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Dokploy Api Cli

Community
Innei
dokploy-api-cli

Use when operating a Dokploy-managed deployment via REST API from the shell — creating/updating/deploying postgres/redis/compose/application services, switching an application's source between docker registry and git build, or scripting redeploys. Covers auth, the working endpoint catalog, and a critical gotcha about deployment status semantics.

Overview

PublisherInnei
RepositorySKILL
Skill namedokploy-api-cli
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 Api Cli 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-api-cli .claude/skills/dokploy-api-cli
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Dokploy Api Cli 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 Api Cli 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 Api Cli 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 API from the CLI

Operate Dokploy entirely from curl when the dashboard isn't practical (scripting, agents, headless ops).

Auth

Generate an API token in dashboard → Profile → API. Pass on every request:

-H "x-api-key: $TOKEN"

Other auth headers (Authorization: Bearer …) return 401 Unauthorized. There is no public OpenAPI/swagger endpoint — discover routes by probing.

Endpoint Catalog (verified)

All endpoints are under https://<dokploy-host>/api/<router>.<procedure>. Reads are GET with query params, writes are POST with JSON body.

Discovery

EndpointNotes
GET /api/project.allList projects
GET /api/project.one?projectId=<id>Project + nested environments[].{applications, mongo, postgres, redis, compose}
GET /api/postgres.one?postgresId=<id>Includes appName (internal hostname), databasePassword, externalPort
GET /api/application.one?applicationId=<id>Includes env (multiline string), sourceType, dockerImage, customGitUrl
GET /api/compose.one?composeId=<id>Includes composeFile, sourceType
GET /api/deployment.all?applicationId=<id>Deploy history (most recent first); composeId works too
GET /api/docker.getContainersByAppNameMatch?appName=<appName>Live container state — use to check actual run status

Postgres

EndpointRequired body
POST /api/postgres.create{name, appName, databaseName, databaseUser, databasePassword, dockerImage, environmentId}
POST /api/postgres.update{postgresId, ...fields-to-change} (e.g. externalPort: 5433 or null)
POST /api/postgres.deploy{postgresId} — required after update to apply port/image changes

Compose (one-shot or long-running stacks)

EndpointRequired body
POST /api/compose.create{name, appName, environmentId, composeType: "docker-compose"}
POST /api/compose.update{composeId, name, description, env, composeFile, sourceType: "raw", composeType, autoDeploy, command} — full replace, all fields required
POST /api/compose.deploy{composeId}
POST /api/compose.stop{composeId}
POST /api/compose.delete{composeId} — also removes containers

Application (long-running services)

EndpointRequired body
POST /api/application.saveDockerProvider{applicationId, dockerImage, username, password, registryUrl} (use empty strings for public images)
POST /api/application.saveGitProvider{applicationId, customGitUrl, customGitBranch, customGitBuildPath, watchPaths, customGitSSHKeyId}
POST /api/application.saveBuildType{applicationId, buildType: "dockerfile"|"nixpacks"|"heroku"|…, dockerfile, dockerContextPath, dockerBuildStage, herokuVersion, railpackVersion}
POST /api/application.saveEnvironment{applicationId, env, buildArgs, buildSecrets, createEnvFile} (env is the entire multiline KEY=VALUE string)
POST /api/application.deploy{applicationId}

Critical Gotcha: deploy status ≠ container status

deployment.all returns status: "done" as soon as Dokploy's outer compose-up exits successfully. The actual workload container may still be initializing, building, or even crashed afterwards.

For a true pass/fail signal, poll docker.getContainersByAppNameMatch and watch the container's state (runningexited) and status text.

bash
# Wrong: only watches deploy step
poll deployment.all → done = ❌ may still be building inside

# Right: watch the actual container
poll docker.getContainersByAppNameMatch → state=running → state=exited → check exit code

Switching application source: docker registry ↔ git build

Useful when the registry image is outdated (e.g., you need today's master before tagging a release):

  1. application.saveGitProvider — point at the repo + branch
  2. application.saveBuildType — set buildType: "dockerfile", dockerfile: "<path>" (e.g. dockerfile)
  3. application.deploy — Dokploy clones, builds, swaps service

To switch back after the image is published:

  1. application.saveDockerProvider — set dockerImage: "<repo>:<tag>", registryUrl: "index.docker.io"
  2. application.deploy — pulls and swaps

sourceType and related fields update automatically based on which save*Provider you called last.

Probing for unknown endpoint shapes

POST with empty body to discover the required fields via the Zod error response:

bash
curl -sS -X POST -H "x-api-key: $TOKEN" -H "content-type: application/json" \
  "https://<host>/api/postgres.create" -d '{}'
# → {"zodError":{"fieldErrors":{"name":[...],"databaseName":[...],...}}}

This is faster than guessing and works for every mutation route.

Common Mistakes

MistakeFix
Trusting deployment.all status aloneCross-check docker.getContainersByAppNameMatch
Calling compose.deploy with stale exited containers from a prior runssh host docker rm -f <appName>-<service>-1 first, or compose.stop then compose.deploy
Hostname mismatch when service names contain hyphensUse the auto-generated appName (e.g. mx-space-pg-7pacdz), not the user-friendly name
Updating compose YAML and expecting current container to reflect itYAML applies on next deploy; running container keeps the YAML it started with
Updating externalPort without redeployMust call postgres.deploy (or equivalent) to apply port mapping

Frequently asked questions

What does the Dokploy Api Cli AI skill do?

Use when operating a Dokploy-managed deployment via REST API from the shell — creating/updating/deploying postgres/redis/compose/application services, switching an application's source between docker registry and git build, or scripting redeploys. Covers auth, the working endpoint catalog, and a critical gotcha about deployment status semantics.

Why use Dokploy Api Cli on TypingMind?

Because you install it once and use it with any model. Dokploy Api Cli 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 Api Cli in TypingMind?

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

Which AI models can use Dokploy Api Cli?

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 Api Cli?

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

Is the Dokploy Api Cli 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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