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analyze-correlations

analyze-correlations is a skill for Claude Code from ChrisGVE/localdata-mcp. It costs 28 tokens per session (519 once invoked), scanned A, original, Apache-2.0.

A workflow for finding and measuring relationships between numeric columns in connected datasets. It tests correlations and can suggest regression models, which predict one value from others.

In plain words
What is it for?
Use it to inspect database schemas, calculate pairwise correlations, filter statistically meaningful results, and identify relationships worth modeling.
Why use it?
It helps reveal which variables move together before you build a predictive model.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter.

Part of the localdata-mcp plugin — 18 skills, 11 agents, 1 MCP server shipped together

not rated 4repo +1 27d ago A scan 28 tokens original Apache-2.0

Good fit Use it to inspect database schemas, calculate pairwise correlations, filter statistically meaningful results, and identify relationships worth modeling.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/chrisgve/localdata-mcp/analyze-correlations
View source ↗ ChrisGVE/localdata-mcp
Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

Any agent
npx skills add ChrisGVE/localdata-mcp --skill analyze-correlations
Clone the repo
git clone --depth 1 https://github.com/ChrisGVE/localdata-mcp

Made for: Claude Code.

Or install localdata-mcp, the plugin that ships this one along with the rest of its 18 skills, 11 agents, 1 MCP server.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for analyze-correlations

README.md
[![agentmods](https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/analyze-correlations/github.svg)](https://agentmods.dev/skills/chrisgve/localdata-mcp/analyze-correlations)
Your own site
<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/analyze-correlations"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/analyze-correlations/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for analyze-correlations

Your own site · 80×15
<a href="https://agentmods.dev/skills/chrisgve/localdata-mcp/analyze-correlations"><img src="https://agentmods.dev/badge/skills/chrisgve/localdata-mcp/analyze-correlations.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 28 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 519 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5.1 $0.00028 $0.00519
Opus 5 $0.00014 $0.00260
Sonnet 5 $0.00006 $0.00104
Haiku 4.5 $0.00003 $0.00052

Measured 11d ago against content hash 38a3082040a1, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-11, from the pricing page.

Security

Grade A, and why

analyze-correlations scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 11d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

skills/statistical/analyze-correlations/SKILL.md · 35 lines

How it starts

The opening of the file, as written. The whole thing — 35 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Analyze Correlations

Discover and quantify relationships between variables in a connected dataset.

Steps

  1. Get the schema. Call describe_database with the database name from $ARGUMENTS. Identify all numeric columns across tables. Note column names and which table each belongs to.

  2. Extract numeric data. Call execute_query to select all numeric columns from the primary table. If data spans multiple tables, join on shared keys. Limit to 50,000 rows if the dataset is large.

  3. Test pairwise correlations. For each meaningful pair of numeric columns, call analyze_hypothesis_test with test type "correlation". Use Pearson for normally distributed data, Spearman for skewed or ordinal data. Record the correlation coefficient and p-value for each pair.

  4. Filter significant results. Retain pairs where p-value is below 0.05. Sort by absolute correlation strength. Classify relationships:

    • Strong: absolute r above 0.7
    • Moderate: absolute r between 0.4 and 0.7
    • Weak: absolute r between 0.2 and 0.4

  5. Measure effect sizes. For the top 5 strongest correlations, call analyze_effect_sizes to quantify practical significance. Compare statistical significance (p-value) against practical significance (effect size). Flag cases where a correlation is statistically significant but practically negligible.

  6. Check for confounders. Look for pairs of strong correlations that share a common variable. Note potential confounding relationships where A correlates with B and A correlates with C.

  7. Present results. Provide a ranked table of correlations with columns: variable pair, correlation coefficient, p-value, effect size, and interpretation. Group by strength category.

  8. Recommend next steps. For the strongest relationships:

    • Suggest /localdata-mcp:regression with the best predictor-outcome pairs
    • Flag multicollinearity risks if predictors are highly correlated with each other
    • Recommend further investigation for surprising or counterintuitive correlations

Read the full file on GitHub · 35 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 11d ago First seen · 35 lines · 28 tokens per session scan A 38a3082040a1

Subscribe to this mod's changes

analyze-correlations is a skill published in the GitHub repository ChrisGVE/localdata-mcp (4 stars, last pushed 27d ago), licensed Apache-2.0. It adds 28 tokens to every session and 519 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

Analytics and metricsClaude Codeai-toolsclaude-desktopdata-analyticsfastmcpmcp
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