pina-workflow

name	pina-workflow
description	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 equation", "I want to solve a PDE", "I have a physics problem", "I need to model a system", "help me solve an ODE", "I want to use neural networks for my equations", "I have some data and equations", or "let's build physics-informed neural networks". Also triggers on mentions of specific equations (Navier-Stokes, Burgers, Poisson, heat equation, wave equation, etc.) or when the user describes a physical system they want to simulate. This skill will guide the user through the full workflow step by step, loading the right sub-skills at the right time.
license	MIT
compatibility	opencode, codex, claude
metadata	{"audience":"users","workflow":"pina-workflow"}

PINA Workflow — End-to-End Agentic Session

[!IMPORTANT] Read RULES.md before using this skill — it applies to all skills.

Orchestrates a full PINA session. Does not duplicate sub-skill content — each stage below requires actually reading the referenced file(s) in full, not acting from memory, summary, or a prior pass over this conversation.

Hard rule for every stage

Before doing anything in a stage:

1. Read every file path listed for that stage, in full, with the view tool.
2. If a stage lists multiple sub-skills, read all of them — the list is mandatory, not illustrative.
3. Do not advance to the next stage until the current stage's checklist (below) is satisfied.
4. If you already read a file earlier in this session and the file hasn't changed, you may skip re-reading it — but only if you can state which stage/turn you read it in. If in doubt, re-read.

Pipeline

Stage 1 — Problem definition    → create-problem
  ├── Domain creation           →   define-domains
  ├── Equation definition       →   define-equations
  └── Condition binding         →   condition-setup
Stage 2 — Model selection       → select-model
Stage 3 — Solver selection      → select-solver
Stage 4 — Trainer configuration → select-trainer

Stage 1 — Problem definition

Read ../create-problem/SKILL.md in full now.

create-problem itself delegates to three sub-skills. Read all three in full, in this order, following whatever instructions create-problem gives for sequencing:

• ../define-domains/SKILL.md
• ../define-equations/SKILL.md
• ../condition-setup/SKILL.md

Exception: if the user already has a working problem object, skip directly to Stage 2 — do not read Stage 1 files.

Stage 1 checklist (must all be true before moving on):

• create-problem read in full
• define-domains read in full, domains defined
• define-equations read in full, equations defined
• condition-setup read in full, conditions bound
• a problem object exists

Stage 2 — Model selection

Read ../select-model/SKILL.md in full now.

Input/output dimensions are known from the Stage 1 problem object — use them, don't re-derive or guess.

Stage 2 checklist:

• select-model read in full
• model instantiated with correct input/output dims

Stage 3 — Solver selection

Read ../select-solver/SKILL.md in full now.

Check the condition types from the problem object (set in Stage 1) — they drive which solver is valid. Do not pick a solver without checking this.

Stage 3 checklist:

• select-solver read in full
• condition types checked against solver requirements
• solver instantiated

Stage 4 — Trainer configuration

Read ../select-trainer/SKILL.md in full now.

Verify all domains are discretised before configuring the trainer — if not, go back to define-domains, don't silently proceed.

Stage 4 checklist:

• select-trainer read in full
• all domains confirmed discretised
• trainer configured

Stage 5 — Train

The user runs trainer.train(). Offer to inspect results afterwards (forward pass, loss curves, discovered unknown parameters for inverse problems).