/dataset-discovery

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 "discover training data for".

openlair@openlair

GitHub

From plugin

dr-claw

1k173 skills8 agents

Install

$ npx -y skills add OpenLAIR/dr-claw --skill dataset-discovery --agent claude-code

How it fires

How this skill gets triggered: by you, by Claude, or both.

Fires itselfAuto-invocation. Claude auto-loads it when your prompt matches the work.
You can call itInvoke it directly when you want it.
Slash command/dataset-discovery

Context preview

The summary Claude sees to decide when to auto-load this skill.

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 "discover training data for".

SKILL.md

dataset-discovery.SKILL.md

name: dataset-discovery
description: >
  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 "discover training data for".

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.
`--modality`: Filter by modality (`image`, `text`, `tabular`, `audio`).
`--workspace`: Output directory. Default: `./datasets/discovery/`

Optionally also call HF MCP tool `hub_repo_search` with `repo_types: ["dataset"]` for semantic search to supplement results.

Phase 3: PRESENT

Show results as a markdown table:

| Name | Source | Downloads | Size | License | Tags | URL | |------|--------|-----------|------|---------|------|-----|

Sort by relevance score (highest first).

Phase 4: DETAIL

When the user wants more info on a specific dataset:

python3 scripts/search_ml_datasets.py detail --dataset-id "huggingface:stanfordnlp/imdb" --workspace ./datasets/discovery/

Writes `metadata.json` and `README.md` to `{workspace}/datasets/{source}_{slug}/`.

Phase 5: PULL

When the user wants to preview data:

python3 scripts/search_ml_datasets.py pull --dataset-id "huggingface:stanfordnlp/imdb" --sample-rows 20 --workspace ./datasets/discovery/

Writes `sample.jsonl` to `{workspace}/datasets/{source}_{slug}/`.

For full dataset download, confirm with the user first, then use `huggingface-cli download` or equivalent.

Workspace Layout

{workspace}/                         # default: ./datasets/discovery/
  search-{YYYY-MM-DD}.json           # search results log
  datasets/
    {source}_{slug}/
      metadata.json                  # detailed metadata
      README.md                      # human-readable summary
      sample.jsonl                   # sample rows