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
npx skills add majiayu000/claude-skill-registry --skill powergraph-gnn-research --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# 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, r
- Scripts
- Workflow
- Validity Anchors (Critical)
- Reference Docs
- Common Pitfalls
- Publication Checklist
What does the powergraph-gnn-research skill do?
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
How do I install it?
Run `npx skills add majiayu000/claude-skill-registry --skill powergraph-gnn-research --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From majiayu000/claude-skill-registry, a repository with 534 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.