Content
61%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 well-structured and action-rich with concrete schemas, sources, and templates across a clearly sequenced multi-stage workflow. Weaknesses are limited validation feedback loops for batch literature operations and some generic/padded prose that could be trimmed.
Suggestions
Add explicit validate→fix→retry feedback loops for the batch steps (e.g., after PDF download and after relevance scoring) to raise workflow_clarity above 3.
Trim generic best-practice and AI-execution principles ('透明性', '保守性', etc.) that do not add skill-specific actionable knowledge, improving token efficiency.
Reference the existing but currently-unlinked reference files (literature-search-guide.md, methodology-extraction-patterns.md) from the relevant workflow steps so all bundle content is navigable.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is mostly efficient with concrete schemas and directory layouts, but generic padded prose ('透明性', '可追溯性', '保守性', '用户中心' best-practices, and a restating troubleshooting section) could be trimmed; fits the 'mostly efficient but includes some unnecessary explanation' anchor. | 3 / 5 |
Actionability | Concrete JSON output schemas, named data sources (PubMed, Google Scholar, IEEE Xplore, arXiv, Semantic Scholar), explicit thresholds (score ≥7, 15-30 papers), naming conventions, and Unpaywall usage give mostly executable guidance; a few steps remain abstract ('AI 语义分析识别 Methods 章节内容') and no runnable code is inlined, keeping it just below 5. | 4 / 5 |
Workflow Clarity | Steps are clearly sequenced across 阶段0-3 with per-step 目标/方法/输出 and some checkpoints (directory validation, scoring threshold, plan review), but batch operations (multi-paper search/download/scoring) lack explicit validate→fix→retry feedback loops, so the batch-operation cap of 3 applies. | 3 / 5 |
Progressive Disclosure | Good structure with real one-level-deep references to output-templates.md and implementation-notes.md, and concrete scripts present (initialize.py, validate.py, bibtex.py, utils.py); minor gaps since literature-search-guide.md and methodology-extraction-patterns.md are not referenced in the body and scripts are not directly navigated. | 4 / 5 |
Total | 14 / 20 Passed |