How do I install the Data Analyzer skill?

Run npx skillmds@latest add wachawo/data-analyzer in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Data Analyzer safe to use?

This skill has not completed SkillMD's automated safety review yet. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Data Analyzer?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Data Analyzer free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Data Analyzer?

wachawo (@wachawo) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Data Analyzer by wachawo — Agent Skill

Name: Data Analyzer
Author: wachawo

File contents

When to use

You receive a new dataset and need to understand its shape and quality before analysis
An analysis produces surprising numbers and you want to verify the underlying data first
A stakeholder asks "is this data reliable?" or "what's in this table?"
You're about to run a model or statistical test and need data-quality assurance

Process

Load and overview — run scripts/data_overview.py to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).
Null profile — run scripts/null_profiler.py; compare output against thresholds in references/quality_thresholds.md and flag columns above limits.
Outlier detection — run scripts/outlier_detector.py (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?
Distribution summary — run scripts/distribution_summary.py for descriptive stats and univariate histograms on each numeric column.
Correlation exploration — run scripts/correlation_explorer.py; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.
EDA checklist sign-off — work through references/eda_checklist.md and confirm each item before declaring the dataset profiled.
Write findings — fill assets/eda_report_template.md with full profiling output; distil top issues into assets/findings_summary.md.

For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see references/pandas_polars_recipes.md.

Inputs the skill needs

Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope
Required: business context — what does one row represent?
Optional: quality threshold overrides (defaults in references/quality_thresholds.md)
Optional: columns to skip (PII, binary blobs, high-cardinality IDs)

Output

assets/eda_report_template.md (filled) — full profiling report with per-column stats
assets/findings_summary.md (filled) — top 3–5 quality issues and recommended next steps
Console output / plots from scripts for interactive inspection

---name: data-analyzerdescription: Systematic exploratory data analysis. Activate when a dataset needs profiling — structure check, nulls, outliers, distributions, correlations — before deeper analysis begins.---# When to use- You receive a new dataset and need to understand its shape and quality before analysis- An analysis produces surprising numbers and you want to verify the underlying data first- A stakeholder asks "is this data reliable?" or "what's in this table?"- You're about to run a model or statistical test and need data-quality assurance# Process1. **Load and overview** — run `scripts/data_overview.py` to get row count, dtypes, memory usage, and a sample. Confirm grain (what one row represents).2. **Null profile** — run `scripts/null_profiler.py`; compare output against thresholds in `references/quality_thresholds.md` and flag columns above limits.3. **Outlier detection** — run `scripts/outlier_detector.py` (IQR + z-score) on numeric columns; document flagged values and decide: real signal or data error?4. **Distribution summary** — run `scripts/distribution_summary.py` for descriptive stats and univariate histograms on each numeric column.5. **Correlation exploration** — run `scripts/correlation_explorer.py`; flag pairs with |r| > 0.8 as potential multicollinearity or redundancy.6. **EDA checklist sign-off** — work through `references/eda_checklist.md` and confirm each item before declaring the dataset profiled.7. **Write findings** — fill `assets/eda_report_template.md` with full profiling output; distil top issues into `assets/findings_summary.md`.For pattern recipes (e.g. polars vs pandas equivalents, chunked reads for large files), see `references/pandas_polars_recipes.md`.# Inputs the skill needs- Required: dataset path (CSV / Parquet / Excel) or a DataFrame already in scope- Required: business context — what does one row represent?- Optional: quality threshold overrides (defaults in `references/quality_thresholds.md`)- Optional: columns to skip (PII, binary blobs, high-cardinality IDs)# Output- `assets/eda_report_template.md` (filled) — full profiling report with per-column stats- `assets/findings_summary.md` (filled) — top 3–5 quality issues and recommended next steps- Console output / plots from scripts for interactive inspection

Frequently asked questions

Data Analyzer