figma-migration-deep-dive

Execute Figma major re-architecture and migration strategies with strangler fig pattern. Use when migrating to or from Figma, performing major version upgrades, or re-platforming existing integrations to Figma. Trigger with phrases like "migrate figma", "figma migration", "switch to figma", "figma replatform", "figma upgrade major".

claude-code
6 Tools
figma-pack Plugin
saas packs Category

Allowed Tools

ReadWriteEditBash(npm:*)Bash(node:*)Bash(kubectl:*)

Provided by Plugin

figma-pack

Claude Code skill pack for Figma (30 skills)

saas packs v1.0.0
View Plugin

Installation

This skill is included in the figma-pack plugin:

/plugin install figma-pack@claude-code-plugins-plus

Click to copy

Instructions

Figma Migration Deep Dive

Overview

Comprehensive guide for migrating to or from Figma, or major version upgrades.

Prerequisites

  • Current system documentation
  • Figma SDK installed
  • Feature flag infrastructure
  • Rollback strategy tested

Migration Types

Type Complexity Duration Risk
Fresh install Low Days Low
From competitor Medium Weeks Medium
Major version Medium Weeks Medium
Full replatform High Months High

Pre-Migration Assessment

Step 1: Current State Analysis


# Document current implementation
find . -name "*.ts" -o -name "*.py" | xargs grep -l "figma" > figma-files.txt

# Count integration points
wc -l figma-files.txt

# Identify dependencies
npm list | grep figma
pip freeze | grep figma

Step 2: Data Inventory


interface MigrationInventory {
  dataTypes: string[];
  recordCounts: Record<string, number>;
  dependencies: string[];
  integrationPoints: string[];
  customizations: string[];
}

async function assessFigmaMigration(): Promise<MigrationInventory> {
  return {
    dataTypes: await getDataTypes(),
    recordCounts: await getRecordCounts(),
    dependencies: await analyzeDependencies(),
    integrationPoints: await findIntegrationPoints(),
    customizations: await documentCustomizations(),
  };
}

Migration Strategy: Strangler Fig Pattern


Phase 1: Parallel Run
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   System    │ ──▶ │  Figma   │
│   (100%)    │     │   (0%)      │
└─────────────┘     └─────────────┘

Phase 2: Gradual Shift
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (50%)     │ ──▶ │   (50%)     │
└─────────────┘     └─────────────┘

Phase 3: Complete
┌─────────────┐     ┌─────────────┐
│   Old       │     │   New       │
│   (0%)      │ ──▶ │   (100%)    │
└─────────────┘     └─────────────┘

Implementation Plan

Phase 1: Setup (Week 1-2)


# Install Figma SDK
npm install @figma/sdk

# Configure credentials
cp .env.example .env.figma
# Edit with new credentials

# Verify connectivity
node -e "require('@figma/sdk').ping()"

Phase 2: Adapter Layer (Week 3-4)


// src/adapters/figma.ts
interface ServiceAdapter {
  create(data: CreateInput): Promise<Resource>;
  read(id: string): Promise<Resource>;
  update(id: string, data: UpdateInput): Promise<Resource>;
  delete(id: string): Promise<void>;
}

class FigmaAdapter implements ServiceAdapter {
  async create(data: CreateInput): Promise<Resource> {
    const figmaData = this.transform(data);
    return figmaClient.create(figmaData);
  }

  private transform(data: CreateInput): FigmaInput {
    // Map from old format to Figma format
  }
}

Phase 3: Data Migration (Week 5-6)


async function migrateFigmaData(): Promise<MigrationResult> {
  const batchSize = 100;
  let processed = 0;
  let errors: MigrationError[] = [];

  for await (const batch of oldSystem.iterateBatches(batchSize)) {
    try {
      const transformed = batch.map(transform);
      await figmaClient.batchCreate(transformed);
      processed += batch.length;
    } catch (error) {
      errors.push({ batch, error });
    }

    // Progress update
    console.log(`Migrated ${processed} records`);
  }

  return { processed, errors };
}

Phase 4: Traffic Shift (Week 7-8)


// Feature flag controlled traffic split
function getServiceAdapter(): ServiceAdapter {
  const figmaPercentage = getFeatureFlag('figma_migration_percentage');

  if (Math.random() * 100 < figmaPercentage) {
    return new FigmaAdapter();
  }

  return new LegacyAdapter();
}

Rollback Plan


# Immediate rollback
kubectl set env deployment/app FIGMA_ENABLED=false
kubectl rollout restart deployment/app

# Data rollback (if needed)
./scripts/restore-from-backup.sh --date YYYY-MM-DD

# Verify rollback
curl https://app.yourcompany.com/health | jq '.services.figma'

Post-Migration Validation


async function validateFigmaMigration(): Promise<ValidationReport> {
  const checks = [
    { name: 'Data count match', fn: checkDataCounts },
    { name: 'API functionality', fn: checkApiFunctionality },
    { name: 'Performance baseline', fn: checkPerformance },
    { name: 'Error rates', fn: checkErrorRates },
  ];

  const results = await Promise.all(
    checks.map(async c => ({ name: c.name, result: await c.fn() }))
  );

  return { checks: results, passed: results.every(r => r.result.success) };
}

Instructions

Step 1: Assess Current State

Document existing implementation and data inventory.

Step 2: Build Adapter Layer

Create abstraction layer for gradual migration.

Step 3: Migrate Data

Run batch data migration with error handling.

Step 4: Shift Traffic

Gradually route traffic to new Figma integration.

Output

  • Migration assessment complete
  • Adapter layer implemented
  • Data migrated successfully
  • Traffic fully shifted to Figma

Error Handling

Issue Cause Solution
Data mismatch Transform errors Validate transform logic
Performance drop No caching Add caching layer
Rollback triggered Errors spiked Reduce traffic percentage
Validation failed Missing data Check batch processing

Examples

Quick Migration Status


const status = await validateFigmaMigration();
console.log(`Migration ${status.passed ? 'PASSED' : 'FAILED'}`);
status.checks.forEach(c => console.log(`  ${c.name}: ${c.result.success}`));

Resources

Flagship+ Skills

For advanced troubleshooting, see figma-advanced-troubleshooting.

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