Interesting approach. I am curious how this information stacks up over time and how efficient it is at incorporating decision knowledge into active context.

I have taken a different approach: allow team members to sync all of their Claude Code and Codex transcripts on a project and give them a skill that lets them ask their AI why decisions were made.

The skill I've built, /total-recall is backed by a Swift-based CLI that provides efficient query tooling that coding agents can use however they see fit to arrive at the answer.

The corpus of data contextify queries is a SQL database managed by macOS and Linux clients. These clients ingest the jsonl files in realtime and optionally can sync transcript data through either a hosted or self-hosted server.

This allows any team member to simply invoke the skill: "Why did we switch over to allauth from aws cognito? /total-recall."

My experience is that Claude Code and Codex don't just land "near" a decision, but can assemble it from what is sometimes a winding pathway of research, benchmarking and experimentation.

Rather than codify requirements into a separate spec, Contextify lets agents pair the state of the code with the conversational record.

I have just released the free personal and source available self-hosted version of Contextify, I'd be glad for feedback.

https://contextify.sh/teams/, https://contextify.sh/self-hosted/

So it seems to work quite well, as it’s been dogfooding itself for the past month… but only time will tell!