evaluating-code-models
evaluating-code-models is an LLM evaluation & observability skill for AI agents, published by davila7 in the claude-code-templates repository on GitHub. Skillspace rates evaluating-code-models C (78/100) and it passed the safety scan; a judge model has also reviewed it.
Evaluates code generation models across HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics. Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality. Industry standard from BigCode Project used by HuggingFace leaderboards.
- Grade
- C
- Quality
- 78 / 100
- Safety
- Passed
- GitHub stars
- 30,694
Placed #1 of 6 in the judged category final for LLM evaluation & observability.
Our two independent classifiers differ on this skill's category, so treat the placement as provisional.
This rating describes the version of the repository we crawled; it may have changed since, so go by its current files.
Our review
- Architecture
- 96 / 100
- Maturity
- Packaged: licensed, with a version or references
- Security scan
- No issues found
- License
- Not declared. Check the repository before reusing it.
Judge review
Placed #1 of 6 in the judged category final for LLM evaluation & observability.
- Trigger
85 85 - Instructions
88 88 - Specificity
92 92 - Safety
88 88 - Disclosure
65 65 - Maintenance
75 75
Grounded in a real benchmark suite and the only candidate here that explicitly sandboxes untrusted model-generated code in Docker before executing it.
Not checked yet
- Category is a classifier prediction; no model has read this skill yet
- No blind task eval yet
- Scope (portable / vendor / repo-internal) not assessed; only the top of each category is read for scope
- Sibling files (scripts, references) not fetched; security scan covers SKILL.md only
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