This skill should be used when setting up or managing Polar local development environment with Docker.
npx skills add https://github.com/fcakyon/claude-codex-settings --skill polar-local-environment
Helps manage the Polar local development environment through the dev docker
CLI. Use it to start, stop, debug, or reason about the local stack.
dev docker deliberately splits the stack so many worktrees can share one set
of heavy infra:
polar-shared:postgres, redis, minio, tinybird, and optional prometheus/grafana. Postgres
and redis publish no host ports; MinIO exposes 9000/9001 for browser
uploads. Use dev docker exec <service> ... for services without host ports.
polar-app-<N>: api,worker, web. Only api and web publish host ports, offset per instance so
worktrees don't collide.
Knowing this prevents the most common confusion: there is no localhost:5432
for the database (it lives in the shared stack and is reached through
dev docker exec db ...). MinIO does expose ports 9000/9001 for browser
uploads and console access.
dev docker auto-detects the instance for the current worktree, so -i is
rarely needed. Priority:
POLAR_DOCKER_INSTANCE pinned in dev/docker/.env.docker (dev docker set-instance N)CONDUCTOR_PORT env var → (port - 55000) / 10 + 1~/.config/polar/docker-instances.json)Run dev docker ports to see the resolved instance and its URLs (add --json
for tooling). To wire this worktree into Claude Code's preview, run
dev docker launch-json, which writes a per-instance .claude/launch.json with
the correct ports (it's gitignored, so regenerate after set-instance).
| Task | Command |
|------|---------|
| Start full stack (background) | dev docker up -d |
| Start and block until healthy | dev docker up -d --wait |
| Start in foreground (stream logs) | dev docker up --no-detach |
| Rebuild with fresh base images | dev docker up -b --pull -d |
| Show this instance's ports/URLs | dev docker ports (--json for tooling) |
| Write Claude Code preview config | dev docker launch-json |
| Stop app stack | dev docker down |
| Stop app and shared infra | dev docker down --all |
| Follow logs | dev docker logs -f [service] |
| Print logs and exit | dev docker logs --no-follow [service] |
| Status | dev docker ps |
| Restart a service | dev docker restart <service> |
| Shell into a service | dev docker shell <service> |
| One-off command in a service | dev docker exec <service> <cmd> |
| Reset this instance | dev docker cleanup -f |
| Wipe ALL shared data | dev docker cleanup --all -f |
| List every instance | dev docker list |
| With monitoring | dev docker up --monitoring -d |
| Service | Project | Host port (instance 0 / N) | Notes |
|---------|---------|----------------------------|-------|
| api | polar-app-<N> | 8000 / 8100+N | FastAPI; /healthz healthcheck |
| web | polar-app-<N> | 3000 / 3100+N | Next.js; healthchecked |
| worker | polar-app-<N> | none | Background jobs |
| db | polar-shared | none (exec) | PostgreSQL; DB polar_dev_<N> |
| redis | polar-shared | none (exec) | Redis DB index = N |
| minio | polar-shared | 9000, 9001 | S3; buckets polar-s3-<N>; console at 9001 |
| tinybird | polar-shared | none (exec) | Analytics |
| prometheus / grafana | polar-shared | none (exec) | --monitoring only |
Discover the exact host ports for the current worktree with dev docker ports.
Only api and web get host ports: Port = Base + Instance (Base 8100 for api,
3100 for web) for instances 1–99. Instance 0 uses the legacy 8000 / 3000.
| Instance | API | Web |
|----------|-----|-----|
| 0 | 8000 | 3000 |
| 1 | 8101 | 3101 |
| 2 | 8102 | 3102 |
| 5 | 8105 | 3105 |
Everything else is per-instance but not on a host port: database polar_dev_<N>,
redis DB index <N>, buckets polar-s3-<N> / polar-s3-public-<N>. Reach them
via dev docker exec <service> or docker exec polar-shared-<service>-1.
| Rule | Category | Description |
|------|----------|-------------|
| service-architecture | Reference | Service details, ports, healthchecks |
| start-environment | Operations | Starting the stack (flags, --wait, --pull) |
| stop-environment | Operations | Stopping and cleanup (app vs shared) |
| manage-instances | Operations | Parallel worktree instances |
| view-logs | Debugging | Viewing service logs |
| shell-and-workflows | Operations | Shell access and common dev workflows |
| troubleshooting | Debugging | Common errors and fixes |
| payment-testing | Operations | Login codes, Stripe webhooks, backoffice |
Assess 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 fcakyon/polar-local-environment 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.