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Dokploy Internal Oneshot

Community
Innei
dokploy-internal-oneshot

Use when running an ephemeral one-shot task (data migration, batch job, schema setup, ad-hoc psql script) that must reach other services in a Dokploy project's internal network without exposing those services to the public internet. The task should join the project's docker overlay network, run to completion, and exit. Pairs with dokploy-api-cli.

Overview

PublisherInnei
RepositorySKILL
Skill namedokploy-internal-oneshot
Stars
81
Forks
2
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Dokploy Internal Oneshot 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/Innei/SKILL.git /tmp/SKILL
mkdir -p .claude/skills
cp -r /tmp/SKILL/skills/infrastructure/dokploy-internal-oneshot .claude/skills/dokploy-internal-oneshot
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dokploy Internal Oneshot 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 Dokploy Internal Oneshot 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 Dokploy Internal Oneshot 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.

Dokploy One-Shot Internal Task

When you need to run something inside a Dokploy project's network — to reach postgres, mongo, redis, etc. by their internal hostnames without temporarily exposing host ports — create a short-lived compose service with sourceType: "raw" and an inline YAML.

When to use

  • Running a one-time data migration script that needs the project's database
  • Triggering a batch job (cleanup, recompute, reindex) inside the same network
  • Quick psql / redis-cli ad-hoc scripts when a log API isn't needed
  • Anything where exposing externalPort on a database is undesirable

When NOT to use

  • Long-running services (use a normal application or compose with persistent intent)
  • Tasks that need only the public-facing API (just curl it)
  • Tasks where you have SSH on the host and prefer docker run --network <net> directly

Compose template

yaml
services:
  runner:
    image: <base-image-with-the-runtime-you-need>   # e.g. node:22-bookworm, python:3.12-slim
    working_dir: /app
    restart: "no"          # one-shot — must not auto-restart
    environment:
      # Internal service hostnames are the auto-generated `appName` of each service.
      # Discover via `GET /api/<svc>.one`.
      DB_URL: "postgres://user:pwd@<pg-appName>:5432/dbname"
      MODE: "dry-run"
    command:
      - bash
      - -c
      - |
        set -e
        # apt-get -q install whatever you need
        # git clone --depth 1 -b master https://github.com/<owner>/<repo>.git /app
        # corepack enable && pnpm install --frozen-lockfile
        echo "==== task start (mode=$$MODE) ===="
        # actual work goes here, e.g.
        #   pnpm exec tsx scripts/migrate.ts --mode "$$MODE"
        echo "==== task end ===="
networks:
  default:
    name: dokploy-network    # Dokploy's default project overlay
    external: true

Critical: escape $ as $$

Docker Compose performs variable interpolation on the command: block before bash sees it. Any $VAR will be substituted from the compose process's env, not the container's, and ${ARRAY[idx]} produces the dreaded:

invalid interpolation format for services.<svc>.command.[].
You may need to escape any $ with another $.

Rule: every $ that bash should see must be written $$ in the YAML. Validate locally with docker compose -f compose.yml config before uploading.

YAML you writeWhat bash seesUse case
$$VAR$VARenv var
$$(cmd)$(cmd)command substitution
$${arr[0]}${arr[0]}array index, PIPESTATUS

Driving it

Use the dokploy-api-cli skill for compose.createcompose.update (with the YAML) → compose.deploy. Then poll the container, not the deployment status:

bash
curl -sS -H "x-api-key: $TOKEN" \
  "https://$HOST/api/docker.getContainersByAppNameMatch?appName=$APP_NAME" \
  | jq '.[0] | "\(.state)|\(.status)"'
# wait for state to flip from running → exited

When the container exits, the exit code is in the status text (Exited (0) / Exited (1) …).

Reading output

The compose YAML you uploaded is the source of truth for the run. There is no public log API on Dokploy. Three options to retrieve output:

  1. Sidechannel-write into a database the runner can reach. See the capture-output-via-sidechannel skill. This is usually the cleanest for migrations because you'll have a database open anyway.
  2. SSH to the swarm host and docker logs <container> (works for the agent if SSH is available; not for users without host access).
  3. Have the runner POST a summary to a public webhook (webhook.site, ntfy.sh, your own endpoint). Useful when neither DB nor SSH is available.

Cleanup

After the task finishes:

  1. compose.delete removes the service AND its containers (no leftover exited container).
  2. If you want to re-run with updated YAML in the same composeId, first ssh host docker rm -f <appName>-<service>-1 because Dokploy's internal docker compose up won't recreate an existing exited container by default.

Common Mistakes

MistakeFix
Writing $VAR in command:Use $$VAR. Validate with docker compose config.
Using ${PIPESTATUS[0]} directlyWrite $${PIPESTATUS[0]}.
Leaving exited container in place between runsdocker rm -f <name> (via SSH) or compose.delete + compose.create again.
Counting on restart: "no" to mean "no retry on Dokploy redeploy"Dokploy will still re-create the container on each compose.deploy. The flag only stops Docker itself from restarting after exit.
Hostname uses friendly nameUse the auto-generated appName (e.g. mx-space-pg-7pacdz); friendly name doesn't resolve.

Frequently asked questions

What does the Dokploy Internal Oneshot AI skill do?

Use when running an ephemeral one-shot task (data migration, batch job, schema setup, ad-hoc psql script) that must reach other services in a Dokploy project's internal network without exposing those services to the public internet. The task should join the project's docker overlay network, run to completion, and exit. Pairs with dokploy-api-cli.

Why use Dokploy Internal Oneshot on TypingMind?

Because you install it once and use it with any model. Dokploy Internal Oneshot 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 Dokploy Internal Oneshot in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/Innei/SKILL/tree/main/skills/infrastructure/dokploy-internal-oneshot. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Dokploy Internal Oneshot?

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 Dokploy Internal Oneshot?

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

Is the Dokploy Internal Oneshot AI skill free?

It is published on GitHub by Innei. Check the repository for licensing terms. 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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