Render Docker logo

Render Docker

Organization
render-oss
render-docker

Builds and deploys Docker containers on Render—Dockerfiles, multi-stage builds, Blueprint Docker fields, private registries, layer caching, and platform constraints. Use when the user mentions Docker, Dockerfile, container images, multi-stage builds, container registry, GHCR, ECR, BuildKit, dockerContext, runtime docker or image, or optimizing Docker builds on Render.

Overview

Publisherrender-oss
Repositoryskills
Skill namerender-docker
Stars
90
Forks
21
Bundled files
4
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.

  • 4 bundled files

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

  • Open source

    Published by render-oss on GitHub. Read the source before you install it.

Installation

Install the Render Docker 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/render-oss/skills/tree/main/skills/render-docker
  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/render-oss/skills.git /tmp/skills
mkdir -p .claude/skills
cp -r /tmp/skills/skills/render-docker .claude/skills/render-docker
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Render Docker 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 Render Docker 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 Render Docker 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.

Render Docker Deployments

Render uses BuildKit for Docker builds. All compute service types that support custom runtimes can use runtime: docker (build from a Dockerfile in the repo) or runtime: image (pull a prebuilt image; no Dockerfile build on Render). Deeper patterns and copy-paste templates live under references/.

When to Use

  • Authoring or debugging a Dockerfile for a Render service
  • Choosing runtime: docker vs runtime: image in a Blueprint
  • Wiring private base images or prebuilt images with registry credentials
  • Multi-stage builds, build args, secrets, and layer caching
  • Performance and security hardening of container images on Render

For full Blueprint authoring, see render-blueprints. For end-to-end deploy flows, see render-deploy.

Before deploying a built or prebuilt image, read references/deployments.md for current trigger, lifecycle, health-check, and signal behavior.

Render Docker Builds

  • BuildKit is used for Docker builds on Render.
  • runtime: docker: Render builds an image from your repo using dockerfilePath, dockerContext, and optional dockerCommand (overrides image CMD).
  • runtime: image: Render pulls image.url; no repo-based image build. Set image.creds.fromRegistryCreds.name when the registry is private.

Blueprint Configuration

FieldRole
dockerfilePathPath to the Dockerfile (default ./Dockerfile)
dockerContextBuild context directory (what is sent to the daemon)
dockerCommandOverrides the container CMD after the image is built
image.urlImage reference for runtime: image (registry/repo:tag or digest)
image.credsDashboard-stored credential reference for a private prebuilt image
registryCredentialDashboard-stored credential reference for private base images used by runtime: docker

Example sketch (values illustrative):

yaml
services:
  - type: web
    name: api
    runtime: docker
    region: oregon
    plan: starter
    dockerfilePath: ./Dockerfile
    dockerContext: .
    dockerCommand: node server.js
    envVars:
      - key: PORT
        value: 10000

For runtime: image, set image.url and, if needed, image.creds per Registry Configuration below.

Multi-Stage Builds

Recommended for production. Use a builder stage for compilation and dependency installation, and a minimal runner stage that only copies artifacts and runtime files. Benefits:

  • Smaller images and faster pulls
  • Fewer tools and secrets in the final image (smaller attack surface)
  • Clear separation between build-time and run-time dependencies

See references/dockerfile-patterns.md for language-specific templates.

Build Args vs Secrets

Critical: Never pass secrets via ARG. Build arguments are stored in image layers and can be recovered from the image history or intermediate layers.

  • Prefer runtime environment variables (Render env vars / secret files) for application secrets.
  • For build-time secrets (e.g. private package feeds), use Docker BuildKit secret mounts (RUN --mount=type=secret,...) rather than ARG.

Treat anything sensitive as runtime or BuildKit secret mount, not as a build arg.

Registry Configuration

Private base images (for runtime: docker) or prebuilt images (runtime: image) need authentication:

  • Store credentials in the Render Dashboard under Registry Credentials.
  • For runtime: docker, reference private base-image credentials with registryCredential.fromRegistryCreds.name.
  • For runtime: image, reference private prebuilt-image credentials with image.creds.fromRegistryCreds.name.

Supports common registries (Docker Hub, GHCR, ECR, Google Artifact Registry, and others). Step-by-step per provider: references/registry-setup.md.

Prebuilt-image services do not automatically deploy when a mutable registry tag moves. After publishing a new image, trigger a manual deploy through the Dashboard or a supported API or deploy-hook flow. Prefer immutable tags or digests so each deploy identifies an explicit artifact; follow references/deployments.md for the complete trigger and verification workflow.

Layer Caching

  • Render caches Docker layers between builds; order Dockerfile instructions so that frequently unchanged layers stay early (see references/optimization-guide.md).
  • Tags and caching: mutable tags like latest can resolve to stale cached images. Prefer immutable references: digest (repo/image@sha256:...) or version pins (v1.2.3).

Platform Specifics

  • Render builds linux/amd64. Avoid assumptions about other architectures in production images.
  • Port binding matches native services: bind HTTP to 0.0.0.0:$PORT (Render sets PORT).
  • Health checks use the same platform behavior described in references/deployments.md.
  • Secret files from Render appear under /etc/secrets/ — do not rely on repo-root secret paths inside the container unless you copy or mount them explicitly in the image.

.dockerignore and Start Commands

  • Always maintain a .dockerignore that excludes node_modules, .git, .env, build artifacts, logs, and OS junk. This shrinks context upload time and avoids leaking local files into layers. Lists and rationale: references/optimization-guide.md.
  • Custom start command: if you need multiple shell steps, use a single shell form, e.g. /bin/sh -c 'set -e; ./migrate && exec node server.js' (prefer exec so your app receives signals for graceful shutdown).

References

DocumentContents
references/dockerfile-patterns.mdMulti-stage templates (Node, Python, Go, Ruby, Rust, static sites)
references/registry-setup.mdDocker Hub, GHCR, ECR, Artifact Registry + Blueprint wiring
references/optimization-guide.mdLayer order, .dockerignore, BuildKit cache mounts, debugging
references/deployments.mdDeploy triggers, lifecycle, health checks, shutdown, and verification

Related Skills

  • render-deploy — Deploy flows, Blueprint vs Dashboard, operational steps
  • render-blueprints — Full render.yaml schema, wiring, and validation
  • render-web-services — Web service behavior, health checks, and HTTP edge cases

Current documentation retrieval

Whenever this skill directs you to consult current Render documentation:

  1. Retrieve the linked Markdown document directly with an available URL-fetching tool or HTTP client, such as curl. Do not substitute web-search summaries for the document.
  2. Confirm that retrieval succeeded and returned the expected document, then read its contents. Saving a file or printing its path is not sufficient.
  3. If the request fails or your tool cannot read the Markdown response, open and read the linked HTML version instead.
  4. If neither version can be retrieved, disclose that the current reference is unavailable and follow any topic-specific fallback in the skill. Use bundled guidance only for stable constraints, and do not guess at changeable platform details.

When a task requires multiple references, apply this workflow to each one and distinguish the documents you verified from those that remain unavailable.

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 Render Docker AI skill do?

Builds and deploys Docker containers on Render—Dockerfiles, multi-stage builds, Blueprint Docker fields, private registries, layer caching, and platform constraints. Use when the user mentions Docker, Dockerfile, container images, multi-stage builds, container registry, GHCR, ECR, BuildKit, dockerContext, runtime docker or image, or optimizing Docker builds on Render.

Why use Render Docker on TypingMind?

Because you install it once and use it with any model. Render Docker 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 Render Docker in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/render-oss/skills/tree/main/skills/render-docker. 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 Render Docker?

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 Render Docker?

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

Is the Render Docker AI skill free?

Yes. It is published on GitHub by render-oss 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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