How do I install the Powergraph Gnn Research skill?

Run npx skillmds@latest add majiayu000/powergraph-gnn-research in your terminal (requires Node.js), paste this page's agent-chat prompt into Claude, Cursor, or any MCP-connected agent, or download the SKILL.md file and copy it into your agent's skills directory.

Is Powergraph Gnn Research safe to use?

This skill has not completed SkillMD's automated safety review yet. Independent scanners report: SkillSpector: PASS, Skill Scanner: PASS. Capability flags: docs only. SkillMD never runs a skill's scripts for you; review the SKILL.md before installing.

Which AI agents work with Powergraph Gnn Research?

This skill is tagged as working with Claude Code, Claude.ai, OpenAI Codex. SKILL.md is an open format, so most agents that read a skills directory can load it too.

Is Powergraph Gnn Research free to use?

Yes. Installing skills from SkillMD is free, and the skill stays under its author's original license.

Who published Powergraph Gnn Research?

majiayu000 (@majiayu000) published this skill. Their other Agent Skills are listed on their SkillMD profile.

Powergraph Gnn Research by majiayu000

Name: Powergraph Gnn Research
Rating: 4.2 (5 reviews)
Author: majiayu000

Powergraph Gnn Research

Research pipeline for topology-aware GNN representation learning on power grids using the PowerGraph benchmark. Use when (1) building physics-guided GNNs for power flow (PF), optimal power flow (OPF), or cascading failure prediction, (2) implementing self-supervised pretraining for power systems, (3) evaluating cascade explanation fidelity against ground-truth masks, or (4) conducting reproducible ML-for-power-systems research. Triggers include "PowerGraph", "power flow GNN", "OPF surrogate", "cascade prediction", "physics-guided GNN", "grid analytics ML", "power system representation learning".

majiayu000 Updated Sep 9, 2026 567 repo stars

File contents

PowerGraph GNN Research Pipeline

Primary claim: A grid-specific self-supervised, physics-consistent GNN encoder improves PF/OPF learning (especially low-label/OOD), and transfers to cascading-failure prediction and explanation.

Scripts

Task	Script
Data ingestion	`scripts/load_powergraph.py`
PF baseline	`scripts/train_pf_baseline.py`
Physics metrics	`scripts/physics_residual.py`
SSL pretraining	`scripts/pretrain_ssl.py`
Multi-task training	`scripts/train_multitask.py`
Explanation eval	`scripts/eval_cascade_explanation.py`

Workflow

Data → PowerGraph → PyG (PF/OPF node targets + cascade graph labels + exp masks)
Baseline → PF regression with sin/cos angles + physics residual metric
Multi-task → Shared encoder + PF/OPF/Cascade heads
SSL → Masked injection/edge reconstruction → fine-tune
Evaluation → Explanation AUC vs ground-truth masks + robustness tests

Validity Anchors (Critical)

Angle handling: Predict sin(θ), cos(θ), recover via atan2. Direct MSE on raw angles fails at ±π wrap-around.

Physics residual: Report KCL mismatch alongside accuracy. Ground truth ≈ 0, random >> 1.

Blocked splits: PowerGraph uses 1-year load @ 15-min. Use months 1-9 train / 10 val / 11-12 test. Random splits leak seasonal patterns.

Explanation fidelity: Use PowerGraph exp.mat ground-truth masks. Report AUC + Precision@K.

Reference Docs

references/data_pipeline.md — PowerGraph → PyG conversion, splits
references/model_architecture.md — Physics-guided message passing, heads
references/ssl_pretraining.md — Masked tasks, low-label experiments
references/uncertainty_quantification.md — Ensembles, MC dropout, calibration
references/evaluation_protocols.md — Metrics, robustness, statistical tests
references/publication_soundness.md — Reviewer risks, claim framing
references/experiment_configs.md — YAML config structure, sweeps

Common Pitfalls

Issue	Fix
Angle wrap-around	sin/cos representation
Data leakage	Blocked time splits
Cascade imbalance	Weighted/focal loss
OOM large grids	Gradient checkpointing
SSL collapse	Stop-gradient + EMA encoder
Physics violations	Residual regularization

Publication Checklist

Ablation: single-task vs multi-task vs SSL+multi-task
Low-label curves: 10/20/50/100% training data
Physics residual alongside accuracy metrics
Blocked splits (not random)
Explanation AUC against exp.mat ground truth
Robustness under edge-drop perturbations
Statistical significance with 95% CI
One-command reproducibility (python analysis/run_all.py)

---name: powergraph-gnn-researchdescription: Research pipeline for topology-aware GNN representation learning on power grids using the PowerGraph benchmark. Use when (1) building physics-guided GNNs for power flow (PF), optimal power flow (OPF), or cascading failure prediction, (2) implementing self-supervised pretraining for power systems, (3) evaluating cascade explanation fidelity against ground-truth masks, or (4) conducting reproducible ML-for-power-systems research. Triggers include "PowerGraph", "power flow GNN", "OPF surrogate", "cascade prediction", "physics-guided GNN", "grid analytics ML", "power system representation learning".--- # PowerGraph GNN Research Pipeline **Primary claim**: A grid-specific self-supervised, physics-consistent GNN encoder improves PF/OPF learning (especially low-label/OOD), and transfers to cascading-failure prediction and explanation. ## Scripts | Task | Script | |------|--------| | Data ingestion | `scripts/load_powergraph.py` | | PF baseline | `scripts/train_pf_baseline.py` | | Physics metrics | `scripts/physics_residual.py` | | SSL pretraining | `scripts/pretrain_ssl.py` | | Multi-task training | `scripts/train_multitask.py` | | Explanation eval | `scripts/eval_cascade_explanation.py` | ## Workflow 1. **Data** → PowerGraph → PyG (PF/OPF node targets + cascade graph labels + exp masks) 2. **Baseline** → PF regression with sin/cos angles + physics residual metric 3. **Multi-task** → Shared encoder + PF/OPF/Cascade heads 4. **SSL** → Masked injection/edge reconstruction → fine-tune 5. **Evaluation** → Explanation AUC vs ground-truth masks + robustness tests ## Validity Anchors (Critical) **Angle handling**: Predict `sin(θ), cos(θ)`, recover via `atan2`. Direct MSE on raw angles fails at ±π wrap-around. **Physics residual**: Report KCL mismatch alongside accuracy. Ground truth ≈ 0, random >> 1. **Blocked splits**: PowerGraph uses 1-year load @ 15-min. Use months 1-9 train / 10 val / 11-12 test. Random splits leak seasonal patterns. **Explanation fidelity**: Use PowerGraph `exp.mat` ground-truth masks. Report AUC + Precision@K. ## Reference Docs - `references/data_pipeline.md` — PowerGraph → PyG conversion, splits - `references/model_architecture.md` — Physics-guided message passing, heads - `references/ssl_pretraining.md` — Masked tasks, low-label experiments - `references/uncertainty_quantification.md` — Ensembles, MC dropout, calibration - `references/evaluation_protocols.md` — Metrics, robustness, statistical tests - `references/publication_soundness.md` — Reviewer risks, claim framing - `references/experiment_configs.md` — YAML config structure, sweeps ## Common Pitfalls | Issue | Fix | |-------|-----| | Angle wrap-around | sin/cos representation | | Data leakage | Blocked time splits | | Cascade imbalance | Weighted/focal loss | | OOM large grids | Gradient checkpointing | | SSL collapse | Stop-gradient + EMA encoder | | Physics violations | Residual regularization | ## Publication Checklist - [ ] Ablation: single-task vs multi-task vs SSL+multi-task - [ ] Low-label curves: 10/20/50/100% training data - [ ] Physics residual alongside accuracy metrics - [ ] Blocked splits (not random) - [ ] Explanation AUC against exp.mat ground truth - [ ] Robustness under edge-drop perturbations - [ ] Statistical significance with 95% CI - [ ] One-command reproducibility (`python analysis/run_all.py`)

Frequently asked questions