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 skills add SFETNI/Deep-Matter-Chem-Skills --skill pinns-mpf-workflowgit clone --depth 1 https://github.com/SFETNI/Deep-Matter-Chem-SkillsWrote 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.
[](https://agentmods.dev/skills/sfetni/deep-matter-chem-skills/pinns-mpf-workflow)<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.
<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>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.
| Model | Per session | Once 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 |
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.
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.
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.
- 4d ago First seen · 362 lines · 7 tokens per session scan A eded3394b961
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.
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