---

name: spektral description: "Helps with Spektral graph neural network workflows, from graph data loading and transforms to layers and models." disable-model-invocation: true metadata: disco-role: operating license: MIT

Spektral

Use this skill when the task mentions Spektral, graph neural networks, graph datasets, loaders, pooling, message passing, or ready-made graph models.

Quick install

• Public install: pip install spektral
• Spektral pulls in TensorFlow and the scientific Python stack declared in pyproject.toml.
• For this Spektral 1.3.1 source revision, prefer TensorFlow/Keras 2.x when model calls hit Keras 3 mask behavior errors; tensorflow==2.15.1 with keras==2.15.0 was verified for CPU workflows. Some older source paths also assume NetworkX/SciPy matrix-era APIs, so check the troubleshooting guide for dependency-version drift.
• For a fast sanity check, run scripts/check_install.py.
• If an editable install complains about multiple top-level packages, use a clean checkout or a source tree that exposes only the spektral package.

Quick import check

python -I -c "import spektral; from spektral.data import Graph, Dataset, SingleLoader, DisjointLoader, BatchLoader; from spektral.layers import GCNConv, GeneralConv"

Route by task family

graph-data

Use this route for graph containers, dataset classes, built-in datasets, transforms, loader selection, data-mode selection, and custom dataset pipelines.

Typical requests:

• "How do I define a custom Dataset?"
• "Which loader should I use for graph classification?"
• "How do I preprocess graphs for a GCN?"
• "Why is my dataset in mixed mode?"

Read:

• sub-skills/graph-data/SKILL.md
• sub-skills/graph-data/references/api-reference.md
• sub-skills/graph-data/references/workflows.md
• sub-skills/graph-data/references/dataset-catalog.md
• sub-skills/graph-data/references/troubleshooting.md
• sub-skills/graph-data/scripts/smoke_data_modes.py

gnn-models

Use this route for convolution layers, pooling layers, custom message passing, ready-made models, graph explanation, and utility ops around graph math and sparse tensors.

Typical requests:

• "How do I build a GCN/GNN?"
• "How do I write a custom MessagePassing layer?"
• "Why does batch mode need GraphMasking?"
• "How do I explain a prediction with GNNExplainer?"

Read:

• sub-skills/gnn-models/SKILL.md
• sub-skills/gnn-models/references/api-reference.md
• sub-skills/gnn-models/references/workflows.md
• sub-skills/gnn-models/references/layer-catalog.md
• sub-skills/gnn-models/references/troubleshooting.md
• sub-skills/gnn-models/scripts/smoke_models.py

When to read the shared references

• references/api-overview.md for the package module map and ownership split.
• references/workflows.md for the root-level workflow map and routing hints.
• references/troubleshooting.md for install/import, dataset download, mode mismatch, and masking issues.
• references/repo-provenance.md to check whether this skill still matches the current repository snapshot.

What this skill covers

• Graph objects, dataset containers, and loader behavior.
• Built-in benchmark datasets and transform pipelines.
• Data-mode choices: single, disjoint, batch, and mixed.
• Convolution, pooling, message passing, and readout layers.
• Ready-made models such as GCN and GeneralGNN, plus GNNExplainer.
• Utility helpers for sparse tensors, graph math, logging, and serialization.

What this skill does not do

• It does not replace TensorFlow or general Keras guidance.
• It does not cover DGL or PyG.
• It does not require the original repository checkout at runtime; the bundled references and scripts should be enough.

If you are unsure where a request belongs

• Start with graph-data if the pain point is shapes, loaders, datasets, transforms, or download/configuration.
• Start with gnn-models if the pain point is layer behavior, pooling, masking, message passing, or model architecture.
• If the task crosses both, read the graph-data route first when the user is defining inputs, and the gnn-models route first when the user is defining the network.