Content
85%Weight 40%Scale 1-3Reviews 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 body with a sequenced workflow, validation feedback loops, and clean one-level-deep references into real bundle files. The only weakness is some conceptual/theoretical prose that pads the token budget.
Suggestions
Trim the '地基:参考系理论' section and inline thesis sentences to a few lines — Claude does not need the theory of frames explained to execute the steps.
Consolidate the duplicated bottleneck/value-capture signal lists (in body, '必过的检查', and research.md) into one authoritative place to cut tokens.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient and task-specific, but the foundational 'reference frame theory' section and several full-sentence theses add conceptual framing Claude does not need, padding the token budget. | 2 / 3 |
Actionability | Provides concrete executable commands (gen_illustration.py invocation with flags, capture.js render line with dimensions), a full template variable table, and a worked example frame — copy-paste ready. | 3 / 3 |
Workflow Clarity | A clearly numbered 10-step flow with explicit validation checkpoints (step 10 'Read 成品 PNG 亲验 → 不行调 frame 重生') and a dedicated '必过的检查' checklist with feedback loops for batch/destructive operations. | 3 / 3 |
Progressive Disclosure | Overview points cleanly to one-level-deep, real bundle files (references/research.md, visual.md, example.md; assets/gen_illustration.py, map_template.html), each clearly signaled with what it covers and when to read it. | 3 / 3 |
Total | 11 / 12 Passed |