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stock-correlation

Analyze how stocks move together using Yahoo Finance price history (yfinance): find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation. Use this skill whenever the user asks what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation — including well-known pairs like AMD/NVDA, GOOGL/AVGO, or LITE/COHR. With a single ticker, assume the user wants its correlated peers.

74

Quality

93%

Does it follow best practices?

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SecuritybySnyk

Passed

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SKILL.md
Quality
Evals
Security

Quality

Content

82%Weight 40%Scale 1-5

Reviews 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.

DimensionReasoningScore

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

Description

100%Weight 40%Scale 1-5

Based on the skill's description, can an agent find and select it at the right time? Clear, specific descriptions lead to better discovery.

An excellent description: concrete third-person action list, comprehensive natural-language triggers with synonyms and named example pairs, explicit what/when structure with a single-ticker default, and a distinct niche. It is on the longer side, but every clause is a functional trigger or capability, not padding.

DimensionReasoningScore

Specificity

Lists multiple concrete actions — 'find correlated peers for a ticker, measure correlation, beta, and spread between specific tickers, cluster a group into a correlation matrix, and track rolling or regime-dependent correlation' — comprehensively covering every sub-skill. Not 4 because there are no coverage gaps: all four documented capabilities are explicitly named in third-person voice.

5 / 5

Completeness

Explicitly answers both 'what' (analyze co-movement: find peers, measure correlation/beta/spread, cluster, track rolling correlation) and 'when' ('Use this skill whenever the user asks...' with concrete trigger phrases), and even adds a single-ticker default ('With a single ticker, assume the user wants its correlated peers'). Clearly matches the 5 anchor, not 4 where the 'when' could be more explicit.

5 / 5

Trigger Term Quality

Comprehensive natural trigger terms including synonyms users would actually say: 'what moves with a stock, what else drops when it drops, related tickers or sympathy plays, sector or supply-chain peers, pair trading or hedging pairs, beta or relative performance, correlation matrices, co-movement, or rolling/realized correlation', plus concrete well-known pairs (AMD/NVDA, LITE/COHR). Not 4 because no common variation is missing.

5 / 5

Distinctiveness Conflict Risk

Clear niche — stock correlation/co-movement analysis via yfinance — with distinct trigger phrases unlikely to fire for unrelated skills. Minimal overlap risk; matches the 'clear niche with distinct triggers' anchor rather than 4's 'minor overlap risk with closely related skills'.

5 / 5

Total

20

/

20

Passed

Validation

100%

Checks the skill against the spec for correct structure and formatting. All validation checks must pass before discovery and implementation can be scored.

Validation — 16 / 16 Passed

Validation for skill structure

No warnings or errors.

Repository

himself65/finance-skills

Path

plugins/market-analysis/skills/stock-correlation/SKILL.md

Reviewed

Table of Contents

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