DataClaw0 introduces an agentic paradigm for actively refining multimodal data to align with user and downstream intents. It uses a two-stage pipeline with factual anchors to generate a large-scale dataset across five domains and achieves strong alignment via supervised fine-tuning and GRPO. Evaluated on video generation, VQA, and GUI navigation, DataClaw0 produces high-information-density data, enabling efficient model adaptation with minimal training data.