القائمة
Use when mapping CFD-AI/SciML related work into a taxonomy, identifying nearest prior art, and writing gap/positioning paragraphs without citation dumping.
التثبيت باستخدام Codex أو Claude انسخ هذا Prompt والصقه في Codex أو Claude أو مساعد آخر ليراجع صفحة Skill ويثبّتها لك.
استنادا إلى تصنيف SOC المهني
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.
