Skill audit report
Awesome Japanese Nlp Resources Audit report.
A curated list of resources dedicated to Python libraries, LLMs, dictionaries, and corpora of NLP for Japanese. Includes a Claude Code plugin with skills to search resources.
OpenAgentSkill Trust Score
OpenAgentSkill Trust Score
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO76
977 GitHub stars
Stars/forks activity
INFO65
977 stars, 47 forks; issue activity unavailable in current metadata
Recent maintenance
PASS88
2mo since push
License clarity
PASS86
CC0-1.0
README/SKILL.md completeness
PASS90
Metadata includes enough usage and workflow context
Dependency/runtime risk
PASS90
no major dependency risk hints in public metadata
Install availability
PASS92
npx skills add taishi-i/awesome-japanese-nlp-resources
Install command safety
PASS92
standard package or runtime install path
Permission surface
PASS86
filesystem or document access
Repository evidence
PASS86
https://github.com/taishi-i/awesome-japanese-nlp-resources
Review status
PASS88
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install and adoption review
Install path
92
npx skills add taishi-i/awesome-japanese-nlp-resources
Repository
88
https://github.com/taishi-i/awesome-japanese-nlp-resources
License
86
CC0-1.0
Maintenance
88
2mo 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
Install command safety
92
standard package or runtime install path
Permission surface
86
filesystem or document access
Stars/forks activity
65
977 stars, 47 forks; issue activity unavailable in current metadata
Adoption
88
977 GitHub stars
Warnings
- Quality score needs review
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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