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N8n Self Hosting

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czlonkowski
n8n-self-hosting

Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, provision, or deploy n8n on their own server/VPS (Hetzner, DigitalOcean, AWS EC2, bare metal) — single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance, or make Python Code nodes run on it (task runners). For SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect domain/SSH/timezone inputs, generate fresh secrets on the box, and bring the stack up with TLS. Trigger on "deploy n8n", "self-host n8n", "n8n docker compose", "n8n queue mode / workers", "n8n reverse proxy / SSL", "back up / update my n8n", "Python runner unavailable" / "n8nio/runners sidecar", or "we don't want to give every user the OAuth client secret" / "enable Sign in with Google" (credential overwrites).

Overview

Publisherczlonkowski
Repositoryn8n-skills
Skill namen8n-self-hosting
Stars
6.2K
Forks
1K
Bundled files
10
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.

  • 10 bundled files

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

  • Open source

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

Installation

Install the N8n Self Hosting 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.
  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/czlonkowski/n8n-skills.git /tmp/n8n-skills
mkdir -p .claude/skills
cp -r /tmp/n8n-skills/skills/n8n-self-hosting .claude/skills/n8n-self-hosting
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable N8n Self Hosting 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 N8n Self Hosting 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 N8n Self Hosting 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.

Deploying self-hosted n8n

This skill takes a fresh Linux VM (Ubuntu/Debian, root or sudo SSH) to a running, HTTPS, production n8n via Docker Compose behind Caddy (automatic Let's Encrypt TLS). It is for self-hosted n8n on Docker — not n8n Cloud, and not for building workflows (that's the rest of this pack).

Two deployment modes. The architectures differ, so pick the mode before doing anything.

You drive this end-to-end over SSH: preflight → install Docker → lay down the project → generate secrets → launch → verify TLS → hand off. The template files live in assets/; the per-mode and security depth live in the reference files named below.

Rule 0 — choose the mode (ask the user)

Do not guess. Ask, then commit to one:

Single / regularQueue
Processesone n8nmain + N workers
Extra servicesnone (SQLite)Redis (queue) + Postgres (DB)
Executes workflowsin the main processon workers, in parallel
Good for1 user, light/moderate load, simplest opshigh volume, heavy/long executions, horizontal scale
Composeassets/docker-compose.single.ymlassets/docker-compose.queue.yml
Deep diveSINGLE_MODE.mdQUEUE_MODE.md

If unsure, start single — it's the simplest correct thing and covers most needs. Moving to queue later means swapping the compose file and migrating SQLite→Postgres, so if the user already expects real volume, start queue.

Rule 1 — secret hygiene (non-negotiable)

A misstep here leaks client credentials. Be diligent:

  1. Generate every secret fresh, on the target box. Never copy an encryption key, DB password, or .env from another n8n instance into this one. See SECURITY.md for the openssl commands.
  2. Secrets live only in .env (mode 600), referenced by the compose as ${VAR}. Never inline a secret into docker-compose.yml, the Caddyfile, or anything you commit.
  3. The N8N_ENCRYPTION_KEY is sacred. It encrypts every stored credential. If it's lost or changes, all saved credentials become undecryptable. Set it explicitly, and tell the user to back it up off the box. Don't echo it into long-lived logs or chat history beyond what's needed to hand it over.
  4. Never expose internal services. Only Caddy (80/443) is public. n8n (5678), Postgres (5432), Redis (6379) stay on the private Docker network — the templates already omit their host port mappings. Don't add them.
  5. .env and Caddy's caddy_data volume (the issued certs + ACME account key) are not artifacts to share. If you're working inside a git repo, confirm .env is git-ignored before any commit.

Inputs to collect up front

  • SSH targetuser@host and how you authenticate (key path or the user confirms the agent already has access). Root or a sudo user.
  • Domain — the full hostname n8n will live at, e.g. n8n.example.com (→ SUBDOMAIN=n8n, DOMAIN_NAME=example.com). The user must control its DNS.
  • TLS email — for Let's Encrypt (SSL_EMAIL).
  • Timezone — IANA name for Schedule/Cron nodes (e.g. Europe/Warsaw), else Etc/UTC.
  • Mode — single or queue (Rule 0). Queue → confirm the box has enough RAM (rough floor ~4 GB; each worker wants ~1–2 GB).
  • Optional modules — some features (currently Agents) are backend modules that stay off unless listed in N8N_ENABLED_MODULES. Ask only if the user brings one up; if they do, read the modules section of QUEUE_MODE.md before enabling it, because in queue mode it has to reach the workers as well.
  • Python Code nodes / who edits workflows — ask "Will workflows use Python Code nodes, or will anyone besides you edit workflows?" A yes to either means task runners in external mode: a n8nio/runners sidecar (one per worker in queue mode). The stock n8nio/n8n image has no Python 3, so in the default internal mode every Python Code node fails with Python runner unavailable: Python 3 is missing from this system. Read TASK_RUNNERS.md before step 3.

The deploy flow

Work through these in order. SINGLE_MODE.md / QUEUE_MODE.md give the mode-specific command detail; SECURITY.md covers secret generation and hardening; DAY2.md covers update/backup/restore.

1. Preflight (the cheapest failure is the one you catch here)

  • SSH in; confirm the OS is Debian/Ubuntu-like (. /etc/os-release).
  • DNS must already point at the box. Compare the box's public IP (curl -s ifconfig.me) with dig +short <fqdn> (run it from the box AND ideally your laptop). If they don't match, stop — Caddy's ACME challenge will fail. Have the user create the A record, wait for it to propagate, then continue.
  • Ports 80 and 443 must be reachable from the internet. Check the host firewall AND any cloud security group / network firewall (Hetzner Cloud, AWS SG, etc.) — these are outside the box and a common silent blocker.

2. Install Docker (if absent)

  • Check docker --version and docker compose version. If missing, install Docker Engine + the Compose plugin (Docker's official get.docker.com script on Ubuntu/Debian is fine). Re-check docker compose version before proceeding.

3. Lay down the project

  • Pick DATA_FOLDER — an absolute path, e.g. /opt/n8n. The DATA_FOLDER value in .env must equal this exact directory (the compose mounts ${DATA_FOLDER}/caddy_config/Caddyfile, and init-data.sh is mounted via a relative ./ path), so always run docker compose from here. Create it, plus caddy_config/ and local_files/ inside.
  • Get the template files onto the box. They live in this skill's assets/ on your machine, not on the server — transfer each one. Either scp them up, or (no local copy needed) write each file's contents over SSH, e.g. ssh <target> 'cat > <DATA_FOLDER>/docker-compose.yml' < assets/docker-compose.single.yml. Land them with these exact names:
    • the chosen compose → <DATA_FOLDER>/docker-compose.yml (rename it to exactly this)
    • Caddyfile<DATA_FOLDER>/caddy_config/Caddyfile
    • queue only: init-data.sh<DATA_FOLDER>/init-data.sh, then chmod +x it
    • the matching .env.*.example<DATA_FOLDER>/.env
  • External task runners (Python): add the runner env vars and the task-runners sidecar to the compose now (TASK_RUNNERS.md has the snippet for each mode). Generate N8N_RUNNERS_AUTH_TOKEN in step 4 with the other secrets.

4. Fill .env + generate secrets

  • Set DATA_FOLDER, DOMAIN_NAME, SUBDOMAIN, SSL_EMAIL, GENERIC_TIMEZONE.
  • Generate each secret on the box with openssl (SECURITY.md has the commands) and write it into .env, replacing the matching REPLACE_WITH_… placeholder: N8N_ENCRYPTION_KEY; queue also POSTGRES_PASSWORD + POSTGRES_NON_ROOT_PASSWORD.
  • Before launching, confirm none are left unset: grep REPLACE_WITH_ .env must return nothing — a leftover placeholder becomes the literal password and Postgres/n8n fail to connect.
  • chmod 600 .env. Record the encryption key so the user can back it up off-box.

5. Firewall

  • ufw: allow OpenSSH + 80 + 443, then enable. Do not open 5678/5432/6379.

6. Launch

  • cd <DATA_FOLDER> && docker compose up -d.
  • Queue mode brings up Redis + Postgres + main + workers (workers via replicas). To add capacity: docker compose up -d --scale n8n-worker=N.

7. Verify (don't declare success without this)

  • docker compose ps — every service Up/healthy (queue: postgres & redis healthy first).
  • n8n itself up (internal): docker compose exec n8n wget -qO- http://localhost:5678/healthz{"status":"ok"}. This separates "n8n is running" from "TLS isn't ready yet."
  • Cert issued: docker compose logs caddy | grep -i 'certificate obtained'. First-boot ACME can take a minute or two; until it finishes, a public https:// request fails TLS — that means the cert is still pending, not that n8n is down.
  • Public reachability (with retry): curl -fsS --retry 5 --retry-delay 10 https://<fqdn>/healthz{"status":"ok"}. (/healthz only proves the process is reachable; /healthz/readiness additionally confirms the DB is connected and migrated — use it when debugging a boot loop.)
  • Queue mode — main and workers must agree. Diff their environments: diff <(docker compose exec -T n8n env | sort) <(docker compose exec -T --index 1 n8n-worker env | sort). Only the public-URL/proxy vars should differ. Anything else means a behavioural setting reached the main but not the workers — and workers are what execute workflows, so it fails at runtime in one node rather than at boot. QUEUE_MODE.md explains the rule.
  • External task runners: docker compose logs n8n | grep 'Registered runner' must show both launcher-javascript and launcher-python (queue: check every worker). Then smoke-test a Python Code node (TASK_RUNNERS.md → Verify).
  • Open https://<fqdn> → the owner setup screen. Whoever completes that signup form first claims the instance — an exposed un-owned instance is a race, so create the owner account immediately, before sharing the URL. Enable 2FA. (Automated deploys can pre-provision the owner via env vars instead — see the owner row in SECURITY.md.)

8. Hand off

  • Give the user: the URL, where the project lives, the encryption key to store safely, and the Day-2 basics (update / backup / restore) from DAY2.md.

What NOT to do

  • Don't skip the DNS/ports preflight. A wrong A record or a closed cloud firewall is the #1 reason Caddy can't get a cert and n8n looks "broken."
  • Don't publish 5678/5432/6379 to the host. Caddy reaches n8n over the private network.
  • Don't reuse another instance's encryption key or .env. Fresh secrets per box.
  • Don't run queue mode on SQLite. Queue requires Postgres (the template already wires it).
  • Don't put secrets in docker-compose.yml or the Caddyfile. .env only.
  • Don't add a behavioural env var to the main only (queue mode). Modules, DB, queue, binary-data and encryption settings belong in the shared x-n8n-env anchor so workers get them too; only the public-URL/proxy vars are main-only. See QUEUE_MODE.md.
  • Don't use :latest blindly. Pin N8N_IMAGE_TAG; update deliberately (DAY2.md).
  • Don't promise Python Code nodes on the stock image in internal mode. They need the n8nio/runners sidecar, on exactly the n8n version, with imports allowlisted in the launcher config. By default even import json is rejected. See TASK_RUNNERS.md.
  • Don't --scale queue workers once runners are external. A sidecar serves exactly one worker. Add worker + runner pairs instead (TASK_RUNNERS.md).

Reference files

  • SINGLE_MODE.md — single-instance specifics, SQLite vs Postgres, when to graduate to queue.
  • QUEUE_MODE.md — queue architecture, workers/concurrency/scaling, shared encryption key, the main-vs-worker env-parity rule, optional backend modules (N8N_ENABLED_MODULES, e.g. Agents), binary data (database mode — filesystem is unsupported in queue mode; S3/Azure = Enterprise), webhook processors, multi-main licensing.
  • SECURITY.md — generating secrets, the encryption-key rules, the full hardening checklist (telemetry off, env-access block, public API, firewall, secure cookies).
  • CREDENTIAL_OVERWRITES.md — managed OAuth: register one OAuth app instance-wide so users never see a client ID/secret ("Sign in with Google" on self-hosted). The endpoint-vs-env choice, the mandatory endpoint auth token, parent-type inheritance, persistence and worker reload.
  • TASK_RUNNERS.md — task runners in external mode: why Python Code nodes need the n8nio/runners sidecar, the compose snippet for single mode and the one-sidecar-per-worker pattern for queue mode, allowlisting Python/JS modules via /etc/n8n-task-runners.json, the builtin deny list, verification, failure signatures, and upgrading n8n and the runners together.
  • DAY2.md — changing a setting (env var) safely, updating the image, backing up (encryption key + volume + Postgres), and restoring.
  • assets/ — the templates: docker-compose.single.yml, docker-compose.queue.yml, Caddyfile, .env.single.example, .env.queue.example, init-data.sh.

Authoritative upstream reference: the official hosting docs live at https://docs.n8n.io/deploy/host-n8n (restructured mid-2026 from the old /hosting/ paths — prefer these URLs). The env-var reference index is at https://docs.n8n.io/deploy/host-n8n/configure-n8n/basic-configuration/use-environment-variables. When this skill and the live docs disagree, trust the docs and tell the user.

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 N8n Self Hosting AI skill do?

Deploy a production self-hosted n8n end-to-end to a fresh Linux VM over SSH, using Docker Compose behind a Caddy reverse proxy with automatic HTTPS. Use whenever the user wants to self-host, install, provision, or deploy n8n on their own server/VPS (Hetzner, DigitalOcean, AWS EC2, bare metal) — single/regular mode or queue mode with workers — or to update, back up, restore, or harden such an instance, or make Python Code nodes run on it (task runners). For SELF-HOSTED n8n (Docker), not n8n Cloud and not building workflows. The skill makes the agent ask single-vs-queue first, collect domain/...

Why use N8n Self Hosting on TypingMind?

Because you install it once and use it with any model. N8n Self Hosting 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 N8n Self Hosting in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/czlonkowski/n8n-skills/tree/main/skills/n8n-self-hosting. 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 N8n Self Hosting?

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 N8n Self Hosting?

As many as you like. As long as a model supports skills, you can use N8n Self Hosting with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the N8n Self Hosting AI skill free?

Yes. It is published on GitHub by czlonkowski 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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