skills.rest
peteromallet
peteromalletCOMMUNITY
Explore all skills by this author →
𝕏@peteromalletGitHub

dataclaw

Export multi-source CLI conversations to Hugging Face datasets with privacy checks.

2.1k|233|Updated Feb 24, 2026
Search Tags:#workflow#cli#export#PII#datasets#huggingface
One-click install
npx skills add https://github.com/peteromallet/dataclaw --skill dataclaw-peteromallet
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: dataclaw
Source: https://github.com/peteromallet/dataclaw/tree/main/docs
Command: npx skills add https://github.com/peteromallet/dataclaw --skill dataclaw-peteromallet

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

This skill automates exporting Claude Code, Codex, Gemini CLI, OpenCode, and OpenClaw conversation histories to Hugging Face datasets, streamlining data sharing while enforcing privacy safeguards.

Core Features & Use Cases

  • Exports multi-source conversations to Hugging Face datasets, enabling easy publishing and collaboration.
  • Includes built-in privacy controls and PII review prompts to help redact sensitive information before export.
  • Use Case: teams exporting agent conversations for model evaluation or dataset curation, with audit trails and step-by-step onboarding.

Quick Start

Run dataclaw status and follow the produced next_steps to begin.

Frequently Asked Questions about dataclaw

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I export Claude Code and Gemini CLI conversations to a Hugging Face dataset?▼

You can export Claude Code and Gemini CLI conversations to a Hugging Face dataset using a CLI-driven workflow with stage tracking and explicit gating. The process generates JSON outputs for structured automation across multiple conversation sources.

Can I redact PII before exporting chat histories to Hugging Face?▼

You can redact PII before exporting chat histories because the workflow includes built-in privacy controls and PII review prompts. These safeguards help you redact sensitive information from conversation histories before publishing to Hugging Face datasets.

Does this conversation export workflow support OpenCode and Codex sources?▼

Yes, the conversation export workflow supports OpenCode and Codex sources alongside Claude Code, Gemini CLI, and OpenClaw. It processes these multi-source conversations into a unified Hugging Face dataset with audit trails and step-by-step onboarding.

What is the best way to automate publishing agent conversations to Hugging Face?▼

The best way to automate publishing agent conversations to Hugging Face is using a CLI workflow that provides explicit gating via a confirm step and JSON outputs. This enables structured automation for teams curating datasets for model evaluation.

Do I need to manually review conversations before exporting them to Hugging Face datasets?▼

You do not need to manually review conversations outside the workflow because it applies explicit gating via a confirm step with PII review prompts. This enforces privacy safeguards to help redact sensitive information before the dataset is published.

Related Skills

Explore More Software Engineering Skills→
MoRen9527MoRen9527

dataclaw

Export conversation histories from AI coding tools to Hugging Face datasets.

Community
Advanced
NagyViktNagyVikt
2

huggingface-vision-trainer

Fine-tune computer vision models on Hugging Face Jobs infrastructure.

Community
Advanced
Aniket-a14Aniket-a14
1

hugging-face-model-trainer

Train language models with TRL on Hugging Face Jobs.

Community
Advanced
black-ytblack-yt
7

huggingface-dataset-publishing

Publish Hugging Face datasets with image and metadata handling.

Community
Advanced