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Run data cleaning to executive summary with agent pipeline

Built five Claude Code agents (cleaner, analyzer, visualizer, report writer, summarizer) chained by an orchestrator for end-to-end data analysis.

Time saved
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Setup time
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Running cost
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Cost range
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Use case overview

Built five Claude Code agents (cleaner, analyzer, visualizer, report writer, summarizer) chained by an orchestrator for end-to-end data analysis.

Reported experience

  1. What they did

    Bronson, a data analyst, used an AI app-idea agent to produce a 713-line specification. He then pointed Claude Code at it. Over about five hours Claude Code built five agents plus a @full-analysis orchestrator that passes files between them. It wrote its own tests and fixed its own errors. He only approved permissions, about 20 times.

  2. How it went

    The build produced 627 passing tests and cost about $35 in estimated API usage. It has not yet been run on real work data, and outputs still need analyst review.

  3. Worth knowing

    The spec was the hard part. Pro plan is enough, though usage limits slow you down; he moved to Max for convenience.

Example prompts

Try with Claude Code
Set up a chain of subagents in this project for analyzing [dataset file]: a cleaner, an analyzer, a visualizer, a report writer, and a summarizer, run in order by an orchestrator. Have them clean the data, find the main patterns, make [number] charts, write a full report, and finish with a one-page executive summary. Save each stage's output in its own folder and keep the original data unchanged.
Coding · Advanced
  1. Write migration plan
  2. Agent edits in batches
  3. Tests per batch

Ship a 40-file refactor with a coding agent

Plan, execute and review a cross-cutting refactor in small batches, with Claude Code making the edits and GitHub Copilot reviewing each PR.

Claude Code · GitHub Copilot