dataset-discovery

id	dataset-discovery
name	dataset-discovery
version	1.0.0
description	Multi-source ML dataset discovery.
stages	["survey","ideation","experiment"]
tools	["read_file","search_project","write_file","run_terminal"]
summary	Multi-source ML dataset discovery. Search HuggingFace Hub, OpenML, GitHub, and paper cross-references for datasets relevant to a research task. Use when asked to "find datasets for", "search ML datasets", "what datasets exist for", or "dis...
primaryIntent	data
intents	["data","research"]
capabilities	["search-retrieval","data-processing"]
domains	["data-engineering"]
keywords	["dataset-discovery","resource prep","search-retrieval","data-processing","data-engineering","dataset","discovery","multi","source","ml","search","huggingface"]
source	builtin
status	verified
upstream	{"repo":"dr-claw","path":"skills/dataset-discovery","revision":"8322dc4ef575affaa374aa7922c0a0971c6db7d7"}
resourceFlags	{"hasReferences":false,"hasScripts":true,"hasTemplates":false,"hasAssets":false,"referenceCount":0,"scriptCount":1,"templateCount":0,"assetCount":0,"optionalScripts":true}

dataset-discovery

Canonical Summary

Multi-source ML dataset discovery. Search HuggingFace Hub, OpenML, GitHub, and paper cross-references for datasets relevant to a research task. Use when asked to "find datasets for", "search ML datasets", "what datasets exist for", or "dis...

Trigger Rules

Use this skill when the user request matches its research workflow scope. Prefer the bundled resources instead of recreating templates or reference material. Keep outputs traceable to project files, citations, scripts, or upstream evidence.

Resource Use Rules

• Treat scripts/ as optional helpers. Run them only when their dependencies are available, keep outputs in the project workspace, and explain a manual fallback if execution is blocked.

Execution Contract

• Resolve every relative path from this skill directory first.
• Prefer inspection before mutation when invoking bundled scripts.
• If a required runtime, CLI, credential, or API is unavailable, explain the blocker and continue with the best manual fallback instead of silently skipping the step.
• Do not write generated artifacts back into the skill directory; save them inside the active project workspace.

Upstream Instructions

Dataset Discovery Skill

Overview

Search multiple ML dataset sources (HuggingFace Hub, OpenML, GitHub, Semantic Scholar) and return a ranked, deduplicated list of relevant datasets.

Agent Workflow

Phase 1: SCOPE

Clarify the user's needs before searching:

• Research task: What problem or domain? (e.g., "sentiment analysis", "medical image segmentation")
• Modality: image / text / tabular / audio / any
• Size preference: small (< 10K rows), medium (10K–1M), large (> 1M), any
• License preference: permissive (MIT/Apache/CC-BY), any, or specific

Phase 2: SEARCH

Run the search script with the user's query:

python3 scripts/search_ml_datasets.py search --query "<query>" --sources huggingface,openml,github,papers --max 30

Options:

• --sources: Comma-separated list from huggingface, openml, github, papers. Default: all four.
• --max: Maximum results to return after dedup + ranking. Default: 30.
• : Filter by modality (, , , ).