AI-Driven Developer Efficiency

About the Project

Developed an AI-driven workflow system using Cursor Rules and MCP (Model Context Protocol) integrations to significantly improve developer efficiency. Collaborated with designers to establish design token guidelines in Figma, which were then codified into Cursor Rules for automated Figma-to-code conversion achieving 80% design match accuracy. Integrated Jira MCP for automated task and subtask creation, and Figma MCP for design component generation. Created comprehensive tech documentation for features to enable LLM understanding of business use cases, and implemented PRD parsing via code to identify gaps proactively before development begins.

Project Overview

Developed an AI-driven workflow system leveraging Cursor Rules and MCP (Model Context Protocol) integrations to significantly improve developer efficiency. The system automates design-to-code conversion, task management, and requirement analysis, reducing manual effort and accelerating development cycles.

Cursor Rules & Design Token Guidelines

Collaborated with designers to establish design token guidelines in Figma files. These guidelines were then codified intoCursor Rules (`.cursorrules`), enabling AI to understand design patterns and generate code that matches design specifications:

# Cursor Rules for Design Token Mapping

## Design Token Guidelines
- Use design tokens from Figma variables
- Map Figma tokens to CSS variables: --color-primary, --spacing-md
- Follow typography scale: title-1 through title-6
- Maintain spacing consistency: 4px base unit

## Component Generation Rules
- Extract colors from Figma design tokens
- Use Tailwind utilities mapped to design tokens
- Generate React components matching Figma structure
- Preserve spacing and typography from design specs

## Figma Token Mapping
- Primary colors → --color-primary → bg-primary
- Spacing tokens → --spacing-* → p-*, m-*, gap-*
- Typography → --font-* → text-* utilities

Figma MCP Integration: Design-to-Code

Integrated Figma MCP to extract design specifications directly from Figma files. The system reads design tokens, component structures, and styling information, then uses Cursor Rules to generate React components with80% design match accuracy:

// Figma MCP Integration Example
import { FigmaMCP } from '@mcp/figma';

// Extract design from Figma
const designSpec = await FigmaMCP.getComponent({
  fileId: 'figma-file-id',
  nodeId: 'component-node-id'
});

// Design tokens extracted
const tokens = {
  colors: designSpec.colors,
  spacing: designSpec.spacing,
  typography: designSpec.typography
};

// Cursor Rules process tokens
// Generates React component matching design
const component = generateComponent(tokens);
// Result: 80% match, developer refines to 100%

MCP Integrations: Jira & Figma

Leveraged MCP (Model Context Protocol) to integrate with Jira and Figma, enabling automated task creation and design extraction:

Jira MCP: Automated Task Management

Jira MCP integration enables automatic creation of tasks and subtasks based on feature requirements. The LLM analyzes tech documentation to understand business context and generates structured Jira tickets:

// Jira MCP Integration
import { JiraMCP } from '@mcp/jira';

// Create tasks from feature requirements
const tasks = await JiraMCP.createTasks({
  project: 'PROJECT-KEY',
  summary: 'Feature: User Authentication',
  description: techDocs.userAuth,
  subtasks: [
    'Implement login API',
    'Create login UI component',
    'Add authentication middleware'
  ]
});

// Automatically creates parent task + subtasks in Jira

Figma MCP: Design Component Extraction

Figma MCP extracts design components, tokens, and specifications directly from Figma files, enabling automated code generation:

// Figma MCP: Extract Design Components
const figmaComponent = await FigmaMCP.getNode({
  fileKey: 'design-file-key',
  nodeId: 'button-component-id'
});

// Returns design specifications
{
  name: 'Primary Button',
  tokens: {
    backgroundColor: '--color-primary',
    padding: '--spacing-md',
    borderRadius: '--radius-sm'
  },
  structure: { /* component hierarchy */ }
}

Tech Documentation & PRD Gap Analysis

Created comprehensive tech documentation for features to enable LLM understanding of business use cases. Implemented PRD parsing via code to identify gaps and missing requirements before development begins:

Tech Documentation Structure

Tech docs provide business context and use cases that help LLMs understand feature requirements:

// Tech Documentation Format
{
  feature: "User Authentication",
  businessContext: {
    useCase: "Allow users to securely log in",
    userFlow: "Login → Dashboard",
    requirements: ["Email/Password", "JWT tokens"]
  },
  technicalSpecs: {
    api: "/api/auth/login",
    components: ["LoginForm", "AuthGuard"],
    dependencies: ["JWT", "bcrypt"]
  }
}

PRD Parser: Gap Analysis

PRD parser analyzes product requirements documents and compares them against existing tech documentation to identify gaps proactively:

// PRD Gap Analysis
import { parsePRD, compareWithTechDocs } from './prd-parser';

const prdRequirements = parsePRD(prdDocument);
const existingFeatures = loadTechDocs();

const gaps = compareWithTechDocs({
  prd: prdRequirements,
  existing: existingFeatures
});

// Identifies missing requirements
{
  missingFeatures: [
    'Email verification flow',
    'Password reset functionality'
  ],
  incompleteSpecs: [
    'API endpoint for user profile'
  ]
}

// Reports gaps before development starts

Efficiency Improvements

✓80% design match accuracy in automated Figma-to-code conversion, reducing manual design implementation time
✓Automated task creation via Jira MCP eliminates manual ticket creation and ensures comprehensive task breakdown
✓PRD gap analysis identifies missing requirements before development, preventing rework and scope creep
✓Design token guidelines ensure consistency between design and code, reducing design-system drift
✓Tech documentation enables LLM understanding of business context, improving code generation accuracy

Project Details