Set up conditions for PINA problems. Covers data types (LabelTensor, Graph, PyG Data), time series conditions, binding equations to domains, and data-driven input→target mapping.
Entry point for creating PINA problems. Routes to sub-skills based on problem type (data-driven vs physics-driven), problem class selection, domain setup, equations, conditions, and discretisation.
Create, modify, and improve PINA skills. Use when the user wants to create a new skill for PINA development guidance, edit an existing PINA skill, or add cross-tool compatibility (opencode, Claude Code, Codex CLI) to a skill. Covers skill directory structure,…
Create and manipulate domains for PINA physics-driven problems. Covers domain types (Cartesian, Ellipsoid, Simplex), set operations, partial/update methods, and domain discretisation.
Define PDEs, ODEs, boundary conditions, and custom equations for PINA physics-driven problems. Covers the equation zoo, custom equation functions, and inverse problem equation signatures.
Orchestrates a complete session from problem definition through trained solver. This is the entry point for solving differential equations or physics problems with neural networks. Use when the user has a vague open-ended request like "I have a differential…
Guides users through selecting, configuring, or creating a PyTorch model for a PINA problem. Use when the user asks about models, architecture, neural network choice, or how to build/choose a model for their PINA problem. Also triggers on phrases like "what…
Guides users through selecting the right PINA solver for their problem, or creating a custom solver when no built-in fits. Use when the user asks "what solver should I use", "how do I train this", "which solver", "choose a solver", "pick a solver", "solver…