workflow-doc-agent

An AI consulting engagement was looking for exactly this:

Have an agent look at a folder of production work (a Python file and an Excel report), summarize the purpose of the code, ask a few questions on frequency of the workflow, and then auto-build the documentation for it.

Rather than write a proposal about how I would build it, I built it. Then I took the call with it running. I got the work.

It reads the folder without executing anything: AST for the Python, sampled sheets and headers for the workbook, so no whole file is ever dumped into a prompt. A cheap model writes the summary and asks three to five clarifying questions about schedule and ownership. A stronger model writes the final runbook. Each stage is validated against a schema before the next one starts.

Two decisions matter more than the rest. Both model providers sit behind one interface with identical schemas and an identical loop, so switching is one environment variable. And a cost tracker stops the run at a hard budget ceiling instead of spending past it. Anyone who has handed an autonomous loop an API key knows why the second one is there.

It ships with 9a tests, a capability eval, a regression eval, and a sample holding 24 months of synthetic data.