What specific IEEE PES requirements does this skill check?

It audits page limits, column formatting, font styles, citation integrity, and abstract word counts as required by the IEEE PES General Meeting and Journal guidelines.

Does it support reproducibility checks?

Yes, the reproducibility mode checks for consistent seeds, documented dataset versions, explicit training commands, and complete configuration files like YAML splits.

Can it distinguish between different types of power system tasks?

Yes, it specifically differentiates between Power Flow, Line Flow, and Cascading Failure predictions, ensuring equations and formulations are mathematically consistent with the specific task.

How does the shadow review mode work?

It simulates a rigorous 'Reviewer #2' persona to evaluate technical soundness, novelty, and experimental rigor, providing a list of major issues, minor fixes, and an acceptance risk rating.

IEEE PES Paper Reviewer

Name: IEEE PES Paper Reviewer
Author: mhdhazmi

bymhdhazmi

0•

Data Science & ML

Provides specialized technical auditing and compliance reviews for Physics-Guided GNN research papers targeting IEEE Power & Energy Society submissions.

The IEEE PES Paper Reviewer skill is a domain-specific assistant designed for researchers working on Physics-Guided Self-Supervised Learning (SSL) GNNs for power grids. It streamlines the preparation of academic manuscripts by validating physics consistency (Power Flow vs. Line Flow), auditing experimental claims against raw data, and ensuring strict adherence to IEEE PES formatting standards. By simulating a rigorous peer-review process, the skill identifies technical weaknesses, assesses reproducibility, and refines the positioning of research against existing baselines like PPGT, helping authors increase their chances of acceptance.

Key Features

01Evidence-based claims auditing to verify experimental results against project data

02Automated IEEE PES compliance auditing for formatting and submission readiness

030 GitHub stars

04Physics-domain validation for Power Flow and Cascading Failure models

05Reproducibility assessment for training seeds, configs, and dataset versions

06Competitive positioning analysis versus Graph Transformer and GNN baselines

Use Cases

01Performing a technical 'shadow review' to identify gaps in novelty or rigor before submission

02Preparing Physics-Guided GNN manuscripts for IEEE PES General Meeting submissions

03Auditing the physics-informed logic and equations in power system machine learning models

What are Skills?·How to Install

Install with 🐟 Skill.Fish

npx skillfish add mhdhazmi/gnnpowersystem paper-review-skills

For use in Claude.ai and ChatGPT

Download Skill