Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add instructions/pina-org/pina/agents-mdgit clone --depth 1 https://github.com/PINA-org/PINAWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00829 | $0.00829 |
| Opus 5 | $0.00415 | $0.00415 |
| Sonnet 5 | $0.00166 | $0.00166 |
| Haiku 4.5 | $0.00083 | $0.00083 |
Grade A, and why
PINA AGENTS.md scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured yesterday.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
PINA — Physics-Informed Neural Architectures
A PyTorch library for solving differential equations with neural networks (PINNs).
Quick Reference
Workflow: Problem → Model → Solver → Trainer
problem = MyProblem()
problem.discretise_domain(n=256, mode="grid")
model = FeedForward(input_dimensions=2, output_dimensions=1)
solver = PINN(problem=problem, model=model)
trainer = Trainer(solver=solver, max_epochs=1000)
trainer.train()
Problem types
SpatialProblem, TimeDependentProblem, ParametricProblem, InverseProblem.
Define output_variables, spatial_domain/temporal_domain, and conditions (dict of Condition).
Condition types
Condition(domain=..., equation=...)— physics residual on sampled domainCondition(input=..., equation=...)— physics residual at fixed pointsCondition(input=..., target=...)— supervised dataCondition(input=..., n_windows=..., unroll_length=...)— time series
Domains
CartesianDomain, EllipsoidDomain, SimplexDomain.
Set ops: Union, Intersection, Difference, Exclusion.
Discretise with problem.discretise_domain(n, mode) where mode is "grid", "random", "lh", "chebyshev".
Solvers
PINN, CausalPINN, SelfAdaptivePINN, CompetitivePINN, GradientPINN, RBAPINN,
SupervisedSolver, AutoregressiveSolver. Ensembles via *EnsembleSolver variants.
Models
FeedForward, ResidualFeedForward, PirateNet, DeepONet, MIONet, FNO,
KolmogorovArnoldNetwork, GraphNeuralOperator, SINDy, and more.
Equation zoo
PoissonEquation, HelmholtzEquation, BurgersEquation, AdvectionEquation,
AllenCahnEquation, AcousticWaveEquation, DiffusionReactionEquation.
Boundary: FixedValue, FixedGradient, FixedFlux, FixedLaplacian.
Key utilities
LabelTensor—torch.Tensorwith named columns; index via.extract(["x", "y"])- Differential operators:
grad,div,laplacian,advection— not cached, compute once Trainerwrapslightning.pytorch.Trainer; handles DataModule, batching, device placement
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- yesterday First seen · 80 lines · 829 tokens per session scan A 4bb21d0402ae
PINA AGENTS.md is an instructions file published in the GitHub repository PINA-org/PINA (789 stars, last pushed 15d ago), licensed MIT. It adds 829 tokens to every session, about $0.0041 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
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Instructions for huggingface/huggingface_hub, covering agent guide for huggingfacehub, project overview, setup, key commands and code structure.
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huggingface_hub CLAUDE.md
Instructions for huggingface/huggingface_hub, a project described as: The official CLI and Python client for the Hugging Face Hub.
Traintools AGENTS.md
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