Content
68%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-organized, largely actionable reference guide with strong decision tables and executable optimization commands. The main gaps are a redundant questions section, batch image operations lacking validation/backup steps, and heavy inline detail that could be split into more reference files.
Suggestions
Add validation/backup guidance to the batch optimization commands (e.g., test on a copy first, verify output with a size check, or note that jpegoptim modifies files in place) to raise workflow clarity.
Remove the 'Task-Specific Questions' section, which duplicates the 'Before Starting' context-gathering questions almost verbatim.
Include one concrete generation example (e.g., a curl or SDK call for Gemini or Flux) in the AI Image Generation section so the primary workflow is executable without opening the reference file.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is information-dense (spec tables, model comparisons, copy-paste commands) and does not explain concepts Claude already knows. It is not a 5 because there is genuine redundancy: the "Task-Specific Questions" section (lines 321-328) repeats the "Before Starting" context questions (lines 17-32), and a few lines like "Every image on your site affects page speed, which affects SEO and conversions" are padding that could be trimmed. | 4 / 5 |
Actionability | Concrete, executable guidance is present throughout — bash commands (cwebp, mogrify, jpegoptim), copy-paste meta tags, exact pixel dimensions, prompt patterns, and a decision tree. It is not a 5 because the core AI-generation workflow stops at "Generate with AI — use Flux or Gemini" without a concrete API call example (deferred entirely to the reference file), leaving minor gaps in the most common path. | 4 / 5 |
Workflow Clarity | Workflows are clearly numbered and sequenced (blog hero 1-4, social 1-4, mockups 1-4, banners 1-5), but the batch optimization commands ("mogrify -format webp -quality 80 *.png", "jpegoptim --max=80 --strip-all *.jpg" — the latter modifies originals in place) run on all matching files with no backup, dry-run, or verification step. Per the rubric, missing validation in batch/destructive operations caps workflow clarity at 3. | 3 / 5 |
Progressive Disclosure | Structure is good: a well-signaled one-level-deep reference ("For detailed prompting guides per model, see references/ai-image-prompting.md") that exists in the bundle, and clearly sectioned inline content. It is not a 5 because substantial detail (the per-model comparison rows, platform size tables, optimization specifics) is inlined in SKILL.md where the anchor expects an overview with content appropriately split across references — only one reference file exists for a ~340-line body. | 4 / 5 |
Total | 15 / 20 Passed |