Content
80%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The content is lean, executable, and free of concept over-explanation, with strong concrete code and config examples. Its weaknesses are the absence of an explicit validation/verification checkpoint for the batch backtest workflow and a single inline structure that does not progressively disclose deeper reference material.
Suggestions
Add an explicit validation step before backtesting, e.g. 'Verify the loaded DataFrame is non-empty (especially for Tushare, which returns empty data when score < 2000) before running the backtest.'
Move the full OKX candlestick API example and/or indicator templates into a references file linked from the body, keeping SKILL.md as a tighter overview to improve progressive disclosure.
Clarify the backtest run sequence as numbered steps with a checkpoint after data retrieval.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is lean and assumes Claude's competence: it does not explain what candlesticks, VWAP, or pandas are, and every section (config JSON, API example, indicator templates, pitfalls) is actionable rather than padded. | 3 / 3 |
Actionability | It provides fully executable, copy-paste-ready code (OKX candles request, VWAP/TWAP/volume templates), a concrete config.json snippet, and a specific pip install command. | 3 / 3 |
Workflow Clarity | The flow (set interval in config.json, run the backtest tool) is present, but minute-level backtesting is a batch operation with no explicit validation checkpoint (e.g. verifying data loaded or Tushare non-empty before running), so it is capped at 2. | 2 / 3 |
Progressive Disclosure | The file is well-sectioned but ~96 lines with no bundle references, and content that could be split out (full OKX API example, indicator templates) is kept entirely inline, so structure is only partially optimized. | 2 / 3 |
Total | 10 / 12 Passed |