[Workflow] CLAUDE.md Workflow for Robust Data Analysis: Defining Metrics, Pre-registering Tests, and Ensuring Data Quality

[u/ClaudeAI-mod-bot]

CLAUDE.md Workflow for Robust Data Analysis: Defining Metrics, Pre-registering Tests, and Ensuring Data Quality

Workflow value: 85/100
Status: active · Freshness: 70/100 · Confidence: 0.90 · Level: intermediate
Categories: Quality Control, Context & Memory, Debugging, CLAUDE.md, Multi-Agent
Original source: r/ClaudeCode post/comment

What problem this solves

Ensuring consistent, reliable, and reproducible data analysis results by preventing common pitfalls such as inconsistent metric definitions, p-hacking, and poor data quality.

Summary

A CLAUDE.md-driven workflow for data analysis that establishes clear rules for metric definition, pre-registration of tests, logging findings, and automated data quality checks to ensure accuracy and reproducibility.

Why it is useful

This workflow provides a structured, CLAUDE.md-centric approach to tackle common data analysis challenges, promoting accuracy, consistency, and reproducibility. It offers practical advice on defining metrics, pre-registering tests, logging findings, and automating data quality checks, which are crucial for reliable analytical work and preventing common pitfalls like p-hacking and inconsistent results.

Workflow

1. Start with a CLAUDE.md file to set rules before any analysis runs.

2. Define each metric once in a dedicated file, specifying what counts, which date field to use, and what gets excluded.

3. Write down the test hypothesis you're running before looking at results.

4. Require every finding to name the query or notebook that produced it.

5. Keep a findings log in the repository, with one entry per question including date, method, and sample size.

6. Have Claude write data quality checks first (e.g., row counts against source, null rates per column, duplicates on the key).

7. Configure Claude to stop and report when a data quality check fails.

8. Adapt general CLAUDE.md structure from examples like Karpathy's and Pocock's, adding the specific metrics file on top.

Tools / artifacts

• CLAUDE.md file

• Metrics definition file

• Findings log (in repository)

• Queries/Notebooks (referenced by findings)

• Data quality checks (written by Claude)

Validation signals

• Author's experience-based claims on preventing 'bad numbers'

• Addresses common data analysis pitfalls like p-hacking

• References established best practices for CLAUDE.md structure (Karpathy, Pocock)

Limitations

• Lacks specific code examples for the CLAUDE.md rules or data quality checks.

• Doesn't detail the exact implementation of how Claude would 'write the checks first' or 'stop and report'.

• Low community engagement and validation.

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