Skill audit report
PyPOTS audit report.
A Python toolkit/library for reality-centric machine/deep learning & data mining on partially-observed time series, with 50+ SOTA neural network models for scientific analysis tasks (imputation, classification, clustering, forecasting, anomaly detection, cleaning) on incomplete industrial irregularly-sampled multivariate TS with NaN missing values
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
PASS86
2.0K GitHub stars
Recent maintenance
PASS100
6d since push
License clarity
PASS86
BSD-3-Clause
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 WenjieDu/PyPOTS
Repository evidence
PASS86
https://github.com/WenjieDu/PyPOTS
Review status
PASS88
AI review data available
Checks
Install and adoption review
Install path
92
npx skills add WenjieDu/PyPOTS
Repository
88
https://github.com/WenjieDu/PyPOTS
License
86
BSD-3-Clause
Maintenance
100
6d 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
2.0K GitHub stars
Warnings
No major warnings detected from available metadata.
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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