Export Claude and Codex conversations into privacy-safe datasets for Hugging Face.
npx skills add https://github.com/ychampion/codeclaw --skill codeclaw-ychampion
Please help me install this Agent Skill. Skill: codeclaw Source: https://github.com/ychampion/codeclaw/tree/main/docs Command: npx skills add https://github.com/ychampion/codeclaw --skill codeclaw-ychampion
Empower users to export Claude Code and Codex conversation histories as organized, privacy-safe training data.
Run the codeclaw export command to generate a dataset from your conversations and review the output before pushing it to Hugging Face.
High-intent search queries and answers about installing and using this skill.
You can export Claude conversation logs into a structured dataset by running the export command, which converts chat sessions into prepared data formatted for machine learning analysis and training resource sharing.
Yes, you can redact personal information before uploading to Hugging Face using built-in privacy management mechanisms. These scripts provide anonymization and review features to safeguard sensitive data and ensure PII is not exposed in shared datasets.
Yes, codeclaw supports exporting conversation histories from both Claude Code and Codex models. It facilitates managing this conversation data uniformly, ensuring privacy compliance and easy dataset sharing across different AI model platforms.
The best way to format chat histories for machine learning training data is using Python scripts that convert coding conversations into organized, privacy-safe datasets. This approach ensures structured formatting suitable for AI development and research teams.
Yes, you need the huggingface_hub dependency to push and share your exported conversation datasets. The Skill utilizes this library alongside pyyaml and json to facilitate uploading formatted data to the Hugging Face platform.
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