Sometimes you need proof. Sometimes you need a paper trail. Sometimes you just want to remember.
Introduction
You've just finished an incredible Claude Code session. Three hours of back-and-forth that transformed a vague idea into a working implementation. There were wrong turns, clever solutions, debugging breakthroughs, and architectural decisions. It was a journey.
Now that session is... gone? Buried in some log file? Inaccessible? All that context, all those decisions, all that reasoning—evaporated into the digital ether.
Or you're in a code review, defending a design decision. "Why did we implement it this way?" You know Claude helped you think through the tradeoffs, but you can't show your work. The conversation that led to this architecture is locked inside a past session.
/export changes this. Every prompt, every response, every tool call—exported to clean markdown. Your AI interactions become first-class documentation.
The Problem
Claude Code sessions contain more than code. They contain:
- Decision rationale: Why you chose this approach over alternatives
- Debugging journeys: The process of identifying and fixing issues
- Architectural discussions: Tradeoffs considered, options weighed
- Learning moments: Explanations that helped you understand something new
- Failed attempts: What didn't work and why (valuable for not repeating mistakes)
Without export, this is ephemeral. The session ends, the context is lost, and you're left with only the final code—stripped of the reasoning that created it.
For teams, this matters even more. How do you share what you learned in a Claude session? How do you train others on effective prompting? How do you document AI-assisted decisions for compliance or auditing?
The Solution
/export dumps your entire conversation to a markdown file, preserving the complete record of your Claude Code session.
How to Use It
Basic Export
At any point during a session:
/export
Claude saves the entire conversation to a markdown file:
Exported conversation to: ./claude-session-2024-01-21-14-32.md
Specify Output Location
Control where the export goes:
/export ./docs/sessions/auth-refactor.md
Export Format
The generated markdown preserves everything:
# Claude Code Session
**Date**: 2024-01-21 14:32:05
**Duration**: 2h 34m
**Model**: Claude Sonnet
---
## User
I need to refactor the authentication module to support OAuth2 in addition to our current JWT approach.
---
## Claude
I'll help you add OAuth2 support while maintaining backward compatibility with your existing JWT authentication. Let me first analyze your current auth implementation.
**Tool Call**: read_file
- Path: src/auth/jwt.ts
- Result: [file contents]
Based on your current implementation, I recommend...
---
## User
What about the token refresh flow?
---
## Claude
Great question. For OAuth2, we have several options for handling token refresh...
[conversation continues]
What's Included
Every export contains:
- All user prompts (exactly as typed)
- All Claude responses (complete text)
- All tool calls (what tools were used, with what parameters, and results)
- Timestamps for each exchange
- Code blocks with syntax highlighting preserved
- Error messages and recovery steps
Pro Tips
Export Before Long Sessions End: If you're in a valuable session and worried about losing context, /export mid-session. You can always export again at the end for the complete record.
Create Session Templates: Export particularly good sessions and use them as templates for similar future work. "Here's how we approached the last API integration—let's follow similar patterns."
Combine with Version Control: Export sessions related to specific features and commit them alongside the code:
feature-branch/
├── src/
│ └── new-feature.ts
└── docs/
└── sessions/
└── new-feature-development.md
Search Your Exports: Keep exports in a dedicated folder. When you remember solving something but not the details, grep through your session history:
grep -r "OAuth" ./docs/sessions/
Redact Sensitive Information: Before sharing exports, review for API keys, passwords, or proprietary information that may have appeared in the conversation.
Real-World Use Case
Scenario: You're a consultant implementing a complex data pipeline for a client. They need documentation of all AI-assisted decisions for their audit trail.
The Challenge: The project involves multiple Claude sessions over several weeks. Each session includes design decisions, implementation details, and troubleshooting. The client's compliance team needs to understand how AI was used in the project.
Your Workflow:
- Start each working session with clear context:
Starting pipeline design session for ETL component.
Requirements: handle 1M records/day, maintain data lineage, support rollback.
- Export at the end of each session:
/export ./client-docs/sessions/etl-design-$(date +%Y%m%d).md
- Create summary documentation from exports:
/export-summary ./client-docs/sessions/ --output ./client-docs/AI-USAGE-REPORT.md
The Result: Complete audit trail. Every AI interaction documented. Every decision traceable. The client's compliance team can see exactly how AI contributed to the project, what was asked, what was suggested, and what was implemented.
Conclusion
/export transforms ephemeral conversations into permanent records. Your Claude Code sessions become documentation, training material, audit trails, and institutional memory.
The next time someone asks "how did you figure that out?"—you can show them. The next time you forget why you made a decision—you can look it up. The next time you need to prove you explored alternatives—you have the receipts.
Tomorrow, we're exploring a feature that brings parallelism to your Claude Code workflows. Day 25 introduces Subagents—how Claude delegates specialized tasks to independent AI workers, each with their own 200K context window. Divide and conquer, AI style.
Follow along with the series to discover a new Claude Code feature every day. What would you use conversation exports for? Share your ideas in the comments!
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