flyte

Deploy a complete Flyte stack (flyte-binary + a hosted PostgreSQL + an object store) onto a kind cluster, running on the user's own machine or a DigitalOcean VM (droplet). PostgreSQL is hosted (Supabase or external); the object store is AWS S3 or Cloudflare R2. Use when the user wants to run Flyte on kind — either reusing an existing kind cluster or creating a new one. For evaluation only (no TLS/auth on the base deployment; optional OIDC auth via Traefik + oauth2-proxy is covered).

Deploy Flyte on a kind cluster onto any host — your local machine, or a fresh cloud VM (DigitalOcean, AWS EC2, or GCP Compute Engine). Provisions the VM (firewall scoped to your IP), installs Docker/kind/kubectl/helm, then runs the kind Flyte deploy on that host and tunnels access back to your machine. Use when the user wants Flyte-on-kind but hasn't decided where to run it, or wants it on a cloud VM rather than locally. For evaluation only (single-node kind, static credentials). Delegates the Flyte install itself to the deploy-flyte-kind skill.

Use when deploying a Flyte v2 (flyte-binary / flyte2) cluster on AWS from scratch — provisions EKS + S3 + RDS PostgreSQL + AWS Load Balancer Controller, then helm-installs the flyte-binary chart behind an ALB, with optional TLS and Okta SSO. Trigger words: "deploy flyte", "flyte v2 on AWS", "flyte EKS".

Entry-point orchestrator for porting Flyte 1 (flytekit) code to Flyte 2 (flyte). Explains the v1 to v2 shift, the terminology mapping, a recommended migration strategy, hybrid v1/v2 pipelines, and routes to sibling migration skills. Use when the user wants to migrate, port, or upgrade Flyte 1 (flytekit) code to Flyte 2. Trigger words are migrate, flytekit, v1 to v2, port, upgrade, convert workflow.

Migrates Flyte 1 task configuration, container images, resources, caching, secrets, scheduling, and CLI/config-file usage to Flyte 2 equivalents. Use when migrating Flyte 1 task configuration, images, resources, secrets, scheduling, or CLI/config-file usage to Flyte 2. Trigger words include resources, ImageSpec, cache_version, secrets, LaunchPlan, CronSchedule, pyflyte, config, register, and deploy.

Migrates Flyte 1 branching, dynamic workflows, failure handling, and fan-out to native Flyte 2 Python. Use when migrating Flyte 1 branching, dynamic workflows, failure handling, or map_task/fan-out to Flyte 2. Trigger words: conditional, @dynamic, map_task, on_failure, branching, parallelism, fan-out, flyte.map, asyncio.gather.

Migrates Flyte 1 data types and offloaded I/O to Flyte 2. Use when migrating Flyte 1 data types and I/O (files, directories, dataframes, dataclasses) to Flyte 2, converting FlyteFile, FlyteDirectory, or StructuredDataset to flyte.io.File, flyte.io.Dir, and flyte.io.DataFrame. Trigger words are FlyteFile, FlyteDirectory, StructuredDataset, DataFrame, dataclass, Pydantic, type, I/O, and serialization.

Migrates Flyte 1 machine learning code to Flyte 2 and unlocks net-new v2 patterns. Use when migrating Flyte 1 ML workloads (training, HPO, GPU/deep learning, batch inference) to Flyte 2, specifying GPU resources, or building the end-to-end pipeline pattern. Trigger words - migrate training, HPO, GPU, deep learning, batch inference, model serving, pytorch.

Migrates Slurm (sbatch/srun) HPC workloads to Flyte 2 (the flyte Python SDK) — job scripts become typed tasks, `#SBATCH` pragmas become TaskEnvironment config, job arrays become flyte.map or asyncio.gather, and multi-node training becomes a clustered task environment. Use when porting an HPC or supercomputer cluster workload off Slurm, translating `#SBATCH` pragmas, or replacing sbatch chains, job arrays, and module load with Flyte. Trigger words are sbatch, srun, SLURM, `#SBATCH`, HPC, job array, partition, squeue, sinfo, module load, scancel, supercomputer, cluster migration.

Migrates Flyte 1 tasks and workflows to Flyte 2, where the @task, @workflow, and @dynamic decorators collapse into a single @env.task on a flyte.TaskEnvironment and a workflow becomes a task that calls other tasks. Use when the user is migrating Flyte 1 tasks/workflows to Flyte 2. Trigger words: migrate task, migrate workflow, @task, @workflow, @dynamic, TaskEnvironment, env.task.

Builds durable agents with Flyte 2 — ReAct patterns, Plan-and-Execute, LangGraph integration, PydanticAI integration, OpenAI Agents SDK integration, agent memory, MCP tool integration, skills, and agent chat UI. Use when the user wants to build AI agents, implement ReAct loops, integrate agent frameworks, add agent memory, or build agent-powered workflows. Trigger words: "agent", "ReAct", "LangGraph", "PydanticAI", "OpenAI agents", "MCP", "tool calling", "memory", "skills", "agentic".

Builds and serves Flyte 2 apps — FastAPI, Streamlit, vLLM, SGLang, WebSocket, and browser apps. Use when the user wants to serve a model, create a REST API, build a dashboard, deploy an LLM backend, or create a web app with Flyte. Trigger words: "app", "serve", "deploy app", "FastAPI", "Streamlit", "vLLM", "SGLang", "REST API", "dashboard", "serving", "endpoint", "webhook", "WebSocket".

Creates Flyte 2 project scaffolds (tasks, workflows, launch plans, apps), selects patterns (map tasks, traces, dynamic workflows, conditions), and generates code from templates aligned to the user's constraints. Use when the user wants to create or scaffold Flyte workflows, tasks, or apps from scratch — "write a Flyte workflow", "create a task", "scaffold a Flyte project", "build a Flyte pipeline". Trigger words: "author", "create", "scaffold", "write a workflow", "write a task", "Flyte project structure".

Handles data engineering patterns: ETL pipelines, data processing, data quality checks, fanout/map tasks, conditions, dynamic workflows, and batch data transformations. Use when the user wants to build ETL pipelines, process large datasets, run data quality checks, fan out data processing tasks, or handle batch data transformations. Trigger words: "ETL", "data pipeline", "data processing", "fanout", "map", "transform", "data quality", "parquet", "CSV", "batch", "extract", "load", "validate", "schema".

Builds minimal evaluation harnesses (unit tests + small-run workflows) and suggests ways to validate correctness and performance early. Use when the user wants to test Flyte tasks, validate pipeline outputs, set up evaluation pipelines, or write unit tests for ML/data workflows. Trigger words: "test", "evaluate", "validation", "unit test", "verify", "assert", "data quality", "metrics", "benchmark".

Handles ML workload patterns: model training, hyperparameter optimization, experiment tracking, model evaluation and selection, batch inference, real-time serving, and model monitoring. Use when the user wants to train models, run hyperparameter search, track experiments, evaluate models, do batch or real-time inference, or set up model monitoring. Trigger words: "train", "training", "hyperparameter", "HPO", "experiment", "tracking", "evaluation", "inference", "batch inference", "model serving", "monitoring", "GPU", "PyTorch", "TensorFlow", "scikit-learn", "HuggingFace", "model".

Suggests performance improvements (task granularity, caching, resource requests, data format changes) using observed run metadata when available. Use when the user wants to optimize workflow performance, debug slow tasks, configure caching, tune resources, or improve throughput. Trigger words: "optimize", "performance", "slow", "cache", "caching", "resource", "throughput", "latency", "speed up", "bottleneck", "profiling", "metadata".

Runs Flyte 2 workflows, interacts with runs and actions, retrieves logs and data, and manages run lifecycle. Use when the user wants to run a workflow, check run status, view logs, get run outputs, re-run a workflow, or manage runs programmatically. Trigger words: "run", "execute", "logs", "status", "output", "input", "watch", "rerun", "cancel", "abort", "run metadata", "action".

Generates flyte.Image specs, Dockerfiles, dependency management, image tagging strategy, and reproducible build instructions for Flyte tasks. Use when the user needs to build container images for Flyte tasks, configure custom images, manage dependencies, or set up reproducible builds. Trigger words: "image", "Docker", "build", "dependency", "pip package", "debian base", "image builder", "push image", "container", "Dockerfile", "uv", "requirements".

Guides correct types, I/O, and serialization for common data (Pandas, Arrow, Parquet, images, audio, HF datasets), including data locality and storage best practices. Use when the user needs help with type annotations, data serialization, file I/O between tasks, custom type transformers, DataFrame handling, or choosing the right Flyte type for their data. Trigger words: "type", "serialize", "deserialize", "DataFrame", "File", "Directory", "custom type", "data format", "Parquet", "Arrow", "Pandas", "type transformer".

Deploy Dex as a local in-cluster OIDC provider (IdP stand-in) for a kind-based Flyte deployment, so oauth2-proxy can be tested with no cloud account or real users. Use when the user wants to stand up Dex locally for testing Flyte authentication. For local testing only — in-memory storage, static test passwords.