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 lean, actionable, and well-structured with concrete commands and prompts. Its main weakness is the absence of error-recovery guidance for generation failures, which caps workflow clarity.
Suggestions
Add a brief error-recovery step for each phase, e.g. 'If generation fails, inspect the error, adjust the prompt or model, and retry before presenting to the user.'
Include a validation step after Phase A image generation (e.g. confirm the image contains the product prominently) before proceeding to animation.
Specify what to do if the user rejects the generated dynamic image at the approval checkpoint (revise prompt and regenerate) to close the feedback loop.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and focused — an inputs table, two concrete phases with inline prompts, and brief notes — with no padding or explanation of concepts Claude already knows, so every token earns its place. | 3 / 3 |
Actionability | It gives specific executable CLI commands with model IDs, exact prompts, aspect ratios, and a copy-paste-ready curl fallback with polling, rather than abstract direction. | 3 / 3 |
Workflow Clarity | The two-phase sequence is clear with an explicit user-approval checkpoint between phases, but there is no validation or error-recovery loop for generation failures, leaving a validation gap. | 2 / 3 |
Progressive Disclosure | No bundle files are needed; the single SKILL.md is well-organized into Inputs, Steps, Trigger Keywords, and Notes sections, which suffices for a focused skill under the simple-skill guideline. | 3 / 3 |
Total | 11 / 12 Passed |