Content
78%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.
The content is lean, actionable, and well-structured with a clear multi-step workflow, explicit validation/feedback loops, and well-signaled one-level-deep references to real bundle files. The main weakness is mild structural redundancy from two overlapping numbered lists that could be consolidated.
Suggestions
Consolidate the two numbered lists under 执行步骤 (the 1-4 layer list and the 1-7 procedure list) into a single unified sequence to remove structural redundancy.
Add one short before/after example showing how a 伪对立 or 工程协议腔 sentence is rewritten, to lift actionability from procedural to demonstrably executable.
Consider moving the long canonical 公共硬约束 block to a shared reference if it is identical across skills, keeping the SKILL.md body focused on this skill's specifics.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and assumes Claude's competence — it does not explain what NSFC, AI-flavor, or basic editing concepts are — but the two overlapping numbered lists (1-4 layers then 1-7 steps) and the canonical common-constraints block could be trimmed or reorganized slightly, keeping it just below level 5. | 4 / 5 |
Actionability | Concrete procedural guidance is present: a four-layer scan, explicit decision categories (保留/改写/合并/人工确认), specific config.yaml keys, and concrete rules (伪对立把 B 放入主干; 同义递进合并), with only minor gaps such as no worked before/after example inline. | 4 / 5 |
Workflow Clarity | A clear 7-step sequence exists with explicit validation checkpoints (two self-eval rounds with distinct scopes, 原句—改写句—不变量 对照) and a feedback loop (无法证明零损失则保留原句并标记人工确认), but the dual numbered-list structure makes the overall flow slightly less crisp than level 5. | 4 / 5 |
Progressive Disclosure | The body is a concise overview that points via clearly signaled one-level-deep links to two real reference files (references/machine-patterns.md '模式与处置', references/regression-cases.md '匿名回归样例'), with detailed material appropriately split out rather than inlined. | 5 / 5 |
Total | 17 / 20 Passed |