Grabset

Tools

• grabhub_build_image_set Build a LoRA-ready image set: relevant to the query, watermarks and near-duplicates removed, and spread out for variety (DINOv3 subject diversity).
• grabhub_describe_audio_dataset Return the fingerprint profile of one catalog dataset: card, facet distributions (language, bandwidth, noise, duration, sounds) and summary stats over its sampled clips.
• grabhub_route_datasets Rank whole Hugging Face datasets for a query, using their fingerprinted sample index. Returns, for each dataset, its repo, the config/split pairs that contain matching samples, and the best-matching indexed rows ("seeds"). The grabset Python client uses this to decide which rows to stream and embed on the fly.
• grabhub_search_audio_samples Search individual samples across Hugging Face datasets by what they contain. Audio is the first modality indexed (10 datasets); other modalities return no results yet. Each sample was fingerprinted with an audio-LLM caption, CLAP and Jina omni audio embeddings, AudioSet sound tags, Whisper language ID and signal probes (bandwidth, SNR). Results mix samples from different datasets and include pointers (repo, config, split, row_idx) that load the audio with the dataset viewer or `datasets`.
• grabhub_search_image_samples Search individual images across Hugging Face image datasets (styles, photography, characters, edit pairs) by what they show. Each result has a dense caption, structured attributes (medium, shot, camera angle, lighting, palette), aesthetic/NSFW/watermark scores, tags, and a pointer (repo, config, split, row_idx) to the source row.

No tool matches.

As the server's own listing gives them. We never call a tool.