Content
56%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 presents a genuinely clear, well-sequenced iterative research workflow with explicit branching, state management, and a worked example. It is weakened by marketing-style padding that adds tokens without adding instruction, missing prompt templates for the LLM steps, and a monolithic single-file layout with no progressive disclosure.
Suggestions
Cut or collapse the redundant sections (技术特点/智能特性, 最佳实践, 性能优化) — they restate the description or give generic advice Claude already knows; keep only the operational notes (parameter restriction, no-script caveat).
Add the concrete prompt templates for the LLM analysis step (the exact instruction that yields the `nextSearchTopic`/`shouldContinue` JSON) and show how a search result is appended to `findings`, so the loop is reproducible without guesswork.
Move the worked example and troubleshooting content into reference files (e.g. references/example.md, references/troubleshooting.md) linked from a lean SKILL.md overview, and add an explicit validation checkpoint for report accuracy/citation before final output.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The core workflow sections (工作流程, 变量说明, 网页搜索集成) are reasonably tight, but a substantial share of the body is padding that restates the description or offers advice Claude already knows — "技术特点" (自适应搜索/避免重复/深度推理), "最佳实践" ("渐进式研究", "多源验证"), "性能优化" ("缓存利用", "增量更新") — which could be cut or tightened. Not 2 because the workflow core itself is efficient rather than extensively explaining known concepts. | 3 / 5 |
Actionability | There is one concrete executable command (`python scripts/web_search.py "<nextSearchTopic>"`), a defined JSON contract (`nextSearchTopic`, `shouldContinue`), a variables table, and a worked example — but the central LLM-analysis step has no actual prompt template, the findings-append step is described only abstractly, and the referenced script lives in an external skill not present in this bundle. So the guidance is concrete but incomplete, matching the 3 anchor better than 4. | 3 / 5 |
Workflow Clarity | The sequence is clearly laid out: five numbered phases, per-iteration steps a–d, explicit conditional branching on `shouldContinue`, dedup rule ("避免重复搜索相同主题"), a full worked example, and a troubleshooting section mapping common failures to fixes. It misses 5 because there is no explicit validation of search-result quality or report accuracy checkpoint — the loop's only gate is the boolean continue flag. | 4 / 5 |
Progressive Disclosure | The single SKILL.md is well sectioned with headers (workflow, variables, examples, troubleshooting), so navigation within the file is fine — but there are no bundle files or references at all, and sizable blocks (工作流示例, 使用场景, 故障排除/性能优化) are inlined that could live in separate reference files. This is the middle anchor: structure present, but content that should be split out is inline. | 3 / 5 |
Total | 13 / 20 Passed |