Research & KnowledgeDone with Claude Code Oct 11, 2026
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.
- 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.
- 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.
- Worth Knowing
- The spec was the hard part. Pro plan is enough, though usage limits slow you down; he moved to Max for convenience.
Try it yourself 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.
Do this with a skill.
These SkillShop playbooks do this exact job.