C✳CODEZ.WIN
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Localized reading guide

explore-dataPurpose, usage and source

Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.

Source retrieved:

Source repository
anthropics/knowledge-work-plugins
SKILL.md
data/skills/explore-data/SKILL.md
Repository stars
34,049
Repository updated
2026-10-30
Categories
Data
Last collected

License: License file needs review

Source SKILL.md

Workflow excerpts are reading aids, not executable instructions from Codez. No third-party Skill was run to produce this page.

The purpose and workflow passages below are upstream material, organized and translated for reading. They are distinct from the separately labeled Codez editorial analysis.

What this Skill does

Profile and explore a dataset to understand its shape, quality, and patterns. Use when encountering a new table or file, checking null rates and column distributions, spotting data quality issues like duplicates or suspicious values, or deciding which dimensions and metrics to analyze.

How the upstream workflow works

Completeness Score

Rate each column: Complete (>99% non-null): Green Mostly complete (95-99%): Yellow -- investigate the nulls Incomplete (80-95%): Orange -- understand why and whether it matters

Upstream passage · L176 ↗

/explore-data - Profile and Explore a Dataset

> If you see unfamiliar placeholders or need to check which tools are connected, see CONNECTORS.md. Generate a comprehensive data profile for a table or uploaded file. Understand its shape, quality, and patterns before diving into analysis.

Upstream passage · L7 ↗

1. Access the Data

If a data warehouse MCP server is connected: Resolve the table name (handle schema prefixes, suggest matches if ambiguous)

Upstream passage · L21 ↗
Upstream passage · ↗
/explore-data <table_name or file>

Requirements and setup

Lineage and Dependencies

When exploring an unfamiliar data environment: Start with the "output" tables (what reports or dashboards consume)

Upstream passage · L311 ↗

Limits and quality checks

4. Identify Data Quality Issues

Apply the quality assessment framework below. Flag potential problems: High null rates: Columns with >5% nulls (warn), >20% nulls (alert)

Upstream passage · L110 ↗

Start with a safe trial

  1. Check the supported host and the repository installation instructions. Keep the Skill directory and its referenced scripts, assets and documentation together; SKILL.md alone may omit dependencies.
  2. Give the agent your real input, desired output and acceptance criteria. Invocation syntax and available tools depend on the host; this site does not assume a universal install command.
  3. Try the source workflow in a disposable project or on a copy. Review operations that change files, remote services or billable resources before allowing them.
  4. Validate the output against the source quality checks and your own requirements. A successful trigger or a high repository Star count does not establish a correct result.

Skill format and directory specification ↗

Read further in the source

Translation and scope

This reading guide selects passages from the pinned upstream SKILL.md. Translation is assisted. It is not a complete manual or a claim of tested compatibility. Read the original and its referenced files before use.

This introduction refers to the pinned version below; current repository activity and collection time are shown separately.

Source retrieved
Source commit
2574e29377c7173d0cecffef29a6ecbd7f8ccb5d
Source SHA-256
af7590fa616360259da712b5ede5f79b817beb854c47a465f95774354988e8a2
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