Content
93%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.
The skill body is lean, executable, and well-structured, with copy-paste commands covering all three generation modes and a concrete troubleshooting table. The only notable gap is the absence of an explicit validation/recovery feedback loop for the (mildly batch) multi-image case.
Suggestions
Add a short validate-then-proceed step for multi-image composition, e.g. confirm expected input count before invoking and verify saved outputs via the printed 'MEDIA: <path>' lines.
Optionally surface the SYSTEM_TEMPLATE customization option earlier (near the examples) so users discover it without reading to the end.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and efficient with no concept-padding or restatement of what Claude already knows; each section (overview, modes, resolution, troubleshooting) earns its place. It is not lower because nothing is padded or over-explained. | 5 / 5 |
Actionability | Provides copy-paste-ready 'uv run' commands with real flags (--prompt, --input-image, --filename, --resolution) and a troubleshooting table with concrete fixes for each blocker. It is not lower because the common cases are fully executable. | 5 / 5 |
Workflow Clarity | Each generation mode has a clearly sequenced example with flags, plus retry guidance for transient errors; checkpoints are mostly present. It is not a 5 because there is no explicit validate-then-proceed feedback loop, and not a 3 because the sequence and error handling are concrete. | 4 / 5 |
Progressive Disclosure | Well-organized sections with one-level-deep references to real bundle files (scripts/generate_image.py and assets/SYSTEM_TEMPLATE), both verified to exist, and no nested reference chains. It is not lower because structure and navigation are clear. | 5 / 5 |
Total | 19 / 20 Passed |