Content
87%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is concise, highly actionable, and well-structured as a self-contained recipe, but the batch multi-angle generation step is missing explicit verification and error-recovery checkpoints.
Suggestions
Add an explicit verification/feedback checkpoint after the Phase B batch generation (e.g. confirm each angle preserved identity, and re-generate any that drifted before presenting the gallery).
Include a brief error-recovery loop for failed image calls (retry with `muapi predict wait` or fall back to the documented curl endpoint) to satisfy the batch-operation feedback-loop expectation.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and operational — inputs table, phased steps with concrete prompts, and brief agent notes — without explaining concepts Claude already knows; every section earns its place. | 3 / 3 |
Actionability | Provides fully executable guidance: specific `muapi image generate`/`muapi image edit` commands, concrete model IDs (nano-banana-pro, nano-banana-2-edit), copy-paste prompts, and a curl fallback, all copy-paste ready. | 3 / 3 |
Workflow Clarity | Phases are clearly sequenced with an explicit approval gate ('Present the base image to the user for approval'), but the batch generation of six angles lacks explicit output verification or error-recovery loops, which caps workflow clarity at 2 for batch operations. | 2 / 3 |
Progressive Disclosure | A single self-contained SKILL.md with well-organized sections (Inputs, Steps, Trigger Keywords, Notes) and no nested or broken references; no bundle files are present or needed, so well-organized sections suffice for a 3. | 3 / 3 |
Total | 11 / 12 Passed |