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claude-data-analyst

Name: claude-data-analyst
Author: danielrosehill

By danielrosehill

First-pass data analysis toolkit: correlations, PII flagging, anomalies, hypothesis tests, data dictionaries, and trend analysis on a dataset in a folder.

npx claudepluginhub danielrosehill/claude-code-plugins --plugin claude-data-analyst

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Stars

Top 25%

Med: 0·Avg: 282

Installs

Med: 0·Avg: 1

What's Inside

Skills14

anomaly-analysis

/anomaly-analysis

Scan a dataset for significant anomalies — outliers, distribution shifts, impossible values, and unusual groupings. Use when the user wants a first-pass integrity and anomaly sweep of a CSV/Parquet/Excel file before deeper analysis.

correlation-analysis

/correlation-analysis

Detect and compute correlations between numeric variables in a dataset. Use when the user wants to see how variables in a CSV/Parquet/Excel file move together — Pearson, Spearman, or Kendall — with a short report flagging the strongest positive and negative pairs.

data-dictionary-creator

/data-dictionary-creator

Generate a data dictionary for a dataset, combining automatic profiling with the user's description of what the data represents. Use when the user wants documentation of columns — names, types, semantic meaning, units, allowed values, and nullability — for a CSV/Parquet/Excel file.

data-enrichment

/data-enrichment

Identify what the user is trying to analyse, diagnose gaps in the current dataset, propose external data sources that could fill them, then plan and implement the enrichment. Use when the dataset alone can't answer the user's question and extra context (reference data, lookups, joinable public datasets) is needed.

data-reporting

/data-reporting

Produce a parametric PDF report describing a dataset — size, schema, distributions, key statistics, and findings from other skills — compiled via Typst. Use when the user wants a shareable, print-ready document about their data, not a one-off markdown summary.

Stats

Version1.2.0

Stars6

MaintenanceExcellent

LicenseMIT

Last CommitApr 23, 2026

AddedApr 23, 2026

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danielrosehill11 danielrosehill

README

Claude Data Analyst

First-pass data analysis toolkit for Claude Code. Point it at a CSV, Parquet, or Excel file and get an initial impression — correlations, PII audit, anomalies, hypothesis checks, a data dictionary, or a trend narrative.

Skills

Skill	What it does
`correlation-analysis`	Compute Pearson/Spearman/Kendall correlations and rank the strongest variable pairs.
`pii-flag`	Scan columns and values for likely PII; mask samples; recommend remediation.
`anomaly-analysis`	Three-layer anomaly sweep: value sanity, distribution outliers, multivariate/temporal.
`hypothesis-testing`	Formalise a user-stated hypothesis, pick the right test, and return supports/refutes/inconclusive.
`data-dictionary-creator`	Merge auto-profiled schema with the user's description into a full data dictionary.
`trend-analysis`	Identify and narrate the major trends — directional, seasonal, compositional, per-segment.
`setup-data-workspace`	Discover data files in the current repo, load them into a DuckDB database, and update CLAUDE.md with query instructions.
`data-enrichment`	Diagnose gaps between the user's analytical goal and the dataset, propose external sources, plan and implement enrichment.
`multivariate-analysis`	Partial correlations, VIF, regression with interactions, Lasso, and PCA to tell which variables actually drive the target and which are redundant.
`forensic-sweep`	Flag data that looks suspiciously clean, imputed, smoothed, or pre-normalised — so the user knows what was done upstream before they got it.
`type-consistency-sweep`	Detect within- and cross-file type inconsistencies that block analysis or DB loading; fix trivial cases or delegate to a `Claude-Data-Wrangler` skill.
`standard-deviation`	Compute SD (plus variance, IQR, MAD, CV) for numeric columns with trustworthiness flags for skew, heavy tails, and small n.
`sample-size`	Characterise the effective sample size per analytical question, flag underpowered segments, and give a go/no-go verdict.
`data-reporting`	Generate a parametric PDF report (Typst) describing the dataset — schema, distributions, quality, findings from prior skills.

Recommended CLI tooling

The skills assume (and will suggest) these are available on PATH:

duckdb — SQL over CSV/Parquet/Excel at speed.
csvkit — csvstat, csvcut, csvlook.
miller (mlr) — pivots and tallies on CSV.
uv — run pandas/scipy/statsmodels/scikit-learn one-liners without a persistent venv.

Optional:

presidio-analyzer — ML-backed PII entity detection (via uv run --with presidio-analyzer).

Installation

claude plugins install claude-data-analyst@danielrosehill

License

MIT.

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