c4-container
Expert C4 Container-level documentation specialist.
Best use case
c4-container is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
Expert C4 Container-level documentation specialist.
Teams using c4-container should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/c4-container/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How c4-container Compares
| Feature / Agent | c4-container | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
Expert C4 Container-level documentation specialist.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
SKILL.md Source
# 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.
- If detailed examples are required, open `resources/implementation-playbook.md`.
## 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:
- [Component Name]: [Description]
- Documentation: c4-component-name.md
## 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
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