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.
Labels
Over about five hours Claude Code built five agents plus a @full-analysis orchestrator that passes files between them.
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.
Built five Claude Code agents (cleaner, analyzer, visualizer, report writer, summarizer) chained by an orchestrator for end-to-end data analysis.
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.
Bronson, a data analyst, used an AI app-idea agent to produce a 713-line specification. He then pointed Claude Code at it.
The “Material” label failed its quality check and is not used for selection.
Details
- Agents
- Claude Code
- Source
- blog
- Source date
- Oct 11, 2026 · Submitted by Bronson Elliott
- Accounts
- 1
- In registry since
- Oct 12, 2026