Carry AI Coding Context Between Sessions with a Markdown Handoff

An AI coding session can end with a working patch and still be hard to continue. The code shows what changed. The conversation may contain the rest: why an approach was rejected, which command failed, what a test actually verified, and where the investigation stopped.

That context matters when you hand work to another person, start a fresh task, or switch tools.

Session Exporter is an open-source plugin from Tor Production that turns the accessible context of a ChatGPT or Codex conversation into a Markdown handoff. It packages instructions as a skill, with no MCP server or account connection of its own.

Its default scope includes the complete accessible transcript, organized context, and an integrity report. The useful part is keeping decisions connected to the evidence behind them.

Preserve the decision and the evidence

A handoff should let the next reader answer four questions:

• What are we trying to do? The goal, constraints, and current state.
• Why did we choose this approach? Decisions and relevant failed attempts.
• What was verified? Files, commands, test results, or sources that support the conclusion.
• What remains open? Unfinished work, missing information, and the next useful step.

Session Exporter's structure adapts to the work. A debugging session may need repository state and test output; a research session may need sources and unresolved questions. Inapplicable sections are omitted.

The transcript remains useful when the handoff needs checking. A note that says “tests passed” is much easier to assess when the underlying command and visible result are preserved.

A small example

Here is a conceptual excerpt from a queue-debugging handoff. It illustrates the information worth preserving; it is not an actual plugin export or a verified test result.

Goal: prevent completion after a worker's lease expires.
Decision: check ownership and expiry within the write transaction.
Verified: a deterministic test covers expiry while waiting for a lock.
Failed attempt: a sleep-based test was flaky in CI.
Next step: add the regression case to the acceptance suite.
Missing: behavior under production traffic has not been measured.

The next session now has a specific place to resume. It also knows which conclusion still needs evidence.

A real export should preserve the commands, results, and wording available in the session. If a detail cannot be retrieved, the document should mark that gap.

Try it in a Codex task

Open the public Plugins Directory through the project's installation guide, find Session Exporter by Tor Production, and install it.

Start a new Codex task after installation so the skill is available. This follows the documented plugin installation flow.

When that task is ready for a handoff, ask:

Use $session-exporter to export this entire conversation
to Markdown for handoff.

The default filename combines session_export, a timestamp, and a short context suffix. Review the file, then supply it to the next person or tool that can read Markdown. Include access to the relevant repository or files when continuing the work requires them.

The README also documents a Git marketplace installation route and a copy-paste fallback for chats where the installed skill is unavailable.

Know what the file can carry

The export can only contain information the current session can access. An earlier message that is no longer retrievable remains a gap, even if it once existed in the conversation. The integrity report records unavailable material, omissions, and redactions.

The public skill instructions exclude hidden system instructions and private chain-of-thought, and require redaction of visible secrets. Review the generated file before sharing it: that requirement is not a guarantee of perfect secret detection.

The plugin adds no analytics, automatic upload, or synchronization. The export stays in the current environment unless you send it elsewhere. A Markdown handoff also does not transfer permissions or give the receiving tool access to your accounts.

Use it where continuity matters

This approach is useful for a paused investigation, a review passed to another agent, or a project that spans several sessions. A short summary may be enough for a small, self-contained task.

The practical check is: can the next reader identify what was verified and what to do next? If a claim lacks evidence, add the source or mark it unresolved.

The repository contains the implementation, installation options, and full export instructions.

AI disclosure: This article was drafted and edited with AI using the project's documentation. The debugging excerpt is illustrative. No measured time, token, or quota savings are claimed.