DGL Repo Skill

Use this repo skill when a task names DGL, Deep Graph Library, DGLGraph, GraphBolt, DGL-Go, DGL sparse, DGL distributed training, or graph neural network workflows implemented with DGL.

Quick Start

Install a public DGL package that matches the target backend and Python version, then verify imports before using deeper workflows:

python -m pip install dgl
python - <<'PY'
import dgl
print(dgl.__version__)
print(dgl.graph(([0, 1], [1, 2]), num_nodes=3))
PY

For PyTorch workflows that use GraphBolt, keep the installed torch version compatible with the DGL wheel's bundled GraphBolt library. If import dgl reports a missing libgraphbolt_pytorch_<version> file, read references/troubleshooting.md.

Route By Task

• Use graph-apis for dgl.graph, dgl.heterograph, canonical etypes, features, transforms, batching, subgraphs, to_block, and graph-level smoke checks.
• Use datasets-and-io for DGLDataset, CSVDataset, meta.yaml, graph save/load, dataset caches, download/cache environment variables, and CSV/OnDiskDataset handoffs.
• Use message-passing-training for update_all, dgl.function, PyTorch GNN layers, full-graph training loops, heterograph modules, readout, and dgl.sparse.
• Use dataloading-graphbolt for stochastic minibatch sampling, dgl.dataloading.DataLoader, NeighborSampler, GraphBolt ItemSet/ItemSampler, feature fetching, OnDiskDataset, GPU sampling, and UVA decisions.
• Use distributed-tools for partition configs, DistGraph, DistDataLoader, node_split, edge_split, ip_config.txt, dry launch-command construction, and safe distributed preflight.
• Use dglgo-cli for DGL-Go configure, recipe, train, export, configure-apply, apply, YAML config linting, and recipe/custom CSV workflows.

Shared References And Scripts

• Read references/install-and-build.md before source builds, backend selection, DGL-Go installs, or native library troubleshooting.
• Read references/troubleshooting.md for cross-cutting import, backend, GraphBolt, sparse, dataset cache, and build failures.
• Read references/repo-development.md when editing the DGL repository itself or selecting focused native tests.
• Read references/repo-provenance.md before deciding whether this skill is stale for a checkout.
• Run python scripts/check_dgl_environment.py for a safe import/backend/native-library diagnostic.

Routing Rules

• If the user asks for a DGL data folder or meta.yaml, route to datasets-and-io before any model or dataloader work.
• If the user asks for blocks, sampled minibatches, GraphBolt, or UVA, route to dataloading-graphbolt; then route model forward() details to message-passing-training.
• If the user asks for a cluster, partition JSON, ip_config.txt, or DistGraph, route to distributed-tools and do not run SSH/cluster launchers without explicit approval.
• If the user asks for a ready-to-run CLI experiment or YAML recipe, route to dglgo-cli; use datasets-and-io for custom CSV data validation and message-passing-training for exported script edits.
• If the request is only about constructing or debugging graph objects, stay in graph-apis and avoid training or dataset assumptions.

Safety Defaults

• Prefer CPU-safe synthetic smoke checks unless the user explicitly requests CUDA, distributed execution, downloads, or long training.
• Treat source builds and distributed launches as environment-mutating or side-effectful; summarize prerequisites and ask before broad builds, SSH, multi-host processes, or data dispatch.
• Do not rely on original DGL checkout docs, examples, tests, or tools at runtime. Use this skill's bundled references and scripts.