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-structured, information-dense body that mostly avoids teaching Claude things it already knows and provides genuinely current model/tooling knowledge. The main weaknesses are missing validation/feedback loops in the workflows (especially around batch optimization commands and AI-generation iteration) and some duplicated sections.
Suggestions
Add verification steps around the batch/destructive optimization commands — e.g., check output file sizes after 'mogrify -format webp'/'jpegoptim --strip-all *.jpg', and note that jpegoptim modifies files in place so originals should be backed up or the -d flag used.
Add a regenerate-and-refine feedback loop to the AI generation workflows — what to do when the output has artifacts, wrong composition, or butchered text (e.g., iterate with a refined prompt or switch models) rather than stopping at 'generate with AI'.
Remove the 'Task-Specific Questions' section, which duplicates the 'Before Starting' context-gathering questions, and de-duplicate 'Common Mistakes' entries that repeat inline guidance.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense with non-obvious, high-value content (current model landscape, exact platform dimensions, copy-paste commands), but the 'Task-Specific Questions' section duplicates 'Before Starting' almost verbatim and 'Common Mistakes' repeats points already made inline — minor over-explanation that could be trimmed, so 4 rather than 3. | 4 / 5 |
Actionability | Mostly executable guidance: real commands (cwebp, mogrify, jpegoptim), concrete prompt patterns, exact dimensions, and a model-selection decision tree. Minor gaps keep it below 5 — 'Annotate with code — Hyperframes or HTML/CSS' gives no example, and Canva/Figma workflow steps are human-in-the-loop actions the agent cannot execute. | 4 / 5 |
Workflow Clarity | Workflows are clearly sequenced with a few checkpoints (optimization checklist, 'Test at actual display size'), but the batch/destructive optimization commands (e.g., 'jpegoptim --max=80 --strip-all *.jpg' modifies files in place) have no verification step, and the AI-generation workflows lack a regenerate-if-flawed feedback loop — the batch-operation guideline caps this at 3. | 3 / 5 |
Progressive Disclosure | Good structure with clear section navigation and one well-signaled, one-level-deep reference (references/ai-image-prompting.md, verified to exist and contain the promised prompting guides). Minor gaps: the prompt-structure formula is duplicated between the body and the reference, and the long platform spec tables could be split out, so 4 rather than 5. | 4 / 5 |
Total | 15 / 20 Passed |