Content
75%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, actionable overview that leans on tables for density and defers detail to clearly labeled one-level-deep references. The main weaknesses are duplicated install/setup content, an unverified primary generation path, and no explicit output validation for batch operations.
Suggestions
Add an explicit validation step after batch generation (e.g., verify each output file exists and is non-empty before applying the SEO checklist), which would close the batch-workflow validation gap.
State the parameters of the core MCP tools (gemini_generate_image, set_aspect_ratio) or point to references/mcp-tools.md at the point of use in the pipeline, so the primary path is as executable as the fallback.
Consolidate the repeated install instructions (Prerequisites vs Error Handling table) into one location, and unify the reference root variables while fixing the missing spaces in the Reference Documentation list.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is table-driven and largely lean, but install instructions are duplicated (Prerequisites and the Error Handling table both repeat "./extensions/banana/install.sh"), and the Architecture Note and Cross-Skill Integration sections add context beyond what the workflow strictly needs. | 4 / 5 |
Actionability | Concrete, executable commands appear throughout ("magick output.png -quality 85 output.webp", the fallback generate.py invocation, presets.py list, cost_tracker.py log, ImageObject JSON and og: meta snippets), but the primary generation path defers the actual tool call parameters and Reasoning Brief construction to reference files, leaving minor gaps. | 4 / 5 |
Workflow Clarity | The 6-step pipeline has a pre-flight availability check, a post-generation SEO checklist, and an error-recovery table with a fallback command, but batch operations (batch N variations, batch.py --csv) get pre-checks (cost estimate, "Plan first", rate-limit check) without any explicit validation of outputs after batch generation. | 4 / 5 |
Progressive Disclosure | The "Reference Documentation" section is clearly signaled ("Load on-demand. Do NOT load all at startup") with nine one-level-deep references each given a one-line description, and in-text citations point to specific files; minor gaps include two different root variables (${CLAUDE_SKILL_DIR} vs ${CLAUDE_PLUGIN_ROOT}) and a few malformed list entries missing a space after the colon. | 4 / 5 |
Total | 16 / 20 Passed |