Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is highly actionable with executable commands and a well-structured progressive disclosure to a real reference file. It loses points for duplicated inline reference content, generic security boilerplate, and missing validation checkpoints in the batch export workflow.
Suggestions
Add an explicit validation checkpoint in Step 4 (e.g., confirm each exported PNG matches the target dimensions and list any missing variants before presenting) to lift workflow clarity.
Trim the duplicated inline size/style tables and the generic Security section, relying on the references file and Claude's existing knowledge to reduce tokens.
Consolidate the repeated ai-multimodal command examples into one parameterized template to avoid near-duplicate blocks.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient with concrete commands and tables, but it repeats the banner-size and style tables inline that already live in references, and the Security section states concepts Claude already knows, adding some unnecessary tokens. | 2 / 3 |
Actionability | Provides fully executable bash/node/python commands with real flags (model, aspect-ratio, size, output paths) and a concrete model-selection table, making the guidance copy-paste ready. | 3 / 3 |
Workflow Clarity | The five-step workflow is clearly sequenced, but batch generation and export steps lack explicit validation/checkpoint steps (e.g., verifying the screenshot dimensions or that all variants exported), which per the guidelines caps batch-operation workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | The SKILL.md is an overview that signals one-level-deep references to a real references/banner-sizes-and-styles.md file, with size and style summaries inline plus 'Full reference' pointers, giving clear navigation without deep nesting. | 3 / 3 |
Total | 10 / 12 Passed |