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: grpo-rl-training description: GRPO reinforcement learning training with TRL. Use when applying Group Relative Policy Optimization for reasoning and task-specific model training. --- # GRPO/RL Training with TRL Expert-level guidance for implementing Group Relative Policy Optimization (GRPO) using the Transformer Reinforcement Learning (TRL) library. This skill provides battle-tested patterns, critical insights, and production-r
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: graphql-architect description: Master modern GraphQL with federation, performance optimization, --- ## Use this skill when - Working on graphql architect tasks or workflows - Needing guidance, best practices, or checklists for graphql architect ## Do not use this skill when - The task is unrelated to graphql architect - You need a different domain or tool outside this scope ## Instructions - Clarify goals, constraints, and
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: grafana-dashboards description: Create and manage production Grafana dashboards for real-time visualization of system and application metrics. Use when building monitoring dashboards, visualizing metrics, or creating operational observability interfaces. --- # Grafana Dashboards Create and manage production-ready Grafana dashboards for comprehensive system observability. ## Do not use this skill when - The task is unrelated
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: gptq description: GPTQ post-training quantization for generative models. Use when quantizing large models to 4-bit with calibration-based weight compression. --- # GPTQ (Generative Pre-trained Transformer Quantization) Post-training quantization method that compresses LLMs to 4-bit with minimal accuracy loss using group-wise quantization. ## When to use GPTQ **Use GPTQ when:** - Need to fit large models (70B+) on limited GPU