0xSero/ai-data-extraction

extract all your personal data history from cursor, codex, claude-code, windsurf, and trae

What it solves

It provides a way to extract complete conversation histories, code contexts, and tool-use data from various AI coding assistants. This allows developers and researchers to gather high-quality, real-world interaction data for machine learning training and fine-tuning.

How it works

The toolkit consists of a series of Python scripts tailored to specific AI tools (such as Cursor, Claude Code, Windsurf, and Gemini CLI). These scripts automatically detect the operating system, search for common installation and storage paths, and parse data from various formats including SQLite databases and JSONL files. The extracted data is then standardized into a timestamped JSONL format containing user messages, AI responses, code snippets, and suggested diffs.

Who it’s for

It is designed for ML engineers and researchers who want to create training datasets from their own AI coding interactions, as well as users who want to archive their AI-assisted development history.

Highlights

  • Broad Tool Support: Extracts data from Cursor, Claude Code, Codex, Trae, Windsurf, Continue, Gemini CLI, and OpenCode.
  • Deep Context Extraction: Captures not just text, but file paths, line numbers, code diffs, and tool execution results.
  • Privacy Tools: Includes an optional privacy filter using the openai/privacy-filter model to redact sensitive information like emails and secrets.
  • Training Ready: Provides examples for direct fine-tuning and integration with the Unsloth library.
  • Skill Synthesis: Includes a utility to synthesize reusable "Agent Skills" from the extracted corpus using an LLM.

Related

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    A curated collection of reusable skills and playbooks for AI coding agents like Claude Code and Cursor, providing structured workflows for tasks like browser automation and codebase planning.