Conciseness

The body is command-dense and avoids re-teaching known concepts, but benchmark descriptions are duplicated (Workflow 1 Step 1 bullet list vs. the Supported Benchmarks table) and the Common Issues section duplicates references/issues.md. Anchor 3 ('mostly efficient but could be tightened') fits; the duplication is more than the 'minor instances' of anchor 4.

Actionability

Nearly all guidance is copy-paste ready (install, standard/quantized/custom-model eval, Docker, a model-comparison bash script and pandas table). Not anchor 5's 'fully executable': the MultiPL-E workflow pulls 'ghcr.io/bigcode-project/evaluation-harness-multiple' but then runs 'docker run ... evaluation-harness-multiple', which fails without the full image name.

Workflow Clarity

Workflows have clear checklists and numbered steps, but none include verification checkpoints (e.g., confirm generations_multi.json exists before the Docker eval, confirm the results JSON was written) for batch GPU runs taking hours. Per the rubric's cap, batch operations without validation cannot score above 3 even though sequences are otherwise clear.

Progressive Disclosure

The bundle contains references/benchmarks.md, references/custom-tasks.md, and references/issues.md, but the body never references any of them — they are undiscoverable — while their content is inlined instead (Supported Benchmarks table, Common Issues). This matches anchor 2 ('content that clearly belongs in separate files is inlined'); it is below anchor 3 because references are not merely unclear, they are entirely absent from the body.

Specificity

Names concrete specifics — 'HumanEval, MBPP, MultiPL-E, and 15+ benchmarks with pass@k metrics' — plus action variants (benchmark, compare, test multi-language). Minor gaps in coverage (e.g., fill-in-the-middle evaluation from the body is absent), so anchor 4 fits better than the comprehensive anchor 5 or the thinner anchor 3.

Completeness

Explicitly answers both: what ('Evaluates code generation models across ... with pass@k metrics') and when ('Use when benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality'). This matches the anchor-5 pattern of concrete what + explicit trigger phrases; the when-clause is already specific, so anchor 4's 'could be more explicit' doesn't apply.

Trigger Term Quality

'benchmarking code models, comparing coding abilities, testing multi-language support, or measuring code generation quality' gives good synonym coverage alongside benchmark names users would naturally say. A few natural terms are missing (e.g., 'code LLM evaluation', 'pass@1'), keeping it below anchor 5.

Distinctiveness Conflict Risk

Clear niche (code-generation benchmarking via named benchmarks) that would not trigger for general LLM evals, but 'benchmarking code models' overlaps slightly with closely related skills like a generic lm-evaluation-harness or EvalPlus skill — anchor 4's 'minor overlap risk with closely related skills'.