Content
77%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
A highly actionable, well-sequenced reference skill with executable code and clear workflows, held back by some conceptual padding and a monolithic single-file structure with no progressive disclosure.
Suggestions
Trim the 'Why Slippage Models Are Needed' preamble and VWAP/TWAP goal explanations; assume the reader knows these concepts and keep only the domain-specific reference data.
Split the per-market reference tables (slippage bps, impact coefficients, trading costs) into a references/ file and link to it from SKILL.md so the main body stays a concise overview.
Fix the illustrative SignalEngine.generate snippet (undefined `signals` and `_compute_signal`) or mark it explicitly as pseudocode so it is not mistaken for executable code.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | Mostly efficient reference material (per-market bps tables, impact coefficients, cost tables, executable functions), but it opens with a 'Why Slippage Models Are Needed' rationale and explains VWAP/TWAP goals that a capable model already knows, so it could be tightened. | 2 / 3 |
Actionability | It provides fully executable Python (fixed_slippage, linear_impact, sqrt_impact, delayed_execution), concrete config JSON, a model-selection decision tree, and copy-paste-ready reference tables. | 3 / 3 |
Workflow Clarity | A clear selection decision tree (capital vs ADV), a numbered 3-step Analysis Framework with a worked example, and a sensitivity-analysis table serving as a verification checkpoint give an explicit, sequenced workflow. | 3 / 3 |
Progressive Disclosure | The ~340-line body is a single monolithic file with all models, tables, and code inline; section organization is good, but detailed reference content that could be split out is not separated into bundle files, and no external references are signaled. | 2 / 3 |
Total | 10 / 12 Passed |