mace

Atomistic structure manipulation, file-format conversion, supercells, surfaces, and geometry optimization with ASE

1 skill

Bayesian optimization for expensive material and molecular properties using Ax and BoTorch

1 skill

Generate, validate, and debug CP2K input files for periodic DFT, AIMD, and geometry optimization

1 skill

Analyze MD trajectories: RDF, MSD, order parameters, structure factor, PMFT, Voronoi, and clustering

1 skill

Biomolecular MD setup, execution, and analysis with GROMACS

1 skill

Write, review, and debug LAMMPS input scripts with physics-aware validation

1 skill

Full Domino Data Lab platform support — workspaces, jobs, model deployment, experiment tracking, GenAI tracing, Spark/Ray/Dask, and app deployment for data science teams

26 skills · 4 commands · 3 agents · 1 MCP server

Study companion for the Deep Learning textbook by Goodfellow, Bengio & Courville (MIT Press, 2016), free to read at deeplearningbook.org. Twenty chapter files, a glossary, patterns and a cheatsheet index the whole book, and a delta reference dates it against 2026 practice with per-claim confidence levels: double descent qualifying the U-curve, AdamW splitting weight decay from L2, transformers displacing Chapter 10's recurrence, diffusion growing out of Chapter 18's score matching. Four stdlib tools make it executable — a prerequisite-closed reading-path planner that refuses goals the book does not cover, a training diagnostic running Chapter 11's rules in priority order so a NaN is never reported as overfitting, a capacity planner that ranks 'shrink the model' last when overparameterized, and a parameter/FLOP/activation-memory calculator that refuses a stack whose shapes do not connect. A companion, not a compilation: the book is copyrighted, so nothing here reproduces its text.

1 skill · 3 commands · 1 agent

Data engineering, ML, and AI specialists - data pipelines, machine learning, LLM architecture

Explicit 8-step empirical-analysis pipeline in the traditional Python econometrics stack (pandas + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml): cleaning -> variable construction -> Table 1 -> diagnostics -> estimation (OLS/IV/DID/RDD/PSM/SCM/DML/Causal Forest) -> robustness -> mechanism/heterogeneity/mediation -> publication-ready tables & figures. Includes epidemiology and ML-causal modes.

1 skill

Agents for data engineering, machine learning, and AI development

12 agents

Operator-grade Mistral skill pack with 24 current API, security, reliability, governance, and migration workflows

24 skills