Content
71%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 a well-structured, actionable workflow with clean progressive disclosure to real bundle files. Its main weakness is redundancy: gap-analysis guidance is repeated across several sections.
Suggestions
Consolidate the gap-analysis guidance into one place (e.g. the 注意事项 section) and reference it from steps 4 and 6 instead of restating it.
Add an explicit validation checkpoint after fetching data (e.g. confirm the data file exists and is non-empty before calling analyze_stock.py) with a retry-on-failure note.
State the data-file handoff explicitly in the main flow (e.g. the file is named stock_data_<code>.json) rather than only in the examples.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly task-focused with concrete script calls and parameters, but gap-analysis points are repeated across step 3, step 4, step 6, and the 注意事项 section, which is noticeable redundancy that could be tightened. | 3 / 5 |
Actionability | Gives concrete, executable invocations ("fetch_stock_data.py --stock_code ... --days 30", "analyze_stock.py --data_file ...") backed by real script files, with worked A-share/HK/US examples; only the exact data-file handoff is left implicit in the main flow. | 4 / 5 |
Workflow Clarity | A clear 6-step sequence (validate code → fetch data → compute indicators → analyze → predict → advise) with step-1 code validation, but lacks explicit data-fetch/file-existence validation or error-recovery feedback loops before downstream analysis. | 4 / 5 |
Progressive Disclosure | A 资源索引 section points to one-level-deep, real bundle files (scripts/fetch_stock_data.py, scripts/analyze_stock.py, references/stock_code_format.md), each annotated with its purpose, keeping the overview in SKILL.md and detail in the bundle. | 5 / 5 |
Total | 16 / 20 Passed |