To transfer Custom GPTs to Claude Projects, export your GPT’s system prompt, knowledge files, and conversation starters, then recreate them as Claude Project resources in Claude.ai or as a CLAUDE.md file for Claude Code. The process has five steps: export the GPT configuration, organize your files locally, rebuild in Claude Projects, recreate conversation starters as prompt templates, and test for behavior parity. There is no direct migration tool, so each component must be extracted and reconstructed manually.

Understanding What You Are Migrating

ChatGPT Custom GPTs store several components: the system prompt, user instructions, conversation starters, and optional uploaded files (knowledge base). Claude Projects work differently—instead of a packaged “GPT,” you create project contexts by adding instructions, files, and specifications directly in Claude.ai or through Claude Code projects. The migration essentially involves extracting each component from your GPT and recreating it as a project resource.

Before starting, identify which GPTs you want to transfer and their purpose. Not every GPT needs migration; focus on those containing valuable custom instructions, specialized knowledge, or workflows you rely on frequently.

Step 1: Export Your Custom GPT Configuration

ChatGPT does not provide a one-click export for Custom GPTs, so you need to manually extract the configuration. Open your Custom GPT in the ChatGPT interface and note the following details:

System Instructions: This is the core prompt defining your GPT’s behavior. Copy this text exactly as written. Look for the “Instructions” or “Configure” section in the GPT editor.

Knowledge Files: Any documents, PDFs, or text files you uploaded become part of the GPT’s knowledge base. Download these individually from the GPT configuration page. If you have many files, you may need to access them through your ChatGPT account’s data storage.

Conversation Starters: These are the example prompts shown when users start a new conversation with your GPT. Note these down as you may want to recreate them as project-specific shortcuts.

Capabilities: Check which capabilities your GPT uses—web browsing, image generation, or code interpreter. Claude Projects have different capability sets, so note these for reference.

For developers comfortable with the ChatGPT API, you can also retrieve GPT configurations programmatically:

import openai

client = openai.OpenAI(api_key="your-api-key")

# List your GPTs
gpts = client.beta.assistants.list(limit=100)

for gpt in gpts.data:
    print(f"GPT ID: {gpt.id}")
    print(f"Name: {gpt.name}")
    print(f"Instructions: {gpt.instructions[:200]}...")
    print("---")

This approach works if you have converted your Custom GPTs to Assistants API format, which gives you programmatic access to instructions and file IDs.

Step 2: Organize Your Resources

Create a local folder structure to hold your extracted resources:

my-gpt-migration/
├── gpt-name-1/
│   ├── instructions.md
│   ├── knowledge/
│   │   ├── file1.txt
│   │   └── file2.pdf
│   └── conversation-starters.md
└── gpt-name-2/
    └── ...

This organization mirrors how Claude Projects work—you can reference local files directly when working with Claude Code. Having a clean folder structure makes the recreation process much smoother.

Step 3: Recreate in Claude Projects

With your resources organized, you can now build equivalent functionality in Claude Projects. There are two primary approaches:

Using Claude.ai Projects

  1. Create a new Project in Claude.ai
  2. Add your GPT’s instructions as the project context. Paste the full system prompt into the project description or instructions field.
  3. Upload the knowledge files you downloaded from ChatGPT. Claude.ai Projects support file uploads that Claude can reference during conversations.
  4. Test the project by asking questions that should trigger the behaviors from your original GPT.

Using Claude Code (CLI)

For developers who prefer working locally, Claude Code offers project-based contexts. Create a CLAUDE.md file in your project folder:

# Project Context

You are [GPT Name], a specialized assistant that [core purpose].

## Instructions

[Paste your full GPT instructions here]

## Capabilities

- [List capabilities]
- [Be specific about behavior]

## Knowledge

This project has access to files in the ./knowledge/ directory.

Then reference your knowledge files in the ./knowledge/ folder. When you run Claude Code in that directory, it automatically loads the context:

cd my-project
claude

The CLI reads CLAUDE.md and makes your knowledge files available during conversations.

Step 4: Preserve Conversation Starters

ChatGPT conversation starters are essentially quick-prompt templates. In Claude Projects, you can recreate these as reusable prompts. Create a prompts/ folder with markdown files for each starter:

prompts/
├── analyze-code.md
├── debug-issue.md
└── explain-concept.md

When starting a new conversation, copy the relevant prompt template into your message. Alternatively, in Claude Code, you can create shell aliases:

# Add to your .bashrc or .zshrc
alias claude-analyze="claude -p 'Analyze the following code for issues:'"

Step 5: Test and Iterate

After recreating your GPT as a Claude Project, thorough testing reveals gaps between the original behavior and the new implementation. Ask the same questions you would ask your Custom GPT and compare responses. Common adjustments include:

Automating the Process for Multiple GPTs

If you manage multiple Custom GPTs, consider building a migration script:

import os
import json

def create_claude_project(gpt_name, instructions_file, knowledge_folder):
    """Create a Claude Project structure from exported GPT data."""
    project_folder = f"./claude-projects/{gpt_name}"
    os.makedirs(project_folder, exist_ok=True)
    
    # Create CLAUDE.md from instructions
    with open(instructions_file, 'r') as f:
        instructions = f.read()
    
    claude_md = f"""# {gpt_name}

## Instructions
{instructions}

## Knowledge
Knowledge files are stored in ./knowledge/
"""
    
    with open(f"{project_folder}/CLAUDE.md", 'w') as f:
        f.write(claude_md)
    
    # Copy knowledge files
    knowledge_dest = f"{project_folder}/knowledge"
    os.makedirs(knowledge_dest, exist_ok=True)
    
    for filename in os.listdir(knowledge_folder):
        src = os.path.join(knowledge_folder, filename)
        if os.path.isfile(src):
            dst = os.path.join(knowledge_dest, filename)
            os.system(f"cp '{src}' '{dst}'")
    
    print(f"Created Claude Project: {gpt_name}")

# Usage
create_claude_project(
    gpt_name="my-code-assistant",
    instructions_file="./my-gpt-migration/gpt-name-1/instructions.md",
    knowledge_folder="./my-gpt-migration/gpt-name-1/knowledge"
)

This script automates the folder creation and file copying, letting you focus on refining the instructions.

Summary

Transferring Custom GPTs to Claude Projects involves exporting your GPT configuration manually, organizing the extracted files, and recreating the functionality using Claude’s project context system. The process requires some upfront effort, but the result is a more developer-friendly workflow with local file access and CLI integration. Start with your most-used GPTs, test thoroughly, and iterate on the instructions until the behavior matches your expectations.

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