C4 Container logo

C4 Container

CommunityPopular
rmyndharis
c4-container

Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container diagrams. Use when synthesizing components into deployment containers and documenting system deployment architecture.

Overview

Publisherrmyndharis
Repositoryantigravity-skills
Skill namec4-container
Stars
1.6K
Forks
264
Bundled files
Instructions only
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.

  • Self-contained

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

  • Open source

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

Installation

Install the C4 Container 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/rmyndharis/antigravity-skills.git /tmp/antigravity-skills
mkdir -p .claude/skills
cp -r /tmp/antigravity-skills/skills/c4-container .claude/skills/c4-container
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable C4 Container 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 C4 Container 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 C4 Container 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.

C4 Container Level: System Deployment

Use this skill when

  • Working on c4 container level: system deployment tasks or workflows
  • Needing guidance, best practices, or checklists for c4 container level: system deployment

Do not use this skill when

  • The task is unrelated to c4 container level: system deployment
  • You need a different domain or tool outside this scope

Instructions

  • Clarify goals, constraints, and required inputs.
  • Apply relevant best practices and validate outcomes.
  • Provide actionable steps and verification.

Containers

[Container Name]

  • Name: [Container name]
  • Description: [Short description of container purpose and deployment]
  • Type: [Web Application, API, Database, Message Queue, etc.]
  • Technology: [Primary technologies: Node.js, Python, PostgreSQL, Redis, etc.]
  • Deployment: [Docker, Kubernetes, Cloud Service, etc.]

Purpose

[Detailed description of what this container does and how it's deployed]

Components

This container deploys the following components:

Interfaces

[API/Interface Name]

  • Protocol: [REST/GraphQL/gRPC/Events/etc.]
  • Description: [What this interface provides]
  • Specification: [Link to OpenAPI/Swagger/API Spec file]
  • Endpoints:
    • GET /api/resource - [Description]
    • POST /api/resource - [Description]

Dependencies

Containers Used

  • [Container Name]: [How it's used, communication protocol]

External Systems

  • [External System]: [How it's used, integration type]

Infrastructure

  • Deployment Config: [Link to Dockerfile, K8s manifest, etc.]
  • Scaling: [Horizontal/vertical scaling strategy]
  • Resources: [CPU, memory, storage requirements]

Container Diagram

Use proper Mermaid C4Container syntax:

mermaid
C4Container
    title Container Diagram for [System Name]

    Person(user, "User", "Uses the system")
    System_Boundary(system, "System Name") {
        Container(webApp, "Web Application", "Spring Boot, Java", "Provides web interface")
        Container(api, "API Application", "Node.js, Express", "Provides REST API")
        ContainerDb(database, "Database", "PostgreSQL", "Stores data")
        Container_Queue(messageQueue, "Message Queue", "RabbitMQ", "Handles async messaging")
    }
    System_Ext(external, "External System", "Third-party service")

    Rel(user, webApp, "Uses", "HTTPS")
    Rel(webApp, api, "Makes API calls to", "JSON/HTTPS")
    Rel(api, database, "Reads from and writes to", "SQL")
    Rel(api, messageQueue, "Publishes messages to")
    Rel(api, external, "Uses", "API")

**Key Principles** (from [c4model.com](https://c4model.com/diagrams/container)):

- Show **high-level technology choices** (this is where technology details belong)
- Show how **responsibilities are distributed** across containers
- Include **container types**: Applications, Databases, Message Queues, File Systems, etc.
- Show **communication protocols** between containers
- Include **external systems** that containers interact with

API Specification Template

For each container API, create an OpenAPI/Swagger specification:

yaml
openapi: 3.1.0
info:
  title: [Container Name] API
  description: [API description]
  version: 1.0.0
servers:
  - url: https://api.example.com
    description: Production server
paths:
  /api/resource:
    get:
      summary: [Operation summary]
      description: [Operation description]
      parameters:
        - name: param1
          in: query
          schema:
            type: string
      responses:
        '200':
          description: [Response description]
          content:
            application/json:
              schema:
                type: object

Example Interactions

  • "Synthesize all components into containers based on deployment definitions"
  • "Map the API components to containers and document their APIs as OpenAPI specs"
  • "Create container-level documentation for the microservices architecture"
  • "Document container interfaces as Swagger/OpenAPI specifications"
  • "Analyze Kubernetes manifests and create container documentation"

Key Distinctions

  • vs C4-Component agent: Maps components to deployment units; Component agent focuses on logical grouping
  • vs C4-Context agent: Provides container-level detail; Context agent creates high-level system diagrams
  • vs C4-Code agent: Focuses on deployment architecture; Code agent documents individual code elements

Output Examples

When synthesizing containers, provide:

  • Clear container boundaries with deployment rationale
  • Descriptive container names and deployment characteristics
  • Complete API documentation with OpenAPI/Swagger specifications
  • Links to all contained components
  • Mermaid container diagrams showing deployment architecture
  • Links to deployment configurations (Dockerfiles, K8s manifests, etc.)
  • Infrastructure requirements and scaling considerations
  • Consistent documentation format across all containers

Frequently asked questions

What does the C4 Container AI skill do?

Expert C4 Container-level documentation specialist. Synthesizes Component-level documentation into Container-level architecture, mapping components to deployment units, documenting container interfaces as APIs, and creating container diagrams. Use when synthesizing components into deployment containers and documenting system deployment architecture.

Why use C4 Container on TypingMind?

Because you install it once and use it with any model. C4 Container 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 C4 Container in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rmyndharis/antigravity-skills/tree/main/skills/c4-container. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use C4 Container?

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 C4 Container?

As many as you like. As long as a model supports skills, you can use C4 Container with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the C4 Container AI skill free?

Yes. It is published on GitHub by rmyndharis 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.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇