feat: update research skills part 9

skills/Research/tides/SKILL.md

@@ -1,20 +1,32 @@
 ---
 id: tides
 name: Tides
-description: Step-by-step guidance for tides.
+description: Step-by-step guidance for tidal data analysis, prediction workflows, and coastal research practices.
 category: Research
+requires: []
+examples:
+  - When is the next high tide in San Francisco Bay?
+  - Provide a tide chart for the coast of Maine for the upcoming week.
 ---
 
 # Tides
 
 Support tides workflows with clear steps and best practices.
 
-## When to Use
+## Instruction
+- Identify the target geographic location or specific tidal station ID for analysis.
+- Retrieve tidal height predictions and historical observations from authoritative maritime databases.
+- Analyze tidal cycles to identify the exact times and heights of high tide, low tide, and Slack water.
+- Account for local datum shifts (e.g., MLLW, MSL) to ensure consistency in depth measurements.
+- Correlate tidal data with lunar phases and meteorological factors (e.g., storm surges) to assess potential flooding risks.
+- Provide step-by-step guidance for integrating tidal charts into coastal research or navigation planning.
 
-- You need help with tides.
-- You want a clear, actionable next step.
+## When to Use
+- When planning coastal field research, maritime navigation, or coastal engineering projects.
+- When needing precise high/low tide schedules for specific global ports or coastal segments.
+- When assessing environmental impacts and sediment transport patterns influenced by tidal cycles.
 
 ## Output
-
-- Summary of goals and plan
-- Key tips and precautions
+- A comprehensive tidal forecast report including peak times and height estimates.
+- Visualized tidal curves or charts for the requested time window.
+- Actionable safety precautions and optimal time windows for coastal activities.

skills/Research/torchdrug/SKILL.md

@@ -1,8 +1,12 @@
 ---
-category: Research
 id: torchdrug
 name: Torchdrug
-description: Graph-based drug discovery toolkit. Molecular property prediction (ADMET), protein modeling, knowledge graph reasoning, molecular generation, retrosynthesis, GNNs (GIN, GAT, SchNet), 40+ datasets, for PyTorch-based ML on molecules, proteins, and biomedical graphs.
+description: PyTorch-based drug discovery toolkit for molecular property prediction, protein modeling, and retrosynthesis using GNNs.
+category: Research
+requires: []
+examples:
+  - Predict the ADMET properties for this SMILES string using TorchDrug.
+  - Build a protein-protein interaction network from this biological dataset.
 ---
 
 # TorchDrug
@@ -37,54 +41,10 @@ This skill should be used when working with:
 - Compatible with PyTorch and PyTorch Lightning
 - Integrates with AlphaFold and ESM for proteins
 
-## Getting Started
-
-### Installation
-
-```bash
-uv pip install torchdrug
-# Or with optional dependencies
-uv pip install torchdrug[full]
-```
-
-### Quick Example
-
-```python
-from torchdrug import datasets, models, tasks
-from torch.utils.data import DataLoader
-
-# Load molecular dataset
-dataset = datasets.BBBP("~/molecule-datasets/")
-train_set, valid_set, test_set = dataset.split()
-
-# Define GNN model
-model = models.GIN(
-    input_dim=dataset.node_feature_dim,
-    hidden_dims=[256, 256, 256],
-    edge_input_dim=dataset.edge_feature_dim,
-    batch_norm=True,
-    readout="mean"
-)
-
-# Create property prediction task
-task = tasks.PropertyPrediction(
-    model,
-    task=dataset.tasks,
-    criterion="bce",
-    metric=["auroc", "auprc"]
-)
-
-# Train with PyTorch
-optimizer = torch.optim.Adam(task.parameters(), lr=1e-3)
-train_loader = DataLoader(train_set, batch_size=32, shuffle=True)
-
-for epoch in range(100):
-    for batch in train_loader:
-        loss = task(batch)
-        optimizer.zero_grad()
-        loss.backward()
-        optimizer.step()
-```
+## Output
+- Trained GNN models and associated performance metric reports.
+- Predicted molecular properties or optimized molecular structures in SMILES format.
+- Step-by-step guidance for configuring complex drug discovery pipelines in PyTorch.
 
 ## Core Capabilities
 
@@ -103,7 +63,7 @@ Predict chemical, physical, and biological properties of molecules from structur
 - GNN models (GIN, GAT, SchNet)
 - PropertyPrediction and MultipleBinaryClassification tasks
 
-**Reference:** See `references/molecular_property_prediction.md` for:
+**Reference:**
 - Complete dataset catalog
 - Model selection guide
 - Training workflows and best practices
@@ -126,7 +86,7 @@ Work with protein sequences, structures, and properties.
 - Structure models (GearNet, SchNet)
 - Multiple task types for different prediction levels
 
-**Reference:** See `references/protein_modeling.md` for:
+**Reference:** 
 - Protein-specific datasets
 - Sequence vs structure models
 - Pre-training strategies
@@ -147,7 +107,7 @@ Predict missing links and relationships in biological knowledge graphs.
 - Embedding models (TransE, RotatE, ComplEx)
 - KnowledgeGraphCompletion task
 
-**Reference:** See `references/knowledge_graphs.md` for:
+**Reference:** 
 - Knowledge graph datasets (including Hetionet with 45k biomedical entities)
 - Embedding model comparison
 - Evaluation metrics and protocols
@@ -169,7 +129,7 @@ Generate novel molecular structures with desired properties.
 - GraphAutoregressiveFlow
 - Property optimization workflows
 
-**Reference:** See `references/molecular_generation.md` for:
+**Reference:** 
 - Generation strategies (unconditional, conditional, scaffold-based)
 - Multi-objective optimization
 - Validation and filtering
@@ -191,7 +151,7 @@ Predict synthetic routes from target molecules to starting materials.
 - SynthonCompletion (reactant prediction)
 - End-to-end Retrosynthesis pipeline
 
-**Reference:** See `references/retrosynthesis.md` for:
+**Reference:** 
 - Task decomposition (center ID → synthon completion)
 - Multi-step synthesis planning
 - Commercial availability checking
@@ -208,7 +168,7 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 - Knowledge graph: TransE, RotatE, ComplEx, SimplE
 - Generative: GraphAutoregressiveFlow
 
-**Reference:** See `references/models_architectures.md` for:
+**Reference:** 
 - Detailed model descriptions
 - Model selection guide by task and dataset
 - Architecture comparisons
@@ -224,7 +184,7 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 - Knowledge graphs (general and biomedical)
 - Retrosynthesis reactions
 
-**Reference:** See `references/datasets.md` for:
+**Reference:** 
 - Complete dataset catalog with sizes and tasks
 - Dataset selection guide
 - Loading and preprocessing
@@ -243,7 +203,6 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 4. Train with scaffold split for realistic evaluation
 5. Evaluate using AUROC and AUPRC
 
-**Navigation:** `references/molecular_property_prediction.md` → Dataset selection → Model selection → Training
 
 ### Workflow 2: Protein Function Prediction
 
@@ -256,7 +215,6 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 4. Fine-tune pre-trained model or train from scratch
 5. Evaluate using accuracy and per-class metrics
 
-**Navigation:** `references/protein_modeling.md` → Model selection (sequence vs structure) → Pre-training strategies
 
 ### Workflow 3: Drug Repurposing via Knowledge Graphs
 
@@ -270,7 +228,6 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 5. Query for "Compound-treats-Disease" predictions
 6. Filter by plausibility and mechanism
 
-**Navigation:** `references/knowledge_graphs.md` → Hetionet dataset → Model selection → Biomedical applications
 
 ### Workflow 4: De Novo Molecule Generation
 
@@ -284,7 +241,6 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 5. Validate chemistry and filter by drug-likeness
 6. Rank by multi-objective scoring
 
-**Navigation:** `references/molecular_generation.md` → Conditional generation → Multi-objective optimization
 
 ### Workflow 5: Retrosynthesis Planning
 
@@ -298,74 +254,26 @@ Comprehensive catalog of GNN architectures for different data types and tasks.
 5. Apply recursively for multi-step planning
 6. Check commercial availability of building blocks
 
-**Navigation:** `references/retrosynthesis.md` → Task types → Multi-step planning
 
 ## Integration Patterns
 
 ### With RDKit
 
-Convert between TorchDrug molecules and RDKit:
-```python
-from torchdrug import data
-from rdkit import Chem
-
-# SMILES → TorchDrug molecule
-smiles = "CCO"
-mol = data.Molecule.from_smiles(smiles)
-
-# TorchDrug → RDKit
-rdkit_mol = mol.to_molecule()
-
-# RDKit → TorchDrug
-rdkit_mol = Chem.MolFromSmiles(smiles)
-mol = data.Molecule.from_molecule(rdkit_mol)
-```
+Convert between TorchDrug molecules and RDKit
 
 ### With AlphaFold/ESM
 
-Use predicted structures:
-```python
-from torchdrug import data
-
-# Load AlphaFold predicted structure
-protein = data.Protein.from_pdb("AF-P12345-F1-model_v4.pdb")
-
-# Build graph with spatial edges
-graph = protein.residue_graph(
-    node_position="ca",
-    edge_types=["sequential", "radius"],
-    radius_cutoff=10.0
-)
-```
+Use predicted structures
 
 ### With PyTorch Lightning
 
-Wrap tasks for Lightning training:
-```python
-import pytorch_lightning as pl
-
-class LightningTask(pl.LightningModule):
-    def __init__(self, torchdrug_task):
-        super().__init__()
-        self.task = torchdrug_task
-
-    def training_step(self, batch, batch_idx):
-        return self.task(batch)
-
-    def validation_step(self, batch, batch_idx):
-        pred = self.task.predict(batch)
-        target = self.task.target(batch)
-        return {"pred": pred, "target": target}
-
-    def configure_optimizers(self):
-        return torch.optim.Adam(self.parameters(), lr=1e-3)
-```
+Wrap tasks for Lightning training
 
 ## Technical Details
 
 For deep dives into TorchDrug's architecture:
 
-**Core Concepts:** See `references/core_concepts.md` for:
+**Core Concepts:** 
 - Architecture philosophy (modular, configurable)
 - Data structures (Graph, Molecule, Protein, PackedGraph)
 - Model interface and forward function signature
@@ -374,73 +282,25 @@ For deep dives into TorchDrug's architecture:
 - Loss functions and metrics
 - Common pitfalls and debugging
 
-## Quick Reference Cheat Sheet
-
-**Choose Dataset:**
-- Molecular property → `references/datasets.md` → Molecular section
-- Protein task → `references/datasets.md` → Protein section
-- Knowledge graph → `references/datasets.md` → Knowledge graph section
-
-**Choose Model:**
-- Molecules → `references/models_architectures.md` → GNN section → GIN/GAT/SchNet
-- Proteins (sequence) → `references/models_architectures.md` → Protein section → ESM
-- Proteins (structure) → `references/models_architectures.md` → Protein section → GearNet
-- Knowledge graph → `references/models_architectures.md` → KG section → RotatE/ComplEx
-
-**Common Tasks:**
-- Property prediction → `references/molecular_property_prediction.md` or `references/protein_modeling.md`
-- Generation → `references/molecular_generation.md`
-- Retrosynthesis → `references/retrosynthesis.md`
-- KG reasoning → `references/knowledge_graphs.md`
-
-**Understand Architecture:**
-- Data structures → `references/core_concepts.md` → Data Structures
-- Model design → `references/core_concepts.md` → Model Interface
-- Task design → `references/core_concepts.md` → Task Interface
 
 ## Troubleshooting Common Issues
 
 **Issue: Dimension mismatch errors**
 → Check `model.input_dim` matches `dataset.node_feature_dim`
-→ See `references/core_concepts.md` → Essential Attributes
 
 **Issue: Poor performance on molecular tasks**
 → Use scaffold splitting, not random
 → Try GIN instead of GCN
-→ See `references/molecular_property_prediction.md` → Best Practices
 
 **Issue: Protein model not learning**
 → Use pre-trained ESM for sequence tasks
 → Check edge construction for structure models
-→ See `references/protein_modeling.md` → Training Workflows
 
 **Issue: Memory errors with large graphs**
 → Reduce batch size
 → Use gradient accumulation
-→ See `references/core_concepts.md` → Memory Efficiency
 
 **Issue: Generated molecules are invalid**
 → Add validity constraints
 → Post-process with RDKit validation
-→ See `references/molecular_generation.md` → Validation and Filtering
-
-## Resources
-
-**Official Documentation:** https://torchdrug.ai/docs/
-**GitHub:** https://github.com/DeepGraphLearning/torchdrug
-**Paper:** TorchDrug: A Powerful and Flexible Machine Learning Platform for Drug Discovery
-
-## Summary
-
-Navigate to the appropriate reference file based on your task:
-
-1. **Molecular property prediction** → `molecular_property_prediction.md`
-2. **Protein modeling** → `protein_modeling.md`
-3. **Knowledge graphs** → `knowledge_graphs.md`
-4. **Molecular generation** → `molecular_generation.md`
-5. **Retrosynthesis** → `retrosynthesis.md`
-6. **Model selection** → `models_architectures.md`
-7. **Dataset selection** → `datasets.md`
-8. **Technical details** → `core_concepts.md`
 
-Each reference provides comprehensive coverage of its domain with examples, best practices, and common use cases.

skills/Research/tracking-regression-tests/SKILL.md

@@ -1,9 +1,12 @@
 ---
-category: Research
 id: tracking-regression-tests
 name: Tracking Regression Tests
-description: This skill enables Claude to track and run regression tests, ensuring new changes don't break existing functionality. It is triggered when the user asks to "track regression", "run regression tests", or uses the shortcut "reg". The skill helps in maintaining code stability by identifying critical tests, automating their execution, and analyzing the impact of changes. It also provides insights into test history and identifies flaky tests. The skill uses the `regression-test-tracker` plugin.
-  This skill enables Claude to track and run regression tests, ensuring new changes don't break existing functionality. It is triggered when the user asks to "track regression", "run regression tests", or uses the shortcut "reg". The skill helps in maintaining code stability by identifying critical tests, automating their execution, and analyzing the impact of changes. It also provides insights into test history and identifies flaky tests. The skill uses the `regression-test-tracker` plugin.
+description: Track and run regression tests using the regression-test-tracker to ensure code stability and identify failures.
+category: Research
+requires: []
+examples:
+  - Run the regression test suite for the current development branch.
+  - Mark this specific unit test as a regression test for automated tracking.
 ---
 
 ## Overview