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Polar Local Environment

CommunityPopular
fcakyon
polar-local-environment

This skill should be used when setting up or managing Polar local development environment with Docker.

Overview

Publisherfcakyon
Repositoryclaude-codex-settings
Skill namepolar-local-environment
Stars
1.2K
Forks
113
Bundled files
8
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 8 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by fcakyon on GitHub. Read the source before you install it.

Installation

Install the Polar Local Environment AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/polar-skills/skills/polar-local-environment
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/fcakyon/claude-codex-settings.git /tmp/claude-codex-settings
mkdir -p .claude/skills
cp -r /tmp/claude-codex-settings/plugins/polar-skills/skills/polar-local-environment .claude/skills/polar-local-environment
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Polar Local Environment in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Polar Local Environment on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Polar Local Environment is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Local Environment Skill

Helps manage the Polar local development environment through the dev docker CLI. Use it to start, stop, debug, or reason about the local stack.

The two-part model

dev docker deliberately splits the stack so many worktrees can share one set of heavy infra:

  • Shared infra — one copy per machine, Docker project 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.
  • Per-instance app stack — one per worktree, project 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.

Instance auto-detection

dev docker auto-detects the instance for the current worktree, so -i is rarely needed. Priority:

  1. POLAR_DOCKER_INSTANCE pinned in dev/docker/.env.docker (dev docker set-instance N)
  2. The cross-worktree registry (~/.config/polar/docker-instances.json)
  3. Otherwise the lowest free number, then registered

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).

When to use

  • Start / stop / restart the local environment
  • View logs or debug a service that won't come up
  • Run several isolated worktree instances in parallel
  • Understand the service architecture or find a service's real port
  • Diagnose container or first-boot errors

Quick reference

TaskCommand
Start full stack (background)dev docker up -d
Start and block until healthydev docker up -d --wait
Start in foreground (stream logs)dev docker up --no-detach
Rebuild with fresh base imagesdev docker up -b --pull -d
Show this instance's ports/URLsdev docker ports (--json for tooling)
Write Claude Code preview configdev docker launch-json
Stop app stackdev docker down
Stop app and shared infradev docker down --all
Follow logsdev docker logs -f [service]
Print logs and exitdev docker logs --no-follow [service]
Statusdev docker ps
Restart a servicedev docker restart <service>
Shell into a servicedev docker shell <service>
One-off command in a servicedev docker exec <service> <cmd>
Reset this instancedev docker cleanup -f
Wipe ALL shared datadev docker cleanup --all -f
List every instancedev docker list
With monitoringdev docker up --monitoring -d

Services

ServiceProjectHost port (instance 0 / N)Notes
apipolar-app-<N>8000 / 8100+NFastAPI; /healthz healthcheck
webpolar-app-<N>3000 / 3100+NNext.js; healthchecked
workerpolar-app-<N>noneBackground jobs
dbpolar-sharednone (exec)PostgreSQL; DB polar_dev_<N>
redispolar-sharednone (exec)Redis DB index = N
miniopolar-shared9000, 9001S3; buckets polar-s3-<N>; console at 9001
tinybirdpolar-sharednone (exec)Analytics
prometheus / grafanapolar-sharednone (exec)--monitoring only

Discover the exact host ports for the current worktree with dev docker ports.

Instance port mapping

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.

InstanceAPIWeb
080003000
181013101
281023102
581053105

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.

Rules index

RuleCategoryDescription
service-architectureReferenceService details, ports, healthchecks
start-environmentOperationsStarting the stack (flags, --wait, --pull)
stop-environmentOperationsStopping and cleanup (app vs shared)
manage-instancesOperationsParallel worktree instances
view-logsDebuggingViewing service logs
shell-and-workflowsOperationsShell access and common dev workflows
troubleshootingDebuggingCommon errors and fixes
payment-testingOperationsLogin codes, Stripe webhooks, backoffice

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Polar Local Environment AI skill do?

This skill should be used when setting up or managing Polar local development environment with Docker.

Why use Polar Local Environment on TypingMind?

Because you install it once and use it with any model. Polar Local Environment is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Polar Local Environment in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/fcakyon/claude-codex-settings/tree/main/plugins/polar-skills/skills/polar-local-environment. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Polar Local Environment?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Polar Local Environment?

As many as you like. As long as a model supports skills, you can use Polar Local Environment with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Polar Local Environment AI skill free?

Yes. It is published on GitHub by fcakyon under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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