Content
76%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.
A highly actionable, well-organized data-source skill with copy-paste code and dense reference tables. Its main gaps are the lack of a sequenced workflow with validation checkpoints and a referenced `references/` directory that is not actually bundled.
Suggestions
Add a brief numbered usage workflow with a validation checkpoint, e.g. '1. format symbol/date 2. call the hist function 3. verify df is non-empty and convert 日期 via pd.to_datetime()'.
Either create the referenced `references/` directory with the promised less-common interface docs, or remove the dangling pointer to it.
Trim the Overview so it does not restate the frontmatter description (free/no-API-key/18k+ stars) word-for-word.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly lean and information-dense (compact tables, copy-paste Quick Start), but the Overview restates the frontmatter ('completely free, open-source', 'No registration or API key required', '18k+ stars') and could be trimmed. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready Quick Start covering the three common cases (A-share/US/HK daily OHLCV) plus concrete function-name/param tables, symbol-format and date-format specs — the common cases are directly runnable. | 5 / 5 |
Workflow Clarity | The pieces (symbol format, date format, function calls) are present but not sequenced into a numbered workflow, and there are no validation checkpoints (e.g., 'verify df is non-empty / has expected columns') even for the read-only fetch path. | 3 / 5 |
Progressive Disclosure | Well-organized into clear sections with a one-level-deep reference pointer ('see the references/ subdirectory'), but that referenced directory does not exist (dangling reference) and the API tables are somewhat bulky inline. | 4 / 5 |
Total | 16 / 20 Passed |