Content
61%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 well structured and token-efficient, with concrete factor definitions, parameter defaults, and a usable engine snippet. Weaknesses center on the core equal-weight pipeline having no executable code or explicit validation checkpoints, and references to engine/registry files that are not part of the skill bundle.
Suggestions
Add an executable code sample (or precise formulas) for the core equal-weight pipeline — factor computation, cross-sectional Z-scoring, composite score, and TopN selection — since the current snippet only covers the ZooSignalEngine path.
Insert explicit validation checkpoints into the workflow, e.g. 'before ranking, assert the cross-section has >= 3 stocks with non-NaN factor values; skip the rebalance date otherwise', and a between-rebalance rule stated as a step rather than only a pitfall.
Either bundle 'zoo_signal_engine.py' (and 'example_signal_engine.py') under scripts/ or state where they live, so the file references in the Zoo Signal Engine section resolve within the skill.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient — tables for factors and parameters, a tight 4-step signal logic, and no padding — matching 'efficient; minor instances of over-explanation'. Not 5 because brief redundancies remain (e.g. 'Z-score normalization (subtract mean, divide by standard deviation)') and the version/migration narration could be trimmed slightly; not 3 because there is no noticeably verbose section. | 4 / 5 |
Actionability | Concrete elements exist (parameter defaults table, factor definitions, an executable ZooSignalEngine snippet), but the primary equal-weight pipeline — steps 1-4 of Signal Logic — is described only in prose with no executable code or formula-level detail for the composite step, matching 'some concrete guidance but incomplete'. Not 4 because the core workflow's implementation is left for the reader to construct. | 3 / 5 |
Workflow Clarity | The sequence is clearly numbered (calculate factors -> standardize -> composite score -> rank and select TopN) and Common Pitfalls encode guardrails ('requires at least 3 stocks', 'weights must be normalized'), but no explicit validation/checkpoint step exists in the rebalance loop, matching 'steps listed but validation gaps; checkpoints implicit'. Not 4 because pitfalls are advisory rather than built-in validation steps for this batch rebalancing operation. | 3 / 5 |
Progressive Disclosure | Well-organized sections with a self-contained ~72-line body, clearly signaled cross-references to the alpha-zoo skill, and a clean legacy-vs-preferred-engine split, matching 'good structure; most content appropriately placed'. Not 5 because referenced paths 'zoo_signal_engine.py', 'example_signal_engine.py', and 'src/factors/registry' are not bundled with the skill, leaving navigation one level deep but pointing outside the bundle. | 4 / 5 |
Total | 14 / 20 Passed |