Content
72%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The body is actionable and well-structured with real bundle references, but it has redundant gap-analysis content and lacks validation checkpoints in its data pipeline. Removing duplication and adding a fetch-verification step would raise conciseness and workflow clarity.
Suggestions
Consolidate the gap-analysis guidance that is currently repeated across steps 4/5/6 and 注意事项 into a single concise section to reduce redundancy and improve conciseness.
Insert an explicit validation step after fetching data (e.g., verify the returned JSON is non-empty and contains K-line records before calling analyze_stock.py) with a retry/recovery path to lift workflow clarity to 3.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient and avoids explaining concepts Claude already knows, but gap analysis is explained repeatedly (in steps 4/5/6 and again in 注意事项) and 任务目标 restates capabilities, so it could be tightened; matches the 'mostly efficient but could be tightened' anchor. | 2 / 3 |
Actionability | Provides concrete, executable script calls with named parameters (e.g., `scripts/fetch_stock_data.py --stock_code 000001 --days 30`) plus three worked examples, matching the 'fully executable code/commands; copy-paste ready' anchor. | 3 / 3 |
Workflow Clarity | A clear 6-step numbered sequence exists, but there are no validation or verification checkpoints between data fetch and analysis, and no error-recovery loop; for a batch data pipeline the guidelines cap workflow clarity at 2. | 2 / 3 |
Progressive Disclosure | Three real bundle files (two scripts, one reference) are referenced one level deep in 资源索引 with purpose annotations and correct relative paths, and the body stays an overview rather than inlining their contents, matching the 'clear overview with well-signaled one-level-deep references' anchor. | 3 / 3 |
Total | 10 / 12 Passed |