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JunMA98JunMA98/Computer-science-claude-skills/skills/torch-geometric/SKILL.md

torch-geometric

Skill3 starsChanged 8 months agoMITView on GitHub

Build graph neural network workflows with PyTorch Geometric for CS research. Use when training GNNs for citation networks, knowledge graphs, recommender graphs, code graphs, program analysis, heterogeneous graphs, node or graph classification, or link prediction with scalable mini-batching and graph transforms.

11 other projects use a version of this.

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Most recently taken up by

Used with

Projects with it also use usfiscaldata, deeptools and 10 more.

Other files people use alongside this one.

Files found in the same public projects as a version of this one, more often than they turn up anywhere else. Each project counts once, large collections count for less, and this file's own repository is left out.
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The file

We split this skill into the rules it gives an agent

4 rules. 0 are common, 4 are distinctive, and 0 are very specific to this project.

  • 0

    Common

    Most of the 11 edited versions we read keep these.

  • 4

    Distinctive

    This author's own choices. Worth a look.

  • 0

    Very specific

    Tied to their project's names, files or tools. Probably skip.

  • Common
  • Distinctive
  • Very specific
  1. Distinctive:

    Use this skill when the task is graph representation learning rather than general graph analytics.

    line 8
  2. Distinctive:

    Check whether edge direction, heterogeneity, and sampling strategy affect the conclusion.

    line 30
  3. Distinctive:

    Use graph visualization only to clarify structure, not as proof of performance.

    line 32
  4. Distinctive:

    When the user asks for PyG help without more detail, return:

    line 36

“Common” is measured: kept by at least 6 in 10 of the 11 edited versions of this file we could read. “Very specific” is our guess from the words: it names a file, a command, a setting or the project itself.

Keep what fits and drop the rest. Your version, ready for your agents.
Files
SKILL.md

PyTorch Geometric

Use this skill when the task is graph representation learning rather than general graph analytics.

Common CS Uses

  • node classification on citation or dependency graphs
  • graph classification for program, document, or interaction structures
  • link prediction for recommendation or knowledge graph completion
  • heterogeneous graph learning
  • graph mini-batching, sampling, and transform pipelines

Local Resources

  • references/datasets_reference.md
  • references/layers_reference.md
  • references/transforms_reference.md
  • scripts/create_gnn_template.py
  • scripts/benchmark_model.py
  • scripts/visualize_graph.py

Working Rules

  • Define graph construction and supervision targets explicitly.
  • Check whether edge direction, heterogeneity, and sampling strategy affect the conclusion.
  • Compare against strong non-GNN baselines when appropriate.
  • Use graph visualization only to clarify structure, not as proof of performance.

Default Output

When the user asks for PyG help without more detail, return:

  1. task formulation
  2. suitable model family or layer choices
  3. data pipeline considerations
  4. evaluation and baseline notes

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