pinns-mpf-workflow

pinns-mpf-workflow is a skill for Claude Code, Codex from SFETNI/Deep-Matter-Chem-Skills. It costs 7 tokens per session (7,013 once invoked), scanned A, original, MIT.

A workflow for using coordinate-based neural networks to solve multiphase-field interface equations. These equations describe how boundaries between several material regions move over space and time.

In plain words
What is it for?
Train and evaluate PINNs-MPF models, choose domain and sampling strategies, handle interface benchmarks, and audit published or reproduced results.
Why use it?
It gives a defined way to set up, reproduce, or assess this simulation approach and compare it with a conventional finite-difference solver.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Train and evaluate PINNs-MPF models, choose domain and sampling strategies, handle interface benchmarks, and audit published or reproduced results.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow
Install

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.

Any agent
npx skills add SFETNI/Deep-Matter-Chem-Skills --skill pinns-mpf-workflow
Clone the repo
git clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-Skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for pinns-mpf-workflow

README.md
[![agentmods](https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow/github.svg)](https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow)
Your own site
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for pinns-mpf-workflow

Your own site · 80×15
<a href="https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow"><img src="https://agentmods.dev/badge/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 7 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 7,013 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

What 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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00007 $0.07013
Opus 5.5 $0.00003 $0.02805
Sonnet 5.5 $0.00001 $0.01403
Haiku 4.5 $0.00001 $0.00701

Measured 4d ago against content hash eded3394b961, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

pinns-mpf-workflow 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 4d ago.

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.

skills/phase-field-microstructure/pinns-mpf-workflow/SKILL.md · 362 lines

How it starts

The opening of the file, as written. The whole thing — 362 lines — stays where its author put it; the contents beside it link to each section on GitHub.

PINNs-MPF Workflow

Description

This skill covers the PINNs-MPF approach: coordinate-based physics-informed neural networks that solve multiphase-field (MPF) interface dynamics. The skill includes:

  • the pairwise MPF equation and its nondimensionalization;
  • extended domain decomposition over space, time, and phases;
  • the master/worker network organization, pyramidal training, and transfer of learning;
  • interface-focused adaptive collocation (AMFO);
  • the Adam/L-BFGS "wheel of optimizers";
  • driven-interface, curvature-shrinkage, and triple-junction benchmarks;
  • independent comparison against a conventional finite-difference solver.

Invoke this skill when setting up, reproducing, or auditing PINNs-MPF-style simulations, or when deciding whether a coordinate-network PINN is appropriate for a multiphase interface problem.

Domain Context

PINNs-MPF (Elfetni & Darvishi Kamachali, Engineering Analysis with Boundary Elements 176, 2025, peer reviewed) solves the isotropic pairwise multiphase-field equation with networks mapping (x, y, t) to phase fractions. The published formulation is:

∂φ_α/∂t = (μσ/N) Σ_β [ ∇²φ_α − ∇²φ_β + (π²/2η²)(φ_α − φ_β) ] + (μ/N) Σ_β ΔG(φ_α, φ_β)

  • Symbols: η is the interface width, μ the interface mobility, σ the interface energy, and N the number of locally present phases. This is the Steinbach/OpenPhase-type obstacle-potential MPF family.
  • Coefficient convention (check this before implementing). The obstacle coefficient is printed as π²/(2η²) in Eq. 1 above. The paper's expanded form uses I_α = ∇²φ_α + (π²/η²)φ_α (Eq. 15), i.e. σ(I_α − I_β), and its two-phase form (Eq. 17) is consistent with Eq. 15. The dimensionless Eqs. 22 and 24 scale differently again.
    • The reference solver in Workflow 6 uses the Eq. 15/17 convention. That convention makes the sine profile of width η stationary, and it reproduced both v = μσκ and v = μΔg in this skill's tests.
    • Its driving term is normalized so the N = 2 limit equals Eq. 17.
    • [EXPERT REVIEW NEEDED: reconcile the obstacle and driving-force coefficients of Eqs. 1, 15, 17, 22 and 24 with the code that produced the published results]
  • Nondimensional time scale: T = η²/(μσ).
  • Two-phase limit: the driven case reduces to a scalar equation with driving term proportional to π√(φ(1−φ))/η. Its sharp-interface limits are v = μΔg for a flat driven front and v = μσκ for curvature-driven motion.

Read the full file on GitHub · 362 lines

Changes

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.

  1. 4d ago First seen · 362 lines · 7 tokens per session scan A eded3394b961

Subscribe to this mod's changes

pinns-mpf-workflow is a skill published in the GitHub repository SFETNI/Deep-Matter-Chem-Skills (8 stars, last pushed 5d ago), licensed MIT. It adds 7 tokens to every session and 7,013 once invoked, about $0.0000 per session on Opus 5.5. 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 2026-10-12.

Related

Other skills, from other repositories

primekg

Query the Precision Medicine Knowledge Graph (PrimeKG) for multiscale biological relationships across genes and proteins, drugs, diseases, phenotypes, pathways, biological processes, exposures and anatomy. Use this skill to search entities by name, pull direct neighbours and their evidence types, summarise the local…

K-Dense-AI/drug-discovery-agent-skills · 121 tokens

fragment-based-count-matrix-generation

Use when you have a backed AnnData object containing processed fragment data (stored in .obsm['fragmentpaired'] or .

HolobiomicsLab/asb-skill-collections · 33 tokens

methylbase-object-handling

Use when after reading in per-sample methylation call files with methRead() and obtaining methylRawList objects, but before calculating differential methylation or performing annotation.

HolobiomicsLab/asb-skill-collections · 40 tokens

monocle3-trajectory-embedding

Use when you have an ArchR project object with dimensionality reduction results (LSI or combined dimensions from scATAC-seq ± scRNA-seq) and want to infer pseudotime trajectories and cell-state transitions.

HolobiomicsLab/asb-skill-collections · 54 tokens

motif-annotation-correlation-analysis

Use when you have a chromVARDeviations object with multiple annotation sets (such as JASPAR motifs and kmers) and need to determine which annotation pairs are redundant (high correlation) versus synergistic (high synergy z-scores).

HolobiomicsLab/asb-skill-collections · 57 tokens

motif-database-query-and-matching

Use when you have a set of differentially accessible peaks (output from differential accessibility testing, e.g., tl.

HolobiomicsLab/asb-skill-collections · 32 tokens