Content
63%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 body is an operational, well-sequenced playbook for two research modes with genuinely concrete selectors, file conventions, and validation gates, and it correctly assumes Claude's competence rather than teaching concepts. Its main weaknesses are redundancy — platform automation details duplicated between inline summaries and the referenced ref files — and the absence of any in-bundle reference files, leaving a ~300-line monolith where a lean overview plus pointers would serve better.
Suggestions
Trim the 'Chrome MCP 自动化' section (ChatGPT/Gemini selector tables, file-upload workflow, 报告提取 details) to a short summary plus pointers to the per-platform ref files, since the refs already hold the full DOM selectors and snippets — this addresses both conciseness and progressive_disclosure.
Consolidate the duplicated file-organization rules: the Mode B '文件命名规范' section repeats the '文件组织规范（铁律）' rules; state them once and have Mode B reference them.
Add explicit failure/quota handling to the Step 2 workflow (e.g., what to do when one of the three parallel research routes fails or hits its 30-day cap) so the recovery loop is as concrete as the Quality Gate.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and operational (no concept explanations Claude already knows), but there is meaningful redundancy: the ChatGPT/Gemini selector tables and the '报告提取' section duplicate content the skill itself says lives in the external platform refs ('详细 DOM 选择器和代码片段见各平台 ref'), and file-naming rules ('一个研究课题 = 一个子目录', the 常见错误 tables) appear twice across Mode A and Mode B. Mostly efficient but could be tightened, matching the 3 anchor. | 3 / 5 |
Actionability | Concrete and largely executable: exact file paths and naming patterns ('project-research/YYYY-MM-DD-{topic}/{provider}-response.md'), real DOM selectors ('[data-testid="deep-research-sidebar-item"]', '.ql-editor[contenteditable="true"]'), JS API names (DataTransfer, new File, clipboard.readText), and a copy-paste-ready Part 1 prompt template. Falls short of 5 because full automation scripts live in the external refs and the Step 2 dispatch/Q&A-append mechanics are described only at hint level. | 4 / 5 |
Workflow Clarity | Mode A has a clear Step 1–5 sequence and Mode B a clear 3-step sequence, with real checkpoints: a Quality Gate on received reports (反例覆盖、来源多样性…), codebase verification in Step 4, and Step 5 commit persistence verified via 'git log --oneline -1'. Not a 5 because some checkpoints are implicit — e.g., what to do when a provider's quota is exhausted or a parallel research run fails is only '发前确认值得用' with no recovery loop. | 4 / 5 |
Progressive Disclosure | Section structure and external references are clearly signaled ('../.cat-cafe-shared-refs/chatgpt-browser-automation.md' etc.), but the skill ships no bundle (no references/, scripts/, assets/), and ~130 lines of platform-specific automation detail (selector tables, file-upload workflow, report extraction per provider) are inlined even though dedicated ref files exist for exactly that content. That inline-vs-ref split matches the 3 anchor: some structure, but content that should be separate is inline; a 4 would require the automation summaries to be trimmed to pointers. | 3 / 5 |
Total | 14 / 20 Passed |