Content
82%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 strong, highly actionable body: complete executable code for every sub-skill, a sensible routing workflow with defaults, and a properly signaled one-level-deep reference. The main weaknesses are minor: some trimmable disclaimer/probe prose, no explicit output-validation checkpoint, and most implementation detail inlined rather than split into references.
Suggestions
Trim the host-specific '!'-wrapped dependency probe to a plain `python3 -c "import yfinance, pandas, numpy"` one-liner and collapse the disclaimer to a single line, removing the Codex-specific exec-string paragraph.
Add an explicit validation checkpoint before presenting results, e.g. verify that the observation count meets the window requirements and that the target ticker survived the dropna filter, re-broadening the peer universe or lookback if not.
Add scipy to the Step 1 dependency probe (Sub-Skill C imports it), and consider moving one or two sub-skill implementations (e.g. Sub-Skills C and D) into references/ to keep SKILL.md closer to an overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The bulk is lean, operational code with minimal prose, but there are minor instances that could be trimmed: the host-specific '!'-wrapped exec probe paragraph ('If your host has not already executed the shell probes below (for example, in Codex)...'), the 'not affiliated with Yahoo' disclaimer, and caveats Claude already knows ('Correlation is not causation'). Fits 'minor instances of over-explanation' (4) rather than 3's 'some unnecessary explanation', since nearly all prose is operational guidance, not concept teaching. | 4 / 5 |
Actionability | Fully executable, copy-paste-ready Python functions for all four sub-skills, a concrete install snippet, a defaults table, and an intent-routing table; the peer-universe construction delegates to references/sector_universes.md, which contains complete working code. The only gap (scipy used in Sub-Skill C but not probed in Step 1) is explicitly mitigated by a documented fallback ('if scipy is not available, fall back to sorting by average correlation'). | 5 / 5 |
Workflow Clarity | Clear sequence — dependency check with install feedback loop, ambiguity-resolution defaults ('If ambiguous, default to Sub-Skill A... Sub-Skill B'), routing table, and a final Step 3 that requires reporting observations and 'tickers dropped due to insufficient data' as light output verification. Not 5 because there is no explicit validation checkpoint on computed outputs (e.g., verifying observation counts or that the target ticker survived the dropna filter) before presenting results; the operations are non-destructive so the cap-3 rule does not apply. | 4 / 5 |
Progressive Disclosure | One clearly signaled, one-level-deep reference (references/sector_universes.md exists, is summarized in a dedicated 'Reference Files' section with when-to-read guidance, and contains no further nested references), good section headers, and a routing table for navigation. Not 5 because ~200 lines of sub-skill code are inlined in SKILL.md while only the peer-universe detail is externalized — a minor organization gap where one or two sub-skill implementations could live in references. | 4 / 5 |
Total | 17 / 20 Passed |