Skill audit report
Build DSPy evaluation harnesses with rich-feedback metrics that are essential for GEPA optimization. Use when writing a metric function, calling dspy.Evaluate, splitting dev/val sets, debugging "why is my optimizer not improving?", or designing CI-ready DSPy eval suites.
The Trust Score helps an agent decide whether a skill is safe enough to shortlist before installation.
GitHub adoption
INFO62
277 GitHub stars
Stars/forks activity
WARN57
277 stars, 24 forks; issue activity unavailable in current metadata
Recent maintenance
PASS100
3d since push
License clarity
PASS86
MIT
README/SKILL.md completeness
PASS86
Metadata includes enough usage and workflow context
Dependency/runtime risk
INFO72
credential or environment access
Install availability
PASS92
npx skills add intertwine/dspy-agent-skills --skill dspy-evaluation-harness
Install command safety
PASS92
standard package or runtime install path
Permission surface
WARN60
secrets or environment access, filesystem or document access
Repository evidence
PASS86
https://github.com/intertwine/dspy-agent-skills/tree/main/skills/dspy-evaluation-harness
Review status
INFO66
AI review data available
Agent Proven outcomes
INFO54
No agent outcome data yet
Checks
Install path
92
npx skills add intertwine/dspy-agent-skills --skill dspy-evaluation-harness
Repository
88
https://github.com/intertwine/dspy-agent-skills/tree/main/skills/dspy-evaluation-harness
License
86
MIT
Maintenance
100
3d since push
AI review
55
No critical security concerns detected. The skill contains only standard DSPy evaluation code and does not include any malicious or unsafe operations.
README/SKILL.md completeness
86
Usable description available
Dependency risk
72
credential or environment access
Install command safety
92
standard package or runtime install path
Permission surface
60
secrets or environment access, filesystem or document access
Stars/forks activity
57
277 stars, 24 forks; issue activity unavailable in current metadata
Adoption
68
277 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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