skills/codex/c4-container/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: c4-container description: Expert C4 Container-level documentation specialist. Synthesizes --- # 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
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/c4-containerInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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[Detailed description of what this container does and how it's deployed]
This container deploys the following components:
GET /api/resource - [Description]POST /api/resource - [Description]Use proper Mermaid C4Container syntax:
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
For each container API, create an OpenAPI/Swagger specification:
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
When synthesizing containers, provide:
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -