Conciseness

The body is ~1100 lines with dense, mostly non-redundant analytics code and genuinely non-obvious empirical tables (regime correlation behavior, half-life ranges, cross-market lag patterns), but it also carries content Claude does not need — full matplotlib boilerplate for heatmaps, dendrograms, and signal plots, plus a long worked report example. It is not 4 because the visualization templates and several verbose docstrings could be trimmed or externalized; it is not 2 because there is little conceptual padding and the domain-specific content earns its place.

Actionability

Nearly all guidance is executable: complete function implementations with defaults and docstrings, concrete thresholds ('keep pairs with p < 0.05', 'half-life between 5 and 60 days'), a pip install command, and a report output template. It falls short of 5 because some snippets are not copy-paste ready — 'from src.quantlib.timeseries import ...' references a project module not in this bundle, and monitor_spread_health uses 'adfuller' and 'np' without importing them in that scope.

Workflow Clarity

The 'Full Workflow From Correlation to Signal' section gives five clearly sequenced steps with explicit validation criteria at each gate (Pearson > 0.6, cointegration p < 0.05, half-life 5-60 days, ADF p < 0.05), and Step 5 monitoring defines feedback loops (recompute Z-Score daily, re-test cointegration monthly, 'Warn if half-life exceeds 2× the original'), reinforced by monitor_spread_health's status-to-action mapping ('exit_now'). Mode 1's numbered scan-then-test workflow mirrors this structure.

Progressive Disclosure

The single SKILL.md is a monolith: the cointegration framework, plotting templates, and cross-market empirical tables are all inline with no references/, scripts/, or assets/ files at all, so content that clearly belongs in separate files is inlined. It is not 2 because internal structure is strong — clear mode sections, tables, an overview, and a notes checklist that make navigation easy despite the size.

Specificity

The description lists many concrete, named capabilities — 'co-movement discovery, deep return-correlation analysis, sector clustering, realized correlation, Engle-Granger / Johansen cointegration, half-life, Kalman dynamic hedge ratio, cross-market linkage analysis, and pair-trading signal generation' — giving comprehensive coverage of the skill's actions in third-person voice. It is not below 4 because there are no meaningful gaps in the action list; every mode in the body is named.

Completeness

The 'what' is clear and detailed (the full capability list), but there is no 'Use when...' clause or equivalent trigger guidance — nothing tells the user when to invoke this skill. Per the rubric guideline, a missing 'Use when...' clause caps completeness at 3; it is not 2 because the 'what' half is explicit and comprehensive.

Trigger Term Quality

Good keyword coverage including natural quant terms users would say: 'correlation', 'cointegration', 'pair-trading signal generation', 'hedge ratio', 'co-movement'. It is not 5 because common variations are missing — the plain phrase 'pairs trading', 'statistical arbitrage', 'cointegration test', and file/data-agnostic synonyms are absent; it is above 3 because the included terms go beyond jargon to several phrases a practitioner would naturally use.

Distinctiveness Conflict Risk

It occupies a clear niche (quantitative correlation/cointegration analytics for pairs trading) with distinct trigger terms like 'cointegration', 'half-life', and 'Kalman dynamic hedge ratio' that no general-purpose skill would claim. Overlap risk with sibling skills is minimal.