Content
81%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.
Highly actionable and well-sequenced: every mode has copy-paste commands, validation checkpoints, and troubleshooting loops, with real one-level-deep references throughout. The main weakness is conciseness — inline benchmark dates/versions, standard-knowledge image-tool recipes, and overlong table cells inflate the body beyond an efficient overview.
Suggestions
Move the benchmark provenance (per-model cost/time figures, the 2026-09-28 and 2026-10-07 sample dates, and the non-benchmarked-model caveats) fully into references/model-benchmarks.md and keep only a pointer plus default-model guidance in the Model Selection table.
Trim the Common Post-Processing code examples to the non-obvious ones (maskable-icon safe-zone padding) and drop recipes Claude already knows, such as basic magick/sips resize and WebP conversion.
Relocate the full --analyze-video JSON schema and composite parameter table into their existing reference files, leaving a compact example and a pointer in SKILL.md to shrink the body toward an overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient tables and commands, but it embeds time-sensitive benchmark data inline ("one sample per model on 2026-09-28", "2026-10-07", version-pinned model names) rather than confining it to a reference or deprecated section, re-teaches standard image-tool usage Claude already knows (magick/sips resize recipes), and several parameter-table cells carry long parenthetical digressions — more than the 'minor instances' of anchor 4. | 3 / 5 |
Actionability | Fully executable copy-paste bash invocations for every mode (generation, transparent, reference editing, --analyze, --analyze-video with presets, contact-sheet/verify, composite init/validate/generate), exact JSON output envelopes, complete parameter tables, and cause→fix troubleshooting entries cover the common cases. | 5 / 5 |
Workflow Clarity | Routing checks at the top correctly divert analysis requests, Steps 1–5 are clearly sequenced with explicit validation ("Confirm it shows 'PNG image data'", "Always run --validate before generating"), and the Common Issues section plus composite-vs-AI decision rule provide feedback loops for error recovery. | 5 / 5 |
Progressive Disclosure | All nine referenced files exist, are one level deep, and are clearly signaled with their purpose (e.g. "For per-model output format … read references/model-benchmarks.md"). However the ~500-line body still inlines substantial detail (full video-analysis JSON schema, composite parameter tables, image-tool recipes) where the overview rationale calls for SKILL.md to point at reference material. | 4 / 5 |
Total | 17 / 20 Passed |