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, highly actionable skill body: executable commands with flags and defaults, complete JSON input schemas, and clearly signaled one-level-deep reference guides. Main weaknesses are redundancy between the workflow and usage sections, glossary-style acronym explanations, and orphaned bundle files (templates, expected output) that lack navigation links.
Suggestions
Merge 'Typical Analysis Workflow' and 'How to Use' into one section — they repeat near-identical commands for the same three scripts.
Drop or compress the acronym expansions (ROI, CTR, CPA, etc.) in the campaign_roi_calculator.py section; Claude knows these marketing metrics — the metric names alone suffice.
Link the actual template paths (e.g. `assets/ab_test_template.md`) and `assets/expected_output.json` from SKILL.md so every bundle file is navigable; 'Use the provided template' currently points nowhere.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | "**CTR**: Click-through rate", "**ROI**: Return on investment percentage" and similar acronym expansions explain concepts Claude already knows, and the 'Typical Analysis Workflow' and 'How to Use' sections duplicate largely the same commands. Mostly efficient, but several sections could be trimmed or merged — matching 'includes some unnecessary explanation or could be tightened'. Not 4 because the duplication and glossary-style padding go beyond 'minor instances'. | 3 / 5 |
Actionability | Every usage is a copy-paste-ready command with flags and defaults ("python scripts/attribution_analyzer.py campaign_data.json --model time-decay --half-life 14"), complete JSON input schemas per script, and a pre-flight validation command ("python -m json.tool your_file.json") — fully executable with common cases covered. Not 4 because there are no real gaps in executable guidance. | 5 / 5 |
Workflow Clarity | The 'Typical Analysis Workflow' gives a clearly sequenced 3-step plan with per-step purpose comments and a narrative on chaining results, plus an Input Validation section listing expected error types. Not 5 because validation is documented separately rather than woven into the workflow as an explicit checkpoint/feedback loop, and not 3 since a concrete validation step is present and these are read-only (non-destructive, non-batch) operations. | 4 / 5 |
Progressive Disclosure | Good structure against the actual bundle: a Reference Guides table signals all three one-level-deep `references/*.md` files (all verified to exist) with purpose statements, and `assets/sample_campaign_data.json` is cited for input examples. Not 5 because four bundle files (the three `assets/*_template.md` templates and `assets/expected_output.json`) are never linked from SKILL.md — 'Use the provided template' in Best Practices gives no path, leaving them undiscoverable. | 4 / 5 |
Total | 16 / 20 Passed |