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Use when mapping CFD-AI/SciML related work into a taxonomy, identifying nearest prior art, and writing gap/positioning paragraphs without citation dumping.
Codex または Claude でインストール この Prompt をコピーして Codex、Claude、または他のアシスタントに貼り付けると、Skill ページを確認してインストールできます。
SOC 職業分類に基づく
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.
| name | related-work-cartographer |
| description | Use when mapping CFD-AI/SciML related work into a taxonomy, identifying nearest prior art, and writing gap/positioning paragraphs without citation dumping. |
| version | 0.4.0 |
| author | CFD-AI Paper Skills maintainers |
| metadata | {"short-description":"Related-work map and prior-art risk audit"} |
Use for related-work structure, prior-art risk, journal positioning, and “how is this different?” questions.
Read only the relevant gold notes:
| Need | Read |
|---|---|
| Field/workflow taxonomy | references/gold-papers/brunton-2020-machine-learning-fluid-mechanics.md |
| Sparse discovery / interpretable dynamics | references/gold-papers/brunton-2016-sindy-pnas.md |
| ML workflow staging | references/gold-papers/brunton-2021-applying-ml-fluid-mechanics.md |
| CFD opportunity map | references/gold-papers/vinuesa-2022-enhancing-cfd-ml.md |
| Experiments | references/gold-papers/vinuesa-2023-transformative-ml-experiments-nrp.md |
| Category | Key papers | What they solve | Limitation | Relation to this work |
|---|
Then add:
| Paper | Workflow role | ML insertion point | Prior-art risk | Source scope |
|---|
Workflow roles: discovery, modeling, sensing/reconstruction, experiments, closure, ROM, control, optimization, acceleration, UQ.
ML insertion points: problem formulation, data curation, architecture, loss, optimization, deployment/control.
Prior work primarily addresses X under Y conditions using Z. However, A remains unresolved, especially for B. The present work targets A by C and evaluates it through D.
The paragraph must name the CFD bottleneck, closest prior role, unresolved boundary, proposed mechanism, and decisive evidence. "Our method improves accuracy and efficiency" is not a gap.
