Technical Context
I wouldn't build the process around the hope that Claude Code will perfectly read someone else's context on its own. For a proper AI integration between agents, I prefer shifting the chat memory directly into the repository files. This way, sessions don't break down, and the agent doesn't have to rescan the entire project for every minor task.
The discussion highlighted three common practices: thread IDs from Codex, running init, and keeping context in CLAUDE.md or AGENTS.md. Out of these, I would confidently bet on CLAUDE.md as the main anchor point, since this specific project context file is backed by Claude Code best practices. However, transferring a Codex thread ID directly to Claude as an official feature is more of a community hack than a guarantee.
If I were connecting Claude Code to a live project, I would create at least two files. The first is CLAUDE.md, featuring a brief architectural overview, commands, constraints, and rules. The second is a handoff note detailing what's already done, which files were modified, what's currently broken, and the immediate next steps.
The content should be brief and to the point. Not a repository dump, but only what the agent actually needs to get started: the tech stack, structure, test/lint/dev commands, critical business rules, restrictions like "don't touch auth", and a few known edge cases. Bloating the file with endless details will only ruin its effectiveness.
I would also keep the prompt simple. Something along the lines of: analyze the project, create a plan first, then update CLAUDE.md and generate handoff.md with the current state; do not modify any code without my confirmation. This is usually more than enough unless the repository is complete chaos.
What This Changes for Business and Automation
The most notable benefit here isn't just convenience, but speed. When the context is stored in the repo, I can easily switch between Codex, Claude Code, and Cursor without wasting tokens on rescanning the entire codebase every single time. For AI implementation within a team, this is a massive saver of time and nerves.
Teams with complex, long-term tasks and multiple active agents win the most. On the other hand, teams that keep their solutions solely in chat logs lose, constantly wondering why a new agent is suggesting rewriting half the project again.
At Nahornyi AI Lab, we usually package these workflows into a ready-to-use setup: context files, handoff notes, validation rules, and short prompts optimized for specific tasks. If you're losing valuable time manually switching between agents, it's time to refine your workflow and build AI automation so that your next AI assistant doesn't start with total amnesia.