---

name: ogb description: "Routes OGB graph dataset, evaluator, conversion, and export workflows." metadata: disco-role: operating disable-model-invocation: true license: MIT

OGB

Use this skill for the Open Graph Benchmark package when the task is about loading OGB datasets, choosing an evaluator, converting molecules or code into OGB graphs, or packaging a new OGB-style dataset.

Read this skill when

• The request names ogb, ogbg-*, ogbn-*, ogbl-*, PCQM4M, MAG240M, WikiKG90M, or DatasetSaver.
• The task asks for GraphPropPredDataset, NodePropPredDataset, LinkPropPredDataset, PCQM4MDataset, PCQM4Mv2Dataset, MAG240MDataset, WikiKG90MDataset, or the matching evaluator.
• The task mentions smiles2graph, py2graph, meta_dict.pt, mapping/, split_dict.pt, test-dev, test-challenge, or OGB submission files.
• The task asks for PyG or DGL dataset wrappers and the corresponding backend packages are installed.

Install

python -m pip install -e .

If you need the molecular helper, install rdkit as well. If you need the PyG or DGL wrapper classes, install the matching optional backend package too.

Start here

1. Read references/api-overview.md for the public class map and the common loader/evaluator shapes.
2. Read references/installation.md for the package install command and the optional backend notes.
3. Read references/dataset-catalog.md when you need the exact dataset names or metrics.
4. Read references/troubleshooting.md when imports fail, downloads are interrupted, or shapes do not match.

Route by task family

• Use sub-skills/graph-property-prediction/SKILL.md for ogbg-*, molecular graph workflows, smiles2graph, and ogbg-code2.
• Use sub-skills/node-property-prediction/SKILL.md for ogbn-* datasets and node-level evaluators.
• Use sub-skills/link-property-prediction/SKILL.md for ogbl-* datasets, ranking metrics, and knowledge-graph completion.
• Use sub-skills/lsc-benchmarks/SKILL.md for PCQM4M, MAG240M, WikiKG90M, submission files, and large-scale benchmark notes.
• Use sub-skills/dataset-contribution/SKILL.md for DatasetSaver and OGB-compatible dataset packaging.

Quick install check

After installing the package, run the bundled smoke helper:

python scripts/check-install.py

If you need the molecule helper, also run:

python scripts/smiles2graph-smoke.py

What this skill does not do

• It does not tell you to run the original repository's heavy training examples as the runtime skill.
• It does not depend on the source checkout remaining available once the skill is generated.
• It does not treat the vendored WikiKG90M external framework tree as bundled runtime content.

Useful bundled helpers

• scripts/check-install.py: verifies the installed package, optional backend presence, and core imports.
• scripts/list-datasets.py: prints the dataset catalog from the installed package metadata.
• scripts/smiles2graph-smoke.py: runs a tiny molecule-to-graph smoke check.

Provenance and routing

Read references/repo-provenance.md to see which repo state this skill was distilled from, and references/repo-routing-metadata.json for the router scenario metadata used during import.