Scientific Computing Suite

High-performance computing, physics/chemistry simulations, ML/DL, Julia, JAX, and data science workflows. Uses the Hub Skill architecture with 14 hubs routing to 112 sub-skills. Optimized for Claude Opus 4.7 with extended context and adaptive reasoning.

Version: 3.5.2 | 11 Agents | 2 Registered Commands | 17 Hubs → 110 Sub-skills | 5 Hook Events

Note

In v3.4.0, research-expert plus 5 methodology skills (research-methodology, research-quality-assessment, research-paper-implementation, scientific-communication, evidence-synthesis) moved to the new research-suite. This suite now focuses purely on computational work. Research-methodology delegations from science-suite agents route to the research-suite instead.

Agents

Agent: neural-network-master

Deep learning authority specializing in architecture design, theory, and implementation (Transformers, CNNs, diagnostics).

Model: opus

Version: 3.5.2

Agent: nonlinear-dynamics-expert

Expert in bifurcation analysis, chaos, coupled networks, pattern formation, and equation discovery (SINDy/UDE).

Model: opus

Version: 3.5.2

Agent: simulation-expert

Expert in molecular dynamics, statistical mechanics, and numerical methods (HPC/GPU).

Model: opus

Version: 3.5.2

Agent: statistical-physicist

Expert in correlation functions, non-equilibrium dynamics, and ensemble theory.

Model: opus

Version: 3.5.2

Agent: jax-pro

JAX expert — jit/vmap/pmap, sharding, VJP/JVP, XLA/HLO, Optax, Diffrax, Pallas, NumPyro. Delegates MD, bifurcation, general Bayes, and productionization to peers. Upgraded to opus in v3.5.2.

Model: opus

Version: 3.5.2

Agent: julia-pro

Julia/SciML expert — dispatch, type stability, DiffEq.jl, ModelingToolkit, SciMLSensitivity, UDE, SINDy, Turing, Optimization.jl. Delegates ML/HPC and productionization to peers. Upgraded to opus in v3.5.2.

Model: opus

Version: 3.5.2

Agent: julia-ml-hpc

Julia ML/HPC expert for Lux.jl, MLJ.jl, CUDA.jl, MPI.jl, and GNNLux. Delegates SciML/ODE to julia-pro.

Model: sonnet

Version: 3.5.2

Agent: pinn-engineer

Physics-informed AI for PINNs, NeuralPDE.jl, DeepXDE, BPINN/BNNODE, physics-constrained losses, and inverse PDEs. Renamed from ai-engineer in v3.5.2.

Model: sonnet

Version: 3.5.2

Agent: python-pro

Python systems engineer for production Python, type-driven design, PyO3/Rust extensions, async, and uv/ruff toolchain.

Model: sonnet

Version: 3.5.2

Agent: sci-workflow-engineer

Scientific LLM workflow engineer for JAX/Julia codegen prompts, experiment templates, scientific RAG, and AI-assisted pipelines. Renamed from prompt-engineer in v3.5.2.

Model: sonnet

Version: 3.5.2

Agent: ml-expert

Classical ML/MLOps with scikit-learn, XGBoost/LightGBM, Optuna, SHAP, and MLflow/W&B. Delegates DL to neural-network-master. Moved to haiku in v3.5.2.

Model: haiku

Version: 3.5.2

Registered Commands

Two slash commands registered in v3.5.2:

Command: md-sim

Molecular dynamics simulation setup, running, and trajectory analysis (GROMACS/OpenMM/JAX-MD).

Command: benchmark

Scientific code benchmarking across backends and hardware targets, with comparison reports.

Skill-Invoked Commands

These commands are triggered by skills, not directly by users:

Command: analyze-data

Analyze data files with statistical tests, visualization, and reproducible reporting.

Command: run-experiment

Design and execute computational experiments with hypothesis tracking.

The legacy paper-review command moved to research-suite and was then removed in favor of the scientific-review skill (produces a .docx deliverable with journal-specific adaptation — strictly better output).

Hub Skills

Skills use a hub architecture: 14 hub skills route to 117 specialized sub-skills.

Hub: nonlinear-dynamics (6 sub-skills)

Bifurcation analysis, chaos, coupled networks, pattern formation, and equation discovery.

  • bifurcation-analysis — BifurcationKit.jl: continuation, codimension-1/2 bifurcations, normal forms

  • chaos-attractors — Lyapunov exponents, attractor reconstruction, fractal dimension, recurrence

  • network-coupled-dynamics — Kuramoto synchronization, master stability, chimera states, epidemic models

  • pattern-formation — Turing instability, dispersion relations, spiral waves, amplitude equations

  • equation-discovery — SINDy, DataDrivenDiffEq.jl, PySINDy, sparse regression

  • jax-julia-interop — Bridge JAX and Julia SciML ecosystems via PythonCall.jl

Hub: jax-computing (6 sub-skills)

Core JAX, optimization, Bayesian inference, differential equations, and physics.

  • jax-mastery — JIT, vmap, grad, pmap, functional transformations

  • jax-core-programming — Pytrees, custom primitives, XLA operations, device memory

  • jax-optimization-pro — Optax, custom schedules, NLSQ, convergence diagnostics

  • jax-bayesian-pro — JAX-specific NumPyro integration and GPU-accelerated sampling

  • jax-diffeq-pro — Diffrax solvers, neural ODEs, stiff systems, adjoint methods

  • jax-physics-applications — JAX-MD, JAX-CFD, PINNs, differentiable physics

Hub: julia-language (13 sub-skills)

Core Julia, packages, compilation, performance, testing, CI/CD, visualization, HPC, and interop.

  • julia-mastery — Multiple dispatch, type system, metaprogramming, performance patterns

  • core-julia-patterns — Broadcasting, comprehensions, closures, standard library

  • package-management — Pkg.jl, Project.toml, Manifest.toml, environments

  • package-development-workflow — PkgTemplates.jl, documentation, versioning, registration

  • compiler-patterns — PackageCompiler.jl, system images, standalone executables

  • performance-tuning — @btime, memory allocation, SIMD, threading, type stability

  • julia-testing-patterns — Test.jl, Aqua.jl, JET.jl static analysis

  • ci-cd-patterns — GitHub Actions for Julia: test matrix, coverage, releases

  • visualization-patterns — Makie.jl, Plots.jl, interactive and publication-quality figures

  • web-development-julia — Genie.jl, HTTP.jl, REST APIs

  • julia-hpc-distributed — Distributed.jl, MPI.jl, SLURM, multi-node parallelism

  • interop-patterns — PythonCall.jl, RCall.jl, ccall, cross-language data exchange

  • ecosystem-selection — Choosing optimal Julia packages for a domain

Hub: julia-ml-and-dl (9 sub-skills)

Julia neural networks, architectures, training, AD backends, GPU kernels, GNNs, RL, pipelines, deployment.

  • julia-neural-networks — Flux.jl and Lux.jl: model definition, training loops

  • julia-neural-architectures — CNNs, RNNs, Transformers in Flux/Lux

  • julia-training-diagnostics — Loss curves, gradient norms, convergence monitoring

  • julia-ad-backends — Zygote.jl, Enzyme.jl, ForwardDiff.jl, DifferentiationInterface.jl

  • julia-gpu-kernels — CUDA.jl, KernelAbstractions.jl, custom GPU kernels

  • julia-graph-neural-networks — GraphNeuralNetworks.jl: GCN, GAT, message passing

  • julia-reinforcement-learning — ReinforcementLearning.jl: DQN, PPO, environments

  • julia-ml-pipelines — MLJ.jl: data pipelines, cross-validation, tuning

  • julia-model-deployment — ONNX export, HTTP.jl serving, PackageCompiler sysimages

Hub: sciml-and-diffeq (8 sub-skills)

SciML ecosystem, DifferentialEquations.jl, ModelingToolkit, optimization, neural PDEs.

  • sciml-ecosystem — Package selection guide for DifferentialEquations.jl, ModelingToolkit, Optimization.jl

  • sciml-modern-stack — Lux.jl neural networks, SciMLSensitivity adjoint methods, UDEs, DEQ

  • differential-equations — ODE/SDE/PDE solvers, callbacks, ensemble simulations

  • modeling-toolkit — Symbolic differential equations, automatic simplification

  • optimization-patterns — Optimization.jl for parameter estimation and inverse problems

  • neural-pde — NeuralPDE.jl: PINNs with ModelingToolkit

  • catalyst-reactions — Chemical reaction networks, deterministic and stochastic simulations

  • jump-optimization — JuMP.jl: LP, QP, NLP, MIP with HiGHS, Ipopt

Hub: correlation-analysis (4 sub-skills)

Mathematical foundations, physical systems, computational methods, and experimental data.

  • correlation-math-foundations — Two-point functions, cumulants, Fourier/Laplace transforms, Wiener-Khinchin

  • correlation-physical-systems — Condensed matter, soft matter, biological, non-equilibrium correlations

  • correlation-computational-methods — FFT-based autocorrelation, multi-tau correlators, JAX-accelerated GPU

  • correlation-experimental-data — DLS, SAXS/SANS, XPCS, FCS, rheology data interpretation

Hub: statistical-physics-hub (8 sub-skills)

Equilibrium/non-equilibrium statistical mechanics, stochastic dynamics, active matter, rare events, and extremes.

  • statistical-physics — Ensemble theory, partition functions, phase transitions; Julia Monte Carlo idioms (v3.1.5)

  • stochastic-dynamics — Master equations, Fokker-Planck direct PDE methods, Langevin, Green-Kubo, jump-diffusion SDEs (Fokker-Planck v3.1.5)

  • non-equilibrium-theory — Fluctuation theorems, entropy production, linear response, BAR/Jarzynski/MBAR with pymbar worked example (BAR example v3.1.7)

  • active-matter — Self-propelled particles, flocking, MIPS, bio-inspired materials

  • multiscale-modeling — Coarse-graining, DPD, nanoscale DEM

  • advanced-simulations — Non-equilibrium thermodynamics, multiscale bridging

  • rare-events-sampling — Large-deviation theory, cloning / importance splitting, SOC / sandpile / avalanche statistics (new in v3.1.4)

  • extreme-value-statistics — GEV/GPD/Hill/Pickands/POT, return levels, non-stationary EVT (new in v3.1.4)

Hub: deep-learning-hub (6 sub-skills)

Architecture design, mathematical foundations, training diagnostics, experimentation, large-scale systems.

  • deep-learning — Core DL: feedforward networks, backpropagation, regularization

  • neural-architecture-patterns — CNNs, RNNs, Transformers, diffusion models, normalizing flows

  • neural-network-mathematics — Universal approximation, optimization landscapes, generalization theory

  • training-diagnostics — Loss curves, gradient pathologies, learning rate tuning

  • deep-learning-experimentation — Ablations, HPO, reproducibility, benchmarks

  • advanced-ml-systems — Distributed training, mixed precision, gradient checkpointing

Hub: ml-and-data-science (7 sub-skills)

Classical ML, data analysis, wrangling, statistics, visualization, curve fitting, experiment tracking.

  • machine-learning — Scikit-learn, XGBoost, LightGBM, feature engineering

  • data-analysis — Pandas, descriptive statistics, correlation, hypothesis testing

  • data-wrangling-communication — Data cleaning, transformation, stakeholder communication

  • statistical-analysis-fundamentals — Distributions, hypothesis tests, confidence intervals

  • scientific-visualization — Matplotlib, seaborn, plotly, domain-specific plots

  • nlsq-core-mastery — JAX-accelerated non-linear least squares curve fitting

  • experiment-tracking — MLflow, Weights & Biases, DVC

Hub: llm-and-ai (5 sub-skills)

LLM application development, evaluation, LangChain, RAG, and NLP.

  • llm-application-dev — LLM-powered apps: API integration, streaming, tool use, agents

  • llm-evaluation — Benchmarks, LLM-as-judge, human evaluation, quality metrics

  • langchain-architecture — LangChain/LangGraph: chains, agents, memory, tools

  • rag-implementation — Vector stores, chunking, re-ranking, hybrid retrieval

  • nlp-fundamentals — Tokenization, embeddings, NER, text classification

Hub: ml-deployment (6 sub-skills)

Model serving, optimization, production engineering, pipelines, infrastructure, federated learning.

  • model-deployment-serving — FastAPI, TorchServe, Triton, BentoML, REST/gRPC

  • model-optimization-deployment — Quantization, pruning, ONNX, TensorRT, mobile

  • ml-engineering-production — Type-safe code, testing, data pipelines, monitoring, drift

  • ml-pipeline-workflow — Airflow, Prefect, Metaflow, automated retraining

  • devops-ml-infrastructure — Docker, Kubernetes, GPU provisioning, cloud ML

  • federated-learning — Federated averaging, differential privacy, PySyft

Hub: simulation-and-hpc (10 sub-skills)

MD simulations, trajectory analysis, ML force fields, parallel computing, GPU, numerical methods, and applied math.

  • md-simulation-setup — GROMACS/LAMMPS force fields, equilibration protocols

  • trajectory-analysis — MDAnalysis, RMSD, RDF, free energy, clustering

  • ml-force-fields — NequIP, MACE, DeePMD, active learning workflows

  • parallel-computing — MPI, OpenMP, Dask, Ray, scalability analysis

  • gpu-acceleration — CUDA, ROCm, JAX pmap, GPU-optimized algorithms

  • numerical-methods-implementation — Finite difference/element, spectral methods, iterative solvers

  • signal-processing — FFT, filtering, spectral estimation, wavelet transforms

  • time-series-analysis — ARIMA, state space models, changepoint detection

  • advanced-optimization — Genetic algorithms, simulated annealing, basin hopping

  • control-theory — PID, LQR, MPC, stability analysis

Hub: research-and-domains (14 sub-skills)

Scientific software engineering, specialized computational domains, and autonomous self-improvement research.

Note

In v3.4.0, 5 methodology sub-skills (research-methodology, research-paper-implementation, research-quality-assessment, scientific-communication, evidence-synthesis) moved to the research-suite. This hub now focuses on computational-engineering-for-science rather than methodology.

  • robust-testing — Property-based, metamorphic, and tolerance-aware testing for scientific code

  • python-development — Idiomatic Python, software engineering for science

  • python-packaging-advanced — uv workspaces, monorepos, reproducible builds

  • rust-extensions — PyO3/maturin high-performance Python extensions

  • type-driven-design — Protocols, Generics, static analysis with pyright/mypy

  • modern-concurrency — asyncio TaskGroups, threading, multiprocessing

  • quantum-computing — Qiskit, PennyLane, VQE/QAOA

  • bioinformatics — Genomics, proteomics, BioPython

  • computer-vision — Image processing, detection, Vision Transformers

  • reinforcement-learning — Gymnasium, Stable-Baselines3, RLlib

  • symbolic-math — SymPy, CAS, algebraic solvers

  • self-improving-ai — Research overview for autonomous self-improvement (research-framework counterpart to agent-core’s self-improving-agents)

  • dspy-basics — Depth-skill companion for DSPy programmatic prompt optimization

  • rlaif-training — Depth-skill companion for Constitutional AI / RLAIF / DPO

Hub: bayesian-inference (10 sub-skills)

NumPyro, Turing.jl, variational inference, MCMC diagnostics, consensus / non-reversible tempering, Bayesian UDEs, Bayesian PINNs, point processes, and Bayesian SINDy equation discovery.

  • numpyro-core-mastery — NumPyro: NUTS/HMC, SVI, hierarchical models, GPU inference

  • turing-model-design — Turing.jl: probabilistic models, Julia-native Bayesian workflows

  • variational-inference-patterns — ELBO, mean-field, normalizing flows, amortized inference

  • mcmc-diagnostics — R-hat, ESS, BFMI, trace plots, ArviZ convergence diagnostics

  • consensus-mcmc-pigeons — Scott-2016 divide-and-conquer Consensus MC and Pigeons.jl non-reversible parallel tempering (new in v3.1.4)

  • bayesian-ude-workflow — Turing + DiffEq + Lux staged pipeline for Bayesian Universal Differential Equations (new in v3.1.4)

  • bayesian-ude-jax — Python/JAX counterpart to Bayesian UDE via Diffrax + Equinox + NumPyro (new in v3.1.4)

  • bayesian-pinn — BNNODE / BayesianPINN (extracted from neural-pde for budget management) (new in v3.1.4)

  • point-processes — Hawkes processes, HSGP, Julia PointProcesses.jl, non-parametric Hawkes EM (new in v3.1.4)

  • bayesian-sindy-workflow — Horseshoe-prior Bayesian SINDy with 5-stage Lorenz-63 worked example (NumPyro + NUTS + ArviZ PSIS-LOO), prior-sensitivity analysis, and Turing UQ-SINDy sidebar (new in v3.1.7 — extracted from equation-discovery to resolve 88% budget pressure)

Hooks

5 hook events with Python script implementations:

  • SessionStart — Detect JAX devices, GPU availability, Julia env

  • PreToolUse — Warn before commands that could corrupt simulations

  • PostToolUse — NaN/Inf check on compute job output (numerical integrity)

  • SessionEnd — Persist structured progress summary for next session

  • SubagentStop — Collect results from parallel science agents

(ExecutionError was removed in v3.4.0 — not supported by the CC v2.1.113 CLI event schema.)