime risk
PASS82
network or browser surface
Install availability
PASS92
npx skills add mljar/mljar-supervised
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/mljar/mljar-supervised
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add mljar/mljar-supervised
Repository
88
https://github.com/mljar/mljar-supervised
License
86
MIT
Maintenance
76
3mo since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
90
Usable description available
Dependency risk
82
network or browser surface
Install command safety
92
standard package or runtime install path
Permission surface
72
filesystem or document access, network or browser access
Stars/forks activity
83
3.3K stars, 446 forks; issue activity unavailable in current metadata
Adoption
88
3.3K 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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