Skill audit report
Use this to build a good evaluation dataset for an LLM app, the part everyone underestimates. Trigger on "make an eval set", "what should I test my LLM on", "I don't have test data for my prompt", "build a golden dataset", or before setting up evals. A great eval set beats a great metric; garbage-in means your evals lie to you.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
WARN48
33 GitHub stars
Stars/forks activity
WARN48
33 stars, 18 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
26d since push
License clarity
PASS86
CC0-1.0
README/SKILL.md completeness
PASS86
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 ContextJet-ai/awesome-llm-observability --skill build-eval-dataset
Install command safety
INFO68
dynamic command execution, standard package or runtime install path
Permission surface
PASS86
filesystem or document access
Repository evidence
PASS86
https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/build-eval-dataset
Review status
WARN46
AI review approval is missing
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add ContextJet-ai/awesome-llm-observability --skill build-eval-dataset
Repository
88
https://github.com/ContextJet-ai/awesome-llm-observability/tree/main/skills/build-eval-dataset
License
86
CC0-1.0
Maintenance
100
26d since push
AI review
55
Review approval is missing
README/SKILL.md completeness
86
Usable description available
Dependency risk
90
no major dependency risk hints in public metadata
Install command safety
68
dynamic command execution, standard package or runtime install path
Permission surface
86
filesystem or document access
Stars/forks activity
48
33 stars, 18 forks; issue activity unavailable in current metadata
Adoption
42
33 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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