Conciseness

The operational core (threshold tables, commands, parameters, red flags) is genuinely useful, but the ~670-line body includes substantial explanation of concepts Claude already knows — 'Pair trading is a market-neutral strategy that profits from...', 'Why Cointegration Matters' bullets, Pearson/beta/z-score primers — much of which duplicates references/cointegration_guide.md and methodology.md. Time-sensitive details (Version 1.0, 'Last Updated: 2025-11-08', '$29/mo' pricing) and padded sections ('Key Advantages', 'Important Notes') add further tokens that could be trimmed.

Actionability

Fully executable throughout: copy-paste-ready `uv run` invocations with concrete flags for both scripts, complete parameter tables, a runnable statsmodels ADF snippet, a realistic JSON output example, and worked use cases. The common cases (sector screening, custom symbol list, single-pair analysis) are each covered with specific commands.

Workflow Clarity

The 8-step workflow is clearly sequenced with per-step objectives, and validation checkpoints exist (data-quality checks in Step 2, statistical minimum requirements and red flags in Quality Standards, input rejection and error behavior in Output, plus a Troubleshooting section). It falls short of the top anchor because the workflow steps themselves don't embed explicit validate-then-proceed feedback loops — error recovery is deferred to separate sections rather than inline at each checkpoint.

Progressive Disclosure

Bundle structure is solid: two reference files exist, are exactly one level deep, and are clearly signaled in 'Reference Documentation' with bullet summaries of their contents; both scripts are documented with purpose, usage, and parameters. The main gap is that sizeable statistical-theory content (correlation interpretation tables, cointegration explanation, half-life derivation) is inlined in the body where the reference guides already cover it — more than a minor organization gap but not the majority of the content.

Specificity

The description lists multiple concrete actions — 'Detects cointegrated stock pairs within sectors, analyzes spread behavior, calculates z-scores, and provides entry/exit recommendations' — plus supported analyses ('correlation analysis, cointegration testing, and spread backtesting'), giving comprehensive coverage of the skill's capabilities. It is not merely naming a domain; each clause is a distinct, verifiable capability.

Completeness

It explicitly answers both questions: what it does ('Detects cointegrated stock pairs... calculates z-scores... provides entry/exit recommendations') and when to use it ('Use when user requests pair trading opportunities, statistical arbitrage screening, mean-reversion strategies, or market-neutral portfolio construction'). The 'Use when' clause contains four concrete trigger phrases, matching the top anchor.

Trigger Term Quality

Strong natural triggers: 'pair trading opportunities', 'statistical arbitrage screening', 'mean-reversion strategies', 'market-neutral portfolio construction' — phrases a user would plausibly say. A few common variations are missing (e.g., 'pairs trading', 'relative value', 'which stocks move together'), keeping it just short of the comprehensive-synonyms anchor.

Distinctiveness Conflict Risk

Statistical arbitrage / pair trading is a clear niche with distinct trigger vocabulary ('cointegrated', 'z-scores', 'market-neutral'), and nothing in the description is generic enough to fire for unrelated skills. Minor theoretical overlap with a general backtesting skill, but the described capability set is unmistakably this skill's.