Elixir/Phoenix deployment patterns — Dockerfile, fly.toml, runtime.exs, mix release, rel/ overlays. Use when configuring Fly.io, Docker, CI/CD, health checks, or production migrations.
npx skills add https://github.com/oliver-kriska/claude-elixir-phoenix --skill deploy
Quick reference for deploying Elixir/Phoenix applications.
config.exs get baked into the release binary. Use runtime.exs with env vars so secrets are resolved at bootverify: :verify_peer allows MITM attacks between your app and database; production data traverses the connectionruntime.exs runs whenever arelease boots, including eval-based migration commands. Only require S3,
Redis, and similar credentials when that integration is enabled
if config_env() == :prod do
database_url = System.get_env("DATABASE_URL") || raise "DATABASE_URL is required"
secret_key_base = System.get_env("SECRET_KEY_BASE") || raise "SECRET_KEY_BASE is required"
host = System.get_env("PHX_HOST") || raise "PHX_HOST is required"
config :my_app, MyApp.Repo,
url: database_url,
pool_size: String.to_integer(System.get_env("POOL_SIZE") || "10"),
ssl: true,
ssl_opts: [verify: :verify_peer]
config :my_app, MyAppWeb.Endpoint,
url: [host: host, port: 443, scheme: "https"],
http: [ip: {0, 0, 0, 0}, port: String.to_integer(System.get_env("PORT") || "4000")],
secret_key_base: secret_key_base,
server: true
end
Keep core boot secrets such as DATABASE_URL and SECRET_KEY_BASE required.
Gate credentials for optional integrations behind the same feature switch that
enables the integration:
s3_config =
if System.get_env("STORAGE_BACKEND") == "s3" do
[
access_key_id:
System.get_env("S3_ACCESS_KEY") ||
raise("S3_ACCESS_KEY is required when STORAGE_BACKEND=s3"),
secret_access_key:
System.get_env("S3_SECRET_KEY") ||
raise("S3_SECRET_KEY is required when STORAGE_BACKEND=s3")
]
else
[]
end
config :my_app, :s3_config, s3_config
This lets release tasks that do not use S3 start without S3 credentials while
still failing fast when S3 is selected.
def call(%{path_info: ["health", "readiness"]} = conn, _opts) do
case Ecto.Adapters.SQL.query(MyApp.Repo, "SELECT 1", []) do
{:ok, _} -> send_resp(conn, 200, ~s({"status":"ok"})) |> halt()
{:error, _} -> send_resp(conn, 503, ~s({"status":"error"})) |> halt()
end
end
| Need | Use |
|------|-----|
| Simple, managed | Fly.io |
| Enterprise, existing K8s | Kubernetes |
| Custom infrastructure | Docker + your orchestrator |
| Resource | Recommendation |
|----------|----------------|
| CPU | NO LIMITS (BEAM scheduler issues) |
| Memory | Set limits (256Mi-512Mi typical) |
| Graceful shutdown | ≥ 60 seconds |
server: true in endpoint configPhoenix 1.8 uses esbuild + tailwind (no Node.js required):
config/config.exs under :esbuild and :tailwindmix assets.deploy builds for productionmix assets.setup installs binaries on first runconfig/config.exsFor detailed patterns, see:
references/docker-config.md - Multi-stage Dockerfile, best practicesreferences/flyio-config.md - fly.toml, clustering, commandsAssess Kubernetes workloads and cluster configuration for AKS Automatic compatibility. Identifies incompatibilities, generates fixes, and guides migration from AKS Standard to AKS Automatic. WHEN: migrate to AKS Automatic, check AKS Automatic readiness, validate manifests for Automatic, assess cluster for Automatic compatibility, fix deployment for Automatic compatibility, identify AKS Automatic migration blockers, is my cluster ready for AKS Automatic.
Discovers available Azure OpenAI model capacity across regions and projects. Analyzes quota limits, compares availability, and recommends optimal deployment locations based on capacity requirements. USE FOR: find capacity, check quota, where can I deploy, capacity discovery, best region for capacity, multi-project capacity search, quota analysis, model availability, region comparison, check TPM availability. DO NOT USE FOR: actual deployment (hand off to preset or customize after discovery), quota increase requests (direct user to Azure Portal), listing existing deployments.
Interactive guided deployment flow for Azure OpenAI models with full customization control. Step-by-step selection of model version, SKU (GlobalStandard/Standard/ProvisionedManaged), capacity, RAI policy (content filter), and advanced options (dynamic quota, priority processing, spillover). USE FOR: custom deployment, customize model deployment, choose version, select SKU, set capacity, configure content filter, RAI policy, deployment options, detailed deployment, advanced deployment, PTU deployment, provisioned throughput. DO NOT USE FOR: quick deployment to optimal region (use preset).
Unified Azure OpenAI model deployment skill with intelligent intent-based routing. Handles quick preset deployments, fully customized deployments (version/SKU/capacity/RAI policy), and capacity discovery across regions and projects. USE FOR: deploy model, deploy gpt, create deployment, model deployment, deploy openai model, set up model, provision model, find capacity, check model availability, where can I deploy, best region for model, capacity analysis. DO NOT USE FOR: listing existing deployments (use foundry_models_deployments_list MCP tool), deleting deployments, agent creation (use agent/create), project creation (use project/create).
Intelligently deploys Azure OpenAI models to optimal regions by analyzing capacity across all available regions. Automatically checks current region first and shows alternatives if needed. USE FOR: quick deployment, optimal region, best region, automatic region selection, fast setup, multi-region capacity check, high availability deployment, deploy to best location. DO NOT USE FOR: custom SKU selection (use customize), specific version selection (use customize), custom capacity configuration (use customize), PTU deployments (use customize).
This skill should be used when working with LaminDB, an open-source data framework for biology that makes data queryable, traceable, reproducible, and FAIR. Use when managing biological datasets (scRNA-seq, spatial, flow cytometry, etc.), tracking computational workflows, curating and validating data with biological ontologies, building data lakehouses, or ensuring data lineage and reproducibility in biological research. Covers data management, annotation, ontologies (genes, cell types, diseases, tissues), schema validation, integrations with workflow managers (Nextflow, Snakemake) and MLOps platforms (W&B, MLflow), and deployment strategies.
Latch platform for bioinformatics workflows. Build pipelines with Latch SDK, @workflow/@task decorators, deploy serverless workflows, LatchFile/LatchDir, Nextflow/Snakemake integration.
Run Python code in the cloud with serverless containers, GPUs, and autoscaling. Use when deploying ML models, running batch processing jobs, scheduling compute-intensive tasks, or serving APIs that require GPU acceleration or dynamic scaling.
Take oliver-kriska/claude-elixir-phoenix-deploy from the repository into ~/.claude/skills for personal
use, or into .claude/skills inside a project.
The agent identifies a skill by the name field in its header. Two skills with the
same name cannot sit side by side — one of them will be ignored.