Content
60%Scale 1-5Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
This is a comprehensive and well-structured skill that covers a complex dual-mode image generation workflow with good decision trees, validation steps, and clear references. Its main weakness is significant verbosity and repetition — the same rules about not silently switching to CLI fallback and asking before using gpt-image-1.5 are stated 4-5 times, and the transparent-image workflow is described in multiple overlapping sections. The content would benefit substantially from deduplication and moving CLI-specific details (size tables, environment setup, dependency installation) into the referenced files.
Suggestions
Deduplicate the repeated CLI fallback switching rules — state the 'never silently switch, always ask' rule once in the top-level rules section and reference it elsewhere instead of restating it in the workflow, transparent image section, and prompting best practices.
Move the gpt-image-2 size table, quality guidance, and CLI environment/dependency setup into references/cli.md or references/image-api.md since these are fallback-only details that inflate the main skill unnecessarily.
Consolidate the transparent-image guidance: the rules section, the dedicated 'Transparent image requests' section, and the workflow steps all describe the same chroma-key-then-ask flow — merge into one authoritative section and cross-reference it.
Consider trimming the use-case taxonomy to just the slug list without descriptions, since Claude can infer what 'product-mockup' or 'lighting-weather' means from context.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The skill is extremely verbose at ~350+ lines with significant repetition. The transparent-image workflow is explained in detail in the rules section, then again in its own dedicated section, then referenced again in the workflow steps, and again in prompting best practices. The CLI fallback caveats ('ask before switching', 'never silently switch') are repeated in at least 4-5 places. Many rules explain things Claude already knows (e.g., what product mockups are, what 'edit' vs 'generate' means). The gpt-image-2 size table and quality guidance could be in a reference file. | 2 / 5 |
Actionability | The skill provides concrete, executable commands (the chroma-key removal script invocation with specific flags, uv pip install commands, CLI subcommands) and structured prompt templates with examples. However, the built-in `image_gen` tool usage lacks concrete code/command examples since it's a built-in tool, and some guidance remains procedural description rather than copy-paste ready. The prompt schema and examples are well-structured and actionable. | 4 / 5 |
Workflow Clarity | The 18-step workflow is clearly sequenced with decision points (mode selection, intent classification, execution strategy) and includes validation steps (step 12: inspect outputs, step 5 in transparent workflow: validate alpha channel). However, the workflow is somewhat buried in the lengthy document and the sheer number of steps (18) with interleaved conditional logic makes it harder to follow. The transparent image sub-workflow has good validation with retry logic (edge-contract fallback). | 4 / 5 |
Progressive Disclosure | The skill has a clear reference map at the bottom pointing to well-organized reference files (prompting.md, cli.md, image-api.md, sample-prompts.md, codex-network.md) with one-level-deep references. Content is reasonably split between shared and fallback-only concerns. However, the main SKILL.md itself is too long — the gpt-image-2 size tables, CLI environment setup, and detailed taxonomy could be moved to reference files. The reference map is well-signaled with clear labels for which mode each file serves. | 4 / 5 |
Total | 14 / 20 Passed |