# I built a workflow that checks whether my AI agent's context was enough, then proves it with my own tests

> Source: <https://dev.to/littlestjames82sys/i-built-a-workflow-that-checks-whether-my-ai-agents-context-was-enough-then-proves-it-with-my-own-4214>
> Published: 2026-10-19 03:41:21+00:00

When I give an AI coding agent a task, I never actually know if the context I sent it was enough. So I built the check.

The stack is four small tools, all Python standard library, all MIT, all local:

A fifth tool, ghostworkflow, runs the chain in one command, and a small local web app puts a face on it.

I ran the whole thing on its own repo and published the result warts and all: verdict attention, 2 verify gaps, and a test failure that turned out to be a path assumption in one of my own tests. The first dogfood run also caught a real bug in the contained runner (it hid the user site-packages, so pytest vanished), which is fixed and shipped.

I am looking for 5 to 10 developers who use AI coding agents daily to run it on one of their own repos and tell me where it breaks or bores them. Quickstart is 6 clones and one command. Links and the case study: [https://gist.github.com/littlestjames82-sys/7e8309315c89706aefa97de6a4cc5399](https://gist.github.com/littlestjames82-sys/7e8309315c89706aefa97de6a4cc5399)

What I want to know most: did the verify verdict match what your agent actually struggled with?