Agent Reference

25 Agents across 4 suites | Version: 3.5.2

Agents are specialized AI personas with defined model tiers, tool access, and domain expertise. Each agent runs at a specific model tier: opus (deep reasoning), sonnet (standard tasks), or haiku (fast/simple).

Agent Core Suite (agent-core) — 3 Agents

Core orchestration, reasoning, and context engineering.

Agent

Model

Description

orchestrator

opus

Multi-agent orchestrator for workflow coordination, agent team assembly, and task allocation

reasoning-engine

opus

Advanced reasoning, prompt design, and cognitive tasks. Masters Chain-of-Thought and structured frameworks

context-specialist

opus

Context engineering specialist for dynamic context management, vector databases, and memory systems

Dev Suite (dev-suite) — 9 Agents

Full-stack engineering, infrastructure, CI/CD, quality assurance, and debugging.

Agent

Model

Description

software-architect

opus

Scalable backend systems, microservices, and high-performance APIs (REST/GraphQL/gRPC)

debugger-pro

opus

AI-assisted debugging, log correlation, and complex root cause analysis across distributed systems

app-developer

sonnet

Web, iOS, and Android applications. Masters React, Next.js, Flutter, and React Native

automation-engineer

sonnet

Software delivery pipelines and Git collaboration. Masters GitHub Actions and GitLab CI

devops-architect

sonnet

Multi-cloud architecture (AWS/Azure/GCP), Kubernetes, and Infrastructure as Code (Terraform/Pulumi)

quality-specialist

sonnet

Code reviews, security audits, and test automation strategies

sre-expert

sonnet

System reliability, observability (monitoring, logging, tracing), and incident response

systems-engineer

sonnet

Low-level systems programming (C, C++, Rust, Go) and production-grade CLI tools

documentation-expert

haiku

Technical documentation, manuals, and tutorials

Research Suite (research-suite) — 2 Agents

Scientific research workflows: peer review, idea-to-plan refinement, and methodology orchestration. New in v3.4.0 (split from science-suite).

Agent

Model

Description

research-expert

opus

Unified specialist for research methodology, evidence synthesis (PRISMA/GRADE), statistical rigor, IMRaD structuring, paper-to-code reproduction, and publication-quality visualization (one-off tasks)

research-spark-orchestrator

opus

Autonomous driver for the 8-stage research-spark refinement pipeline; owns _state.yaml, enforces artifact contract, fans out to sub-agents at Stages 2/6/8

Science Suite (science-suite) — 11 Agents

Scientific computing, HPC, physics simulations, ML/DL, and nonlinear dynamics. research-expert moved to research-suite in v3.4.0. In v3.5.2, jax-pro and julia-pro upgraded to opus; ml-expert moved to haiku.

Agent

Model

Description

jax-pro

opus

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

julia-pro

opus

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

neural-network-master

opus

Deep learning authority: architecture design, theory, and implementation (Transformers, CNNs, diagnostics)

nonlinear-dynamics-expert

opus

Bifurcation analysis, chaos, coupled networks, pattern formation, and equation discovery (SINDy/UDE)

simulation-expert

opus

Molecular dynamics, statistical mechanics, and numerical methods (HPC/GPU)

statistical-physicist

opus

Correlation functions, non-equilibrium dynamics, and ensemble theory

julia-ml-hpc

sonnet

Julia ML, Deep Learning, and HPC (Lux.jl, MLJ.jl, CUDA.jl, MPI.jl, GNNLux)

pinn-engineer

sonnet

Physics-informed AI for PINNs, NeuralPDE.jl, DeepXDE, BPINN/BNNODE, and inverse PDEs

python-pro

sonnet

Python systems engineering: type-driven development, Rust extensions, and performance

sci-workflow-engineer

sonnet

Scientific LLM workflows: JAX/Julia codegen prompts, experiment templates, and AI-assisted pipelines

ml-expert

haiku

Classical ML/MLOps with scikit-learn, XGBoost/LightGBM, Optuna, SHAP, and MLflow/W&B

Model Tier Summary

Tier

Count

Agents

opus

13

orchestrator, reasoning-engine, context-specialist, software-architect, debugger-pro, research-expert, research-spark-orchestrator, jax-pro, julia-pro, neural-network-master, nonlinear-dynamics-expert, simulation-expert, statistical-physicist

sonnet

10

app-developer, automation-engineer, devops-architect, quality-specialist, sre-expert, systems-engineer, julia-ml-hpc, pinn-engineer, python-pro, sci-workflow-engineer

haiku

2

documentation-expert, ml-expert

Cross-Suite Delegation

Agents delegate across suite boundaries when tasks require multiple domains. Key patterns:

From

To

Boundary

software-architect

devops-architect

Architecture ↔ Infrastructure

julia-pro

julia-ml-hpc

SciML/ODE ↔ ML training/GPU/HPC

julia-pro

software-architect (dev-suite)

Scientific computing ↔ Productionization/API design

jax-pro

software-architect (dev-suite)

JAX implementation ↔ Productionization/API design

neural-network-master

julia-ml-hpc

DL theory ↔ Julia implementation

nonlinear-dynamics-expert

jax-pro / julia-pro

Theory ↔ Implementation

statistical-physicist

jax-pro

Theory ↔ JAX implementation

See the Integration Map for full delegation patterns and MCP server roles.

Resources

Generated from v3.5.2 validated marketplace data.