claude-comp-chem-skills

MIT Engaging cluster reference. SLURM job scheduling, Coley group nodes, filesystem layout, Python environments, and common commands. Use when working with the Engaging HPC cluster.

Verifies that bibliography references exist and have accurate metadata. Use when the user asks to "check references", "verify a bibliography", "validate citations", "check for hallucinated citations", "check a .bib file", or mentions auditing a BibTeX file for accuracy. Looks up each entry against Crossref and arXiv, then spawns web-search subagents for non-indexed sources, flagging missing DOIs, wrong years, title mismatches, venue mismatches, and broken URLs.

Reference guide for the MolGPU shared GPU cluster. Machine specs, SSH setup, shared filesystem, conda/mamba, multi-GPU training tips, and common commands.

VASP on MIT Engaging. VASP 6.4.2 (GCC/OpenMPI, Rocky 8 compatible), module loading, SLURM submission, POTCAR paths. Use when setting up or running VASP calculations on Engaging.

MassSpecGym benchmark reference — dataset schema, Python API, transforms, evaluation metrics, retrieval pipeline, and common patterns. Use when working with MassSpecGym data, models, or evaluation.

Machine learning workflows for molecular data. Use when building ML models with molecules, splitting datasets, selecting fingerprints for ML, avoiding data leakage, or doing scaffold-based train/test splits. Covers common pitfalls in molecular ML.

Python package and environment management using uv and mamba. Use when installing packages, creating virtual environments, setting up new projects, or managing dependencies. NOT for general Python coding questions.

Materials science toolkit. Crystal structures (CIF, POSCAR), phase diagrams, band structure, DOS, Materials Project integration, format conversion, for computational materials science.

Modern RDKit workflows for cheminformatics, including molecular fingerprints, drawing, and property calculations. Use when working with molecules, SMILES, molecular fingerprints (Morgan, ECFP, RDKit, atom pairs, topological torsions), molecule visualization/drawing, substructure search, or chemical property calculations. This skill provides up-to-date syntax patterns as RDKit's API evolves.

Graph Neural Networks (PyG). Node/graph classification, link prediction, GCN, GAT, GraphSAGE, heterogeneous graphs, molecular property prediction, for geometric deep learning.