Content
72%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 content is concise and actionable with concrete CLI commands and a complete generation prompt, organized into clean sections. Its main weakness is the lack of a validation/feedback checkpoint for the batch image-generation step.
Suggestions
Add a verification step after generation (e.g., confirm both faces are clearly visible and identity matches the reference before presenting, regenerate if not) to satisfy the batch-operation feedback-loop requirement.
Tighten the Notes section by linking to a reference doc for the curl/poll fallback instead of inlining the boilerplate template.
Specify a concrete retry/error path for when `muapi predict wait` fails or the grid is malformed.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly lean with well-organized sections (Inputs, Steps, Trigger Keywords, Notes) and the long prompt is necessary content; only the Notes section has minor over-explanation (curl fallback boilerplate) that could be trimmed, fitting the 4 anchor. | 4 / 5 |
Actionability | It gives a concrete `muapi image edit` call with model id, reference image, full prompt, and aspect ratio, plus a curl fallback and poll command — mostly executable with only minor template gaps (`{...}`), matching the 4 anchor. | 4 / 5 |
Workflow Clarity | The sequence (gather input → submit plan → generate → present) is present, but this batch-style multi-pose generation has no validation or verification step that the output preserves identity or renders correctly, which per the guidelines caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | The body is under 50 lines with no external references needed and is well-organized into clearly labeled sections, which per the simple-skill exception qualifies for a 5 on progressive disclosure. | 5 / 5 |
Total | 16 / 20 Passed |