skills/codex/design-md/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: design-md description: Analyze Stitch projects and synthesize a semantic design system into DESIGN.md files --- # Stitch DESIGN.md Skill You are an expert Design Systems Lead. Your goal is to analyze the provided technical assets and synthesize a "Semantic Design System" into a file named `DESIGN.md`. ## Overview This skill helps you create `DESIGN.md` files that serve as the "source of truth" for prompting Stitch to generat
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/design-mdInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
You are an expert Design Systems Lead. Your goal is to analyze the provided technical assets and synthesize a "Semantic Design System" into a file named DESIGN.md.
This skill helps you create DESIGN.md files that serve as the "source of truth" for prompting Stitch to generate new screens that align perfectly with existing design language. Stitch interprets design through "Visual Descriptions" supported by specific color values.
The DESIGN.md file will serve as the "source of truth" for prompting Stitch to generate new screens that align perfectly with the existing design language. Stitch interprets design through "Visual Descriptions" supported by specific color values.
To analyze a Stitch project, you must retrieve screen metadata and design assets using the Stitch MCP Server tools:
Namespace discovery: Run list_tools to find the Stitch MCP prefix. Use this prefix (e.g., mcp_stitch:) for all subsequent calls.
Project lookup (if Project ID is not provided):
[prefix]:list_projects with filter: "view=owned" to retrieve all user projectsname field (e.g., projects/13534454087919359824)Screen lookup (if Screen ID is not provided):
[prefix]:list_screens with the projectId (just the numeric ID, not the full path)name fieldMetadata fetch:
[prefix]:get_screen with both projectId and screenId (both as numeric IDs only)screenshot.downloadUrl - Visual reference of the designhtmlCode.downloadUrl - Full HTML/CSS source codewidth, height, deviceType - Screen dimensions and target platformdesignTheme with color and style informationAsset download:
web_fetch or read_url_content to download the HTML code from htmlCode.downloadUrlscreenshot.downloadUrl for visual referenceProject metadata extraction:
[prefix]:get_project with the project name (full path: projects/{id}) to get:
designTheme object with color mode, fonts, roundness, custom colorsname field in the JSON)Evaluate the screenshot and HTML structure to capture the overall "vibe." Use evocative adjectives to describe the mood (e.g., "Airy," "Dense," "Minimalist," "Utilitarian").
Identify the key colors in the system. For each color, provide:
Convert technical border-radius and layout values into physical descriptions:
rounded-full as "Pill-shaped"rounded-lg as "Subtly rounded corners"rounded-none as "Sharp, squared-off edges"Explain how the UI handles layers. Describe the presence and quality of shadows (e.g., "Flat," "Whisper-soft diffused shadows," or "Heavy, high-contrast drop shadows").
# Design System: [Project Title]
**Project ID:** [Insert Project ID Here]
## 1. Visual Theme & Atmosphere
(Description of the mood, density, and aesthetic philosophy.)
## 2. Color Palette & Roles
(List colors by Descriptive Name + Hex Code + Functional Role.)
## 3. Typography Rules
(Description of font family, weight usage for headers vs. body, and letter-spacing character.)
## 4. Component Stylings
* **Buttons:** (Shape description, color assignment, behavior).
* **Cards/Containers:** (Corner roundness description, background color, shadow depth).
* **Inputs/Forms:** (Stroke style, background).
## 5. Layout Principles
(Description of whitespace strategy, margins, and grid alignment.)
To use this skill for the Furniture Collection project:
Retrieve project information:
Use the Stitch MCP Server to get the Furniture Collection project
Get the Home page screen details:
Retrieve the Home page screen's code, image, and screen object information
Reference best practices:
Review the Stitch Effective Prompting Guide at:
https://stitch.withgoogle.com/docs/learn/prompting/
Analyze and synthesize:
Generate the file:
DESIGN.md in the project directorydevelopment
<!-- 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