Content
85%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: sequenced steps with validation, a realistic worked example with an error-recovery loop, an anti-patterns table, and clean progressive disclosure into two genuine reference files. The remaining issues are small: a summary list that duplicates the step sections, an undefined pixel_diff_img helper in Step 6, and mismatched diff thresholds between the helper default and the assertion.
Suggestions
Define or rename pixel_diff_img in Step 6 to match the Step 3 pixel_diff helper (or show the in-memory variant), and align the default threshold (0.001) with the asserted threshold (0.005) so copy-pasted code is consistent.
Trim the 'How to use' list to a one-line orientation (or remove it) since Steps 1-6 restate the same instructions in detail, saving tokens without losing information.
Consider adding 'snapshot' / 'visual regression' phrasing to the description so users searching with those common terms trigger the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is code-forward and assumes Claude's competence (e.g., no explanation of what PDFs or Pillow are), but the 'How to use' numbered list duplicates the detailed Step 1-6 sections that follow, and a few asides ('the workhorses for PDF to image') add little. This matches level 4 ('efficient; minor instances that could be trimmed') better than level 5's every-token-earns-its-place. | 4 / 5 |
Actionability | Concrete, executable guidance throughout: install commands, render code, a working pixel_diff implementation, a pytest test, and an update-baseline workflow. It stops short of level 5 because Step 6 calls pixel_diff_img() which is never defined (Step 3 defines path-based pixel_diff()), and the diff threshold defaults are inconsistent (0.001 in pixel_diff vs 0.005 in the assertion). | 4 / 5 |
Workflow Clarity | Steps 1-6 are clearly sequenced with an assertion checkpoint (Step 4), and the worked example demonstrates an explicit feedback loop: diff fails, diagnose with pdfinfo -list-embedded-fonts to rule out font substitution, fix, re-run until under threshold, then refresh baselines. The opt-in UPDATE_PDF_BASELINES mode guards the baseline-overwrite operation, matching the level-5 anchor. | 5 / 5 |
Progressive Disclosure | The body is a well-organized overview with two real, one-level-deep reference files (deterministic-rendering.md, cross-engine-regression.md) that are clearly signaled at point of use and summarized in a References section; verified the referenced files exist and do not nest further references. Advanced material (metadata stripping, cross-engine setups) is appropriately split out, matching the level-5 anchor. | 5 / 5 |
Total | 18 / 20 Passed |