Content
71%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.
A well-structured, highly actionable skill with excellent progressive disclosure and a single well-used reference file. Its weaknesses are token efficiency — Claude-known explanations and rot-prone inline version/date details — and the absence of an executable API-call example for the core generation workflow.
Suggestions
Move model version numbers, deprecation dates, and pricing links into a dedicated reference file or a clearly-marked 'Deprecated / model versions' section, keeping only the decision-relevant summary inline.
Add one copy-paste-ready generation example (e.g., a curl or Python call to the Gemini or Replicate API with a sample prompt) so the core AI-generation workflow is executable end-to-end.
Trim explanations of concepts Claude already knows (what OG images are, PNG/SVG use cases, CLS basics) and add explicit verification steps to the workflows (e.g., check output file size after compression, confirm text legibility after generation).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly table-driven and dense, but includes explanations Claude already knows ("The image that appears when your URL is shared on social media", the PNG-for-transparency/SVG-scales format guide, "width and height attributes prevent layout shift (CLS)") and pervasive inline time-sensitive version/date detail (FLUX.2 [pro]/[flex]/[klein], "gpt-image-1 retires on 23 Oct 2026", Midjourney v7) that is not confined to a deprecation section — only the DALL-E note is. This is more than minor over-explanation, so it fits the "could be tightened" anchor rather than the efficient one. | 3 / 5 |
Actionability | Strong executable guidance: copy-paste commands (cwebp, mogrify, jpegoptim), ready-to-use OG meta-tag HTML, concrete prompt patterns, and exact per-platform dimension tables. Falls short of fully executable because the core action — generating an image via API — never shows an actual API call example (only links), and mockup annotation stays vague ("Hyperframes or HTML/CSS"). | 4 / 5 |
Workflow Clarity | Each asset type has a clear numbered sequence (blog hero, social graphics, mockups, banners) plus a model-selection decision tree, with some checkpoints ("Test at actual display size", "compress to <200KB"). Validation is sparse — no verify step after generation or optimization (e.g., confirm output file size or text legibility) and no error-recovery feedback loops — so it does not reach the explicit-validation anchor. | 4 / 5 |
Progressive Disclosure | The body is a well-sectioned overview that keeps exactly one clearly-signaled, one-level-deep reference — "For detailed prompting guides per model, see references/ai-image-prompting.md" — which exists in the bundle, and the prompting detail is appropriately split out of the main file. Navigation is easy and content is appropriately placed, matching the clear-overview anchor. | 5 / 5 |
Total | 16 / 20 Passed |