Content
62%Reviews the quality of instructions and guidance provided to agents. Good implementation is clear, handles edge cases, and produces reliable results.
The skill is highly actionable with executable code and a clear validated workflow, but it fails progressive disclosure by packing everything into one monolithic file and is somewhat verbose for a skill body.
Suggestions
Split the body into reference files (e.g. references/cointegration.md, references/signals.md, references/visualization.md) and keep SKILL.md as a concise overview that links one level deep.
Move the empirical pattern tables and visualization templates into separate reference files to reduce the main body's token footprint.
Trim prose that restates general finance knowledge Claude already knows (e.g. crisis correlation behavior, cointegration-vs-correlation distinction).
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is heavily code-dense and avoids explaining basic concepts, but at ~1100 lines it is far from lean; some prose restates domain knowledge Claude already knows (e.g. crisis correlation convergence, what cointegration 'measures') and could be tightened. | 2 / 3 |
Actionability | Every section provides complete, executable Python functions with imports, docstrings, and concrete return structures, plus threshold configuration tables — copy-paste ready. | 3 / 3 |
Workflow Clarity | The 'Full Workflow From Correlation to Signal' section sequences five explicit steps with validation checkpoints (p<0.05, half-life ranges, ADF stationarity, monthly re-testing, half-life warning), matching the clear-sequence-with-validation anchor. | 3 / 3 |
Progressive Disclosure | This is a monolithic ~1100-line SKILL.md with all content inline and no bundle files or references directory; the only cross-references point to external repo paths (agent/src/agent/context.py) rather than structured one-level-deep reference files. | 1 / 3 |
Total | 9 / 12 Passed |