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.
An exceptionally actionable, well-structured skill body: every command is copy-paste ready, workflows carry explicit validation checkpoints, and detail is pushed into real, one-level-deep reference files with a routing table. The main weakness is token weight in the main body — dated benchmark figures, the inline video-model roster, and paragraph-length table cells duplicate material that references/model-benchmarks.md already exists to hold.
Suggestions
Move the measured cost/time figures and the 2026-09-28 benchmark date out of the Model Selection table into references/model-benchmarks.md (already cited there), keeping only a one-line cost tier hint (e.g. cheapest ≈ recraft-flash, default ≈ $0.07) in the main body.
Replace the inline list of all 15 video-model keywords with the three presets plus "run --list-models for the full keyword list and pricing", letting --list-models carry the roster.
Trim the paragraph-length cells in the Parameters table (e.g. --output and --image-size) to one line each and move the format-conversion and 0.5K-preview caveats into references/model-benchmarks.md or a short Notes subsection.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is operational rather than pedagogical (no explanations of concepts Claude already knows), but it carries time-sensitive detail inline — the 16-row model table with "real OpenRouter charges… one sample per model on 2026-09-28", the full 15-keyword video model list, and prose-heavy table cells (the --output and --image-size rows each embed a paragraph of caveats). Matches anchor 3 ("mostly efficient but includes some unnecessary explanation or could be tightened"); not 4 because dated benchmark data sits in the main body instead of the already-existing references/model-benchmarks.md or a deprecated section, which the rubric explicitly penalizes. | 3 / 5 |
Actionability | Fully executable throughout: copy-paste-ready `uv run python …/generate-image.py` invocations for every mode (generation, transparent, reference-edit, analyze, analyze-video, contact-sheet, costs), complete parameter tables with defaults and per-model limits, JSON output envelope examples, and cause/fix troubleshooting entries. Clearly anchor 5. | 5 / 5 |
Workflow Clarity | Clear sequenced workflow: routing checks gate the three modes before any steps run, Steps 1–5 include prompt setup, optional enhancement, generation, cleanup ("rm -f …/tmp/prompt.txt"), and an explicit verify step ("file OUTPUT_PATH… Confirm it shows 'PNG image data'"), with Common Issues as the error-recovery loop. Composite mode mandates "Always run --validate before generating" with exit codes 0/2 — a real validation checkpoint for the batch operation. Anchor 5. | 5 / 5 |
Progressive Disclosure | Nine one-level-deep reference files (prompt-core, prompt-platforms, prompt-categories, consistency-presets, analyze-reference, model-benchmarks, composite-reference, setup-guide, api-reference), all verified to exist on disk, each cited in the body with its purpose — plus a category-detection table that routes requests to a specific file and section. References are never nested and navigation is easy. Anchor 5. | 5 / 5 |
Total | 18 / 20 Passed |