Content
57%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 dense, well-organized knowledge reference with genuinely valuable China A-share calibration data, but it functions as an encyclopedia rather than an operational skill: there is no executable code despite declared dependencies, no explicit analysis workflow, and no progressive disclosure into reference files. The dangling 'src.quantlib.impact.sqrt_impact' pointer undermines both actionability and navigation.
Suggestions
Split the ~300-line body into the SKILL.md overview plus one-level-deep reference files (e.g., references/metrics.md for VPIN/Kyle-lambda/Amihud methodology, references/china-ashare.md for auction/block-trade mechanics), keeping only the quick workflow and output template inline.
Add runnable code for at least the core computations (VPIN buckets, Kyle lambda regression, impact estimate) in scripts/, or remove the 'pip install pandas numpy scipy' Dependencies section if no code is intended; also fix or remove the dangling 'src.quantlib.impact.sqrt_impact' reference since it does not exist in this bundle.
Add an explicit ordered workflow section (load Level-2/daily data -> compute metrics with the referenced scripts -> validate against the stated thresholds -> emit the report in the Output Format), so the implied sequence becomes a checkable procedure with validation checkpoints.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is dense and mostly efficient — compact tables, code blocks, and China A-share calibration data Claude cannot reliably know ("VPIN > 0.5 -> dangerous", "9:20-9:25 orders can be entered but not canceled", "Amihud < 0.5 -> high liquidity"), with every line earning its place. Not 5: the flash-crash 'Triggers' section and 'Spread drivers' lines restate textbook mechanisms Claude already knows. Not 3: there is no prose padding or library-tutorial filler; the value is overwhelmingly non-redundant calibration detail. | 4 / 5 |
Actionability | Concrete guidance exists — step-by-step VPIN computation with real formulas ("buy_volume = V × Φ(ΔP / σ)", "V = average daily volume / 50"), worked impact examples with arithmetic, screening thresholds, and a full output-report template — but nothing is executable: no runnable code despite a Dependencies section declaring 'pip install pandas numpy scipy', and the one implementation pointer ('src.quantlib.impact.sqrt_impact') is a dangling path not present in this bundle. This matches 'some concrete guidance but incomplete; pseudocode instead of executable code'. Not 4: the gap is not minor — a user cannot run anything, and key details (data sourcing, Kyle lambda regression mechanics) are only sketched. | 3 / 5 |
Workflow Clarity | The 'Analysis Framework' sections and the 'Output Format' report template imply an analysis sequence (liquidity diagnosis -> order-flow analysis -> cost estimate -> execution suggestion), but no explicit ordered procedure connects data collection, metric computation, and report generation, and there are no validation checkpoints or sanity checks on computed metrics. Not 4: the sequence is implicit rather than clearly laid out; not 2 because the VPIN/Kyle-lambda calculation steps are individually sequenced and the output template anchors the end state. | 3 / 5 |
Progressive Disclosure | Sections are well-organized with clear headers and a consistent format, but the ~300-line body is fully monolithic — no references/, scripts/, or assets/ bundle exists, and content that clearly belongs in separate files (per-metric methodology, China A-share mechanics, the report template) is all inline, matching the score-3 anchor 'content that should be separate is inline'. The single reference to 'src.quantlib.impact.sqrt_impact' points outside the bundle and is unsignaled. Not 4: there is no appropriate split at all; not 2 because internal structure (headers, tables, labeled subsections) is good, not minimal. | 3 / 5 |
Total | 13 / 20 Passed |