Content
67%Weight 40%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A well-organized instructional body with genuinely actionable specifics (thresholds, sample sizes, stratification, a 9-step sequence) and a clearly signaled one-level-deep reference file. The main costs are cross-section redundancy that inflates token use and a 627-line scripts/evaluator.py in the bundle that SKILL.md never points to.
Suggestions
Collapse the redundancy between Core Concepts, Detailed Topics, Guidelines, and Gotchas — the multi-dimensional-rubric point is stated four times; state it once authoritatively and let Guidelines reference rather than restate.
Reference scripts/evaluator.py from the body with a 'Read when' pointer (as done for metrics.md) so the largest bundle artifact is discoverable, and either link or remove the placeholder 'External resources' entries.
Make the code examples executable by either defining or importing the helper functions they call (load_rubric, assess_dimension, run_validate_repo), or explicitly justify them as illustrative scaffolds.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is information-dense and directive rather than padded with concepts Claude already knows, but it repeats its own points across sections: "multi-dimensional rubrics instead of single scores" appears in Core Concepts, Detailed Topics, Guidelines #1, and Gotcha #5, and the 8-item Guidelines section largely recapitulates earlier sections. This matches 'mostly efficient but includes some unnecessary explanation or could be tightened' — below 4 because the Core Concepts / Detailed Topics / Guidelines overlap is a systematic redundancy, not just minor trimming. | 3 / 5 |
Actionability | Concrete, specific guidance throughout: numeric thresholds (0.7 general / 0.9 high-stakes pass, 0.85/0.70 alert lines), sample sizes (20-30 early, 50+ mature), a 9-step build sequence, a 4-level complexity stratification, and four worked examples in Python and YAML. It stops short of 5 because the code examples call undefined helpers (load_rubric, assess_dimension, run_validate_repo) — scaffolding rather than copy-paste executable — fitting 'mostly executable guidance; concrete code or commands with minor gaps'. | 4 / 5 |
Workflow Clarity | The "Building Evaluation Frameworks" sequence gives a clearly ordered 9 steps with an explicit checkpoint ('Establish baseline metrics before making changes so improvements can be measured against a known reference') and a validation-separation step (deterministic failures before LLM scores). It is not a 5 because the sequence itself lacks explicit error-recovery feedback loops (what to do when a baseline regresses or a stratum underperforms), matching 'clear sequence with most checkpoints present; minor validation gaps'. | 4 / 5 |
Progressive Disclosure | Good structure with well-signaled, one-level-deep references — the body links "[Metrics Reference](./references/metrics.md) - Read when: designing specific evaluation metrics..." and that file exists and matches its described content. Against the actual bundle structure, though, scripts/evaluator.py (627 lines) is never referenced from SKILL.md, leaving a substantial bundle artifact undiscoverable, and the 'External resources' entries are unlinked placeholders — minor organization gaps that hold this below a 5. | 4 / 5 |
Total | 15 / 20 Passed |