Skill audit report
Python statistical modeling: regression (OLS, WLS, GLM), discrete (Logit, Poisson, NegBin), time series (ARIMA, SARIMAX, VAR), with rigorous inference, diagnostics, and hypothesis tests. Use scikit-learn for ML; statistical-analysis for test choice.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
359 GitHub stars
Stars/forks activity
WARN57
359 stars, 35 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
10d since push
License clarity
PASS86
BSD-3-Clause
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
WARN54
command execution surface, external package install surface
Install availability
PASS92
npx skills add jaechang-hits/SciAgent-Skills --skill statsmodels-statistical-modeling
Install command safety
PASS92
standard package or runtime install path
Permission surface
INFO62
shell or command execution, network or browser access
Repository evidence
PASS86
https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/biostatistics/statsmodels-statistical-modeling
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add jaechang-hits/SciAgent-Skills --skill statsmodels-statistical-modeling
Repository
88
https://github.com/jaechang-hits/SciAgent-Skills/tree/main/skills/biostatistics/statsmodels-statistical-modeling
License
86
BSD-3-Clause
Maintenance
100
10d since push
AI review
88
Approved with no listed issues
README/SKILL.md completeness
86
Usable description available
Dependency risk
54
command execution surface, external package install surface
Install command safety
92
standard package or runtime install path
Permission surface
62
shell or command execution, network or browser access
Stars/forks activity
57
359 stars, 35 forks; issue activity unavailable in current metadata
Adoption
68
359 GitHub stars
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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