This is mostly solved just by writing proper commit messages: https://blog.br11k.dev/2026-03-23-code-review-bottleneck-par...

Much more interesting part is how exactly you map Context/Why/Verify to a product spec / acceptance criterions.

And I already posted how to do this. SCIP indexes from product spec -> ACs -> E2E tests -> Evidence Artifacts -> Review (approve/reject, reason) -> if all green then we make a commit that has #context + #why + #verify (I believe this is just points to e2e specs that belong to this AC)

Here's full schema: https://tinyurl.com/4p43v2t2 (-> https://mermaid.ai/live/edit)

What I'm trying to visualize is exactly where the cognitive bottleneck happens. So far I've identified three edges:

1. Spec <-> AC (User can shorten URL -> which ACs make this happen?)

2. AC <-> Plan (POST /urls/new must create new DB record and respond with 200) -> how exactly this code must look like?

3. Plan/Execute/Verify -> given this E2E test, how can I verify that test doing what AC assumes?

The cognitive bottleneck is when we transforming artifacts:

- Real world requirements (user want to use a browser) -> Spec (what exactly matters?)

- Spec -> AC (what exactly scenarios we are supporting?)

And you can see on every step we are "compressing" something ambiguous into something deterministic. That's exactly what is going on in Engineer's head. And so my tooling that I'm gonna release soon is targeted exactly to eliminate parts that can we spend most time on: "figuring out how this file connects to the Spec I have in my head, that I built from poorly described commit messages, outdated documents, Slack threads from 2016, and that guy who seemingly knowed everything before he left the company".