Name: Co Scientist Graph Neural Networks
Author: nahisaho

Graph neural networks

Graph neural networks skill. GNN architectures (GCN/GAT/GraphSAGE), molecular graph learning, knowledge graph embeddings, and graph-level prediction tasks.

Use This Skill When

GNN architectures (GCN/GAT/GraphSAGE).
Molecular graph learning.
Knowledge graph embeddings.
Graph-level prediction tasks.

Required Inputs

Research objective, decision target, or hypothesis.
Available data, source constraints, and domain assumptions.
Required outputs, success metrics, and deadline or reproducibility constraints.

Workflow

Confirm scope, assumptions, and the exact artifact set to save.
Apply the narrowest domain method that answers the request with defensible evidence.
Save code, tables, figures, and intermediate outputs to files instead of chat-only output.

Graph neural networks

Graph neural networks skill. GNN architectures (GCN/GAT/GraphSAGE), molecular graph learning, knowledge graph embeddings, and graph-level prediction tasks.

Use This Skill When

GNN architectures (GCN/GAT/GraphSAGE).
Molecular graph learning.
Knowledge graph embeddings.
Graph-level prediction tasks.

Required Inputs

Research objective, decision target, or hypothesis.
Available data, source constraints, and domain assumptions.
Required outputs, success metrics, and deadline or reproducibility constraints.

Workflow

Confirm scope, assumptions, and the exact artifact set to save.
Apply the narrowest domain method that answers the request with defensible evidence.
Save code, tables, figures, and intermediate outputs to files instead of chat-only output.

Co Scientist Graph Neural Networks

Graph neural networks

Use This Skill When

Required Inputs

Workflow

Co Scientist Graph Neural Networks

Graph neural networks

Use This Skill When

Required Inputs

Workflow

Deliverables

Quality Gates

Gotchas

Validation Loop

Continuous Learning V2

Continuous Learning V2

Continuous Learning V2

Continuous Learning

Continuous Learning

Pytorch Patterns