DAAF-Contribution-Community

DAAF-Contribution-Community / daaf

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DAAF, the Data Analyst Augmentation Framework: An open-source, extensible workflow for Claude Code that allows skilled researchers to rapidly scale their expertise and accelerate data analysis by as much as 5-10x -- without sacrificing the transparency, rigor, or reproducibility demanded by our core scientific principles.

daafguide.substack.com
116
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100% credibility
Found Feb 17, 2026 at 68 stars -- GitGems finds repos before they trend. Get early access to the next one.
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AI Analysis
Python
AI Summary

DAAF is an open-source AI-powered workflow that helps researchers rapidly analyze public education data with built-in transparency, rigor, and reproducibility.

How It Works

1
🔍 Discover DAAF

You hear about DAAF, a helpful tool that lets AI speed up your data research while keeping everything clear and checkable.

2
💻 Set it up quickly

Follow simple steps to get DAAF running on your computer in about 10 minutes, connecting it to your AI account.

3
❓ Ask your research question

Tell DAAF your question, like 'How do college graduation rates relate to admissions selectivity?', focusing on public education data.

4
🤖 AI does the heavy lifting

DAAF's AI explores data sources, plans the analysis, fetches and cleans data, runs stats and charts, all while showing every step.

5
📊 Review your results

Get a polished report with key findings, visuals, limitations, plus a notebook to explore every data file and code yourself.

6
🔄 Tweak and expand

Ask for changes, new charts, or deeper dives, and DAAF updates everything quickly while keeping your work auditable.

🎉 Unlock faster insights

Finish complex research reports in hours instead of weeks, with full transparency to verify and share confidently.

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AI-Generated Review

What is daaf?

DAAF is a Python-based workflow that augments data analysts with Claude Code, accelerating analysis 5-10x by automating fetch, clean, transform, analyze, and visualize steps from a research question—while delivering transparent, reproducible Python scripts and marimo notebooks demanded by core scientific rigor. It allows skilled researchers to scale expertise on public datasets like Urban Institute's 40+ education sources, producing reports with findings, limitations, and visuals in hours, not weeks. Docker setup gets you running in 10 minutes with a high-usage Anthropic account.

Why is it gaining traction?

Unlike ad-hoc LLM chats or opaque auto-analysis tools, DAAF enforces file-first execution, adversarial QA, and human-in-loop guardrails, generating auditable pipelines that force-multiply your work without replacing judgment. Parallel projects, self-improving learnings, and extensible skills for new data/methods hook analysts chasing daaf-like augmentation. The 10-minute demo on college graduation rates shows real 5-10x speed on complex queries.

Who should use this?

Policy analysts dissecting education outcomes, social science researchers probing selectivity-graduation links, or data teams auditing institutional performance with public APIs. Perfect for Claude Code users handling repetitive ETL on structured data, needing reproducible outputs for stakeholders.

Verdict

Worth piloting for daaf-style analyst augmentation if you tolerate early bugs (52 stars, 1.0% credibility score)—docs and demo shine, but test coverage lags and API costs add up. Strong foundation for accelerating rigorous analysis; contribute to mature it.

(198 words)

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