cfd-ai-paper-skills

Use when writing, reviewing, auditing, revising, or producing LaTeX for CFD-AI and scientific-ML manuscripts, reviews, experiment plans, reproducibility audits, related-work positioning, reviewer responses, or benchmark evaluations.

Use when stress-testing CFD, fluid mechanics, turbulence, PINN, neural-operator, surrogate, closure, or SciML manuscripts from a strict reviewer perspective.

Use when designing figures, result flow, captions, and visual evidence for CFD-AI/SciML journal manuscripts.

Use when coordinating multiple specialized CFD-AI/SciML paper agents: reviewer, evidence auditor, experiment planner, figure editor, prose editor, and final gatekeeper.

Use when auditing a CFD-AI/SciML manuscript's abstract, introduction, contributions, or discussion to verify that every claim is supported by evidence and scoped correctly.

Use when running iterative reviewer-to-editor revision of a CFD-AI/SciML manuscript: reviewer audit, editorial rewrite, re-audit, residual-risk tracking, and submission-readiness gating.

Use when turning a CFD-AI/SciML draft, abstract, result section, or manuscript idea into a strict reviewer-audit gate with rejection risks, missing evidence, and concrete fixes.

Use when writing, revising, outlining, or critiquing CFD-AI and scientific ML journal papers; focuses on argument structure, claim-evidence alignment, novelty, and reviewer defense.

Use when auditing CFD-AI/SciML papers for reproducibility: governing equations, nondimensional numbers, geometry, mesh, boundary conditions, solver settings, datasets, splits, baselines, hyperparameters, compute, and exact commands.

Use when designing experiments, baselines, ablations, generalization tests, and physical diagnostics for CFD-AI and scientific ML manuscripts.

Use before submitting a CFD-AI/SciML journal manuscript to check formatting, files, declarations, figures, references, cover letter, and reviewer-risk items.

Use when writing, editing, compiling, or debugging LaTeX scientific manuscripts, journal templates, equations, figures, tables, captions, and BibTeX.

Use when drafting or auditing limitations, ethics, compute, data/code availability, and LLM-usage disclosure sections for CFD-AI/SciML papers.

Use when mapping CFD-AI/SciML related work into a taxonomy, identifying nearest prior art, and writing gap/positioning paragraphs without citation dumping.

Use when searching, reading, organizing, and writing related work for CFD-AI/SciML papers, with emphasis on taxonomy, close-prior-art risk, and gap positioning.

Use when preparing point-by-point responses and manuscript revision plans for CFD-AI/SciML journal reviews.

Use when simulating a journal/ML-conference reviewer for a CFD-AI/SciML manuscript, producing strengths, major concerns, reproducibility concerns, required experiments, and decision risk.

Use when auditing CFD-AI/SciML experiments for baselines, ablations, generalization, physical diagnostics, uncertainty, compute reporting, and leakage-safe evaluation.