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: executable code for every routed intent, a clear routing table with defaults, and sensible use of a reference file. The main weaknesses are illustrative tables that add token weight without new information, the absence of explicit data-download validation checkpoints, and inlining all four sub-skill implementations rather than splitting some into references.
Suggestions
Trim or shorten the illustrative result tables in A3, B2, and D3 — one example row each would convey the presentation format at a fraction of the tokens.
Add an explicit validation checkpoint after yf.download (e.g., check for empty/partial data and surface dropped tickers before computing correlations) to complete the workflow's feedback loop.
Consider moving one or two of the less-common sub-skills (e.g., Sub-Skill C: Sector Clustering or D: Realized Correlation) into reference files, keeping SKILL.md as a tighter routing overview.
| Dimension | Reasoning | Score |
|---|---|---|
Conciseness | The body is efficient — no library tutorials or explanations of concepts Claude already knows — but the illustrative result tables (A3, B2, D3) and interpretation guides restate what the code output already shows and could be trimmed. This sits noticeably above the mostly-efficient midpoint but short of 'every token earns its place'. | 4 / 5 |
Actionability | All four sub-skills ship complete, copy-paste-ready functions with real yfinance/pandas/scipy calls, plus operational details like MultiIndex column handling, dropna thresholds, and a scipy fallback. The common cases for each routed intent are covered. | 5 / 5 |
Workflow Clarity | The sequence is clear (dependency check → routing table → sub-skill → response requirements) with real feedback loops (DEPS_MISSING → pip install, broaden-to-sector if <10 peers, scipy fallback). It falls short of 5 because there is no explicit checkpoint for empty or partial downloads before computing, and no post-run verification step. | 4 / 5 |
Progressive Disclosure | SKILL.md works as a routing overview with one clearly signaled, one-level-deep reference (references/sector_universes.md, which exists and contains the promised screener implementation). Minor gap: all four sub-skill implementations are inlined (~300 lines) where the routing pattern suggests splitting some out. | 4 / 5 |
Total | 17 / 20 Passed |