Content
75%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-structured, actionable skill body that sequences the creation workflow with explicit checkpoints and pushes detail to real reference files at one level of depth. Its main weaknesses are minor verbosity in emphasized framing and a dangling references to an examples/ directory whose files are not present in the bundle.
Suggestions
Remove or create the missing example files under examples/ (llm-judge-metric.md, narrative-metric.md, custom-code-metric.py, section-extraction-metric.py) so all referenced bundle paths resolve.
Trim redundant emphasis lines like '**This is the most critical concept in metric design**' and repeated restatements of the 'prefer llm_judge over custom_code' point to tighten the token budget.
Add explicit pass/fail or sample-size validation criteria to the deploy-and-test step so the iterate loop has a concrete stopping condition rather than 'matches expectations'.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is largely efficient and assumes Claude's competence (e.g. it does not re-explain what an LLM is), but includes some padded framing such as '**This is the most critical concept in metric design**' and repeated reinforcement of the same points across sections that could be trimmed slightly. | 4 / 5 |
Actionability | It provides concrete, executable guidance — a numbered creation workflow, a copy-paste trigger-prompt template, field-level direction ('prompt goes in the description field, NOT the prompt field'), and a baseline-metrics table — with minor gaps such as exact endpoint names being deferred to api-reference.md rather than shown inline. | 4 / 5 |
Workflow Clarity | The six-step Metric Creation Workflow is clearly sequenced and step 2 is flagged as mandatory ('Skipping steps (especially step 2) leads to metrics that miss edge cases'), and the Manual Fix First flow plus Cost Guard add validation/confirmation checkpoints; the cap-related batch-operation guidance is present, though the deploy→test→iterate loop is described rather than given explicit pass/fail validation criteria. | 4 / 5 |
Progressive Disclosure | Structure is good: SKILL.md is an overview with clearly signaled one-level-deep references to real files (prompt-patterns.md, pythonic-patterns.md, advanced-patterns.md, api-reference.md all exist), but the Additional Resources section also lists an examples/ directory with four example files that do not exist in the bundle, a navigation gap that holds it below the clear-overview anchor. | 4 / 5 |
Total | 16 / 20 Passed |