Content
63%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 dense, domain-rich skill with genuinely useful market-specific reference data and executable code, undermined by educational padding on concepts Claude already knows and by inlining everything in one long file. Splitting reference tables into separate files and trimming conceptual walkthroughs would raise both conciseness and progressive disclosure.
Suggestions
Move the per-market reference tables (slippage bps, impact coefficients, trading costs) and the VWAP/TWAP execution detail into a references/ file, keeping SKILL.md as a lean overview with one-level-deep pointers.
Delete or compress sections explaining what Claude already knows — the "Why Slippage Models Are Needed" block, the VWAP/TWAP formula walkthroughs, and the square-root advantages list — keeping only the China-specific empirical parameters.
Replace the incomplete SignalEngine snippet with either a fully runnable integration example or a short note pointing to the actual signal_engine.py, and add an explicit validation step (e.g. check ADV and participation bounds before applying an impact estimate).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The reference tables (per-market slippage, impact coefficients, trading costs) and the tested-code guidance earn their tokens, but sections like "Why Slippage Models Are Needed", the VWAP/TWAP formula walkthroughs, and the "Advantages of the square-root model" list explain concepts Claude already knows. Mostly efficient with some unnecessary explanation — matching the 3 anchor, not 4, because the conceptual padding spans multiple sections. | 3 / 5 |
Actionability | The four impact-function examples are executable with expected outputs, import paths, defaults, and a numeric selection decision tree, plus concrete config JSON. However, the SignalEngine integration snippet is a fragment (`signals` never initialized, `self._compute_signal` undefined), so guidance is mostly executable with minor gaps — the 4 anchor rather than 5's fully copy-paste-ready coverage. | 4 / 5 |
Workflow Clarity | The model-selection decision tree and the three-step transaction-cost analysis are clearly sequenced with worked numeric examples, and the Notes section lists explicit constraints (T+1, price limits, participation caps). It falls short of 5 because there are no explicit validate-then-proceed checkpoints (e.g. confirming ADV/participation bounds before trusting an impact estimate), landing on the 4 anchor: clear sequence, most checkpoints present. | 4 / 5 |
Progressive Disclosure | The body is well-sectioned but monolithic at ~330 lines with no bundle files: the per-market reference tables, VWAP/TWAP detail, and output templates are prime candidates for separate reference files. This matches the 3 anchor (content that should be separate is inline, structure present but could be better organized); it is above 2 because section headers and navigation within the file are good. | 3 / 5 |
Total | 14 / 20 Passed |