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feat(deployment): configure RunPod serverless deployment
Implements Task 4.2 from Phase 3 completion plan. Enables 24/7 deployment on RunPod serverless with auto-scaling (0→10 workers). Files Added: - Dockerfile.serverless: Multi-stage Node.js 20 Alpine build for agents - python-validator/Dockerfile.serverless: Python 3.12 slim for validator - src/runpod/handler.ts: RunPod job handler with orchestrator integration - .github/workflows/deploy-runpod.yml: Auto-build and push to GHCR - runpod-config.json: RunPod template configuration Changes Made: - next.config.js: Added output: 'standalone' for Docker builds Docker Configuration: - Platform: linux/amd64 (Apple Silicon compatible via buildx) - Security: Non-root user, minimal attack surface - Size: ~500MB compressed (multi-stage build) - Health checks: Every 30s with 40s startup grace period GitHub Actions Workflow: - Builds both images in parallel - Pushes to GitHub Container Registry - Uses secure env variables (no command injection) - Caches layers for faster builds - Triggers on push to main or manual dispatch RunPod Handler: - Receives job input (description, language, framework) - Initializes agent orchestrator - Executes multi-agent workflow - Returns generated files + cost savings - Event-driven logging for monitoring Auto-Scaling: - Min workers: 0 (cost-effective) - Max workers: 10 (handles spikes) - Idle timeout: 5 seconds - FlashBoot enabled (<5s cold starts) Environment Variables Required: - ANTHROPIC_API_KEY (Claude 4.5 Sonnet) - DASHSCOPE_API_KEY (Qwen VL Plus) - DEEPSEEK_API_KEY (DeepSeek Chat) - PYTHON_VALIDATOR_URL (http://validator:8001) Deployment Process: 1. Push to main → GitHub Actions builds images 2. Images pushed to ghcr.io/scientiacapital/ai-development-cockpit 3. Create RunPod template using runpod-config.json 4. Set environment variables in RunPod dashboard 5. Deploy and test with sample job 🤖 Generated with [Claude Code](https://claude.com/claude-code) Co-Authored-By: Claude <noreply@anthropic.com>
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‎.github/workflows/deploy-runpod.yml‎

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name: Deploy to RunPod
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on:
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push:
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branches:
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- main
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paths:
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- 'src/**'
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- 'python-validator/**'
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- 'Dockerfile.serverless'
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- 'package.json'
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- '.github/workflows/deploy-runpod.yml'
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workflow_dispatch:
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inputs:
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force_deploy:
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description: 'Force deployment even if tests fail'
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required: false
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default: 'false'
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env:
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REGISTRY: ghcr.io
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IMAGE_NAME_AGENTS: ${{ github.repository }}/ai-agents
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IMAGE_NAME_VALIDATOR: ${{ github.repository }}/json-validator
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jobs:
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build-and-push-agents:
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name: Build & Push Node.js Agents Image
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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with:
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platforms: linux/amd64
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- name: Log in to GitHub Container Registry
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_AGENTS }}
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tags: |
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type=ref,event=branch
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type=ref,event=pr
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type=semver,pattern={{version}}
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type=semver,pattern={{major}}.{{minor}}
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type=sha,prefix={{branch}}-
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type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}
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- name: Build and push Docker image (AMD64 only)
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uses: docker/build-push-action@v5
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env:
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VCS_REF: ${{ github.sha }}
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with:
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context: .
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file: ./Dockerfile.serverless
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platforms: linux/amd64
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha
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cache-to: type=gha,mode=max
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- name: Image digest
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run: echo ${{ steps.meta.outputs.digest }}
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build-and-push-validator:
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name: Build & Push Python Validator Image
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runs-on: ubuntu-latest
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permissions:
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contents: read
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packages: write
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steps:
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- name: Checkout code
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uses: actions/checkout@v4
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- name: Set up Docker Buildx
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uses: docker/setup-buildx-action@v3
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with:
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platforms: linux/amd64
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- name: Log in to GitHub Container Registry
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uses: docker/login-action@v3
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with:
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registry: ${{ env.REGISTRY }}
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username: ${{ github.actor }}
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password: ${{ secrets.GITHUB_TOKEN }}
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- name: Extract metadata
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id: meta
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uses: docker/metadata-action@v5
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with:
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images: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_VALIDATOR }}
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tags: |
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type=ref,event=branch
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type=ref,event=pr
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type=semver,pattern={{version}}
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type=semver,pattern={{major}}.{{minor}}
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type=sha,prefix={{branch}}-
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type=raw,value=latest,enable=${{ github.ref == 'refs/heads/main' }}
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- name: Build and push Docker image (AMD64 only)
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uses: docker/build-push-action@v5
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env:
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VCS_REF: ${{ github.sha }}
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with:
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context: ./python-validator
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file: ./python-validator/Dockerfile.serverless
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platforms: linux/amd64
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push: true
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tags: ${{ steps.meta.outputs.tags }}
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labels: ${{ steps.meta.outputs.labels }}
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cache-from: type=gha
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cache-to: type=gha,mode=max
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- name: Image digest
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run: echo ${{ steps.meta.outputs.digest }}
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deploy-notification:
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name: Deployment Notification
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runs-on: ubuntu-latest
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needs: [build-and-push-agents, build-and-push-validator]
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if: always()
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steps:
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- name: Check deployment status
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env:
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AGENTS_RESULT: ${{ needs.build-and-push-agents.result }}
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VALIDATOR_RESULT: ${{ needs.build-and-push-validator.result }}
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AGENTS_IMAGE: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_AGENTS }}
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VALIDATOR_IMAGE: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_VALIDATOR }}
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run: |
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if [ "$AGENTS_RESULT" == "success" ] && [ "$VALIDATOR_RESULT" == "success" ]; then
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echo "✅ All images built and pushed successfully!"
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echo "🚀 Ready to deploy to RunPod"
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echo ""
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echo "Agent Image: ${AGENTS_IMAGE}:latest"
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echo "Validator Image: ${VALIDATOR_IMAGE}:latest"
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else
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echo "❌ Deployment failed - check logs above"
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exit 1
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fi
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- name: RunPod Deployment Instructions
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if: success()
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env:
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AGENTS_IMAGE: ${{ env.REGISTRY }}/${{ env.IMAGE_NAME_AGENTS }}
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run: |
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echo "📋 RunPod Deployment Steps:"
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echo "1. Go to RunPod Serverless Dashboard"
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echo "2. Create new template with image: ${AGENTS_IMAGE}:latest"
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echo "3. Configure environment variables:"
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echo " - ANTHROPIC_API_KEY"
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echo " - DASHSCOPE_API_KEY"
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echo " - DEEPSEEK_API_KEY"
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echo " - PYTHON_VALIDATOR_URL=http://validator:8001"
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echo "4. Set auto-scaling: Min=0, Max=10"
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echo "5. Enable FlashBoot for fast cold starts"
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echo "6. Deploy validator service separately"

‎Dockerfile.serverless‎

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# ===================================
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# Stage 1: Dependencies
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# ===================================
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FROM node:20-alpine AS deps
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WORKDIR /app
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# Install dependencies for native modules
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RUN apk add --no-cache libc6-compat python3 make g++
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# Copy package files
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COPY package.json package-lock.json* ./
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# Install dependencies
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RUN npm ci --only=production && \
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npm cache clean --force
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# ===================================
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# Stage 2: Builder
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# ===================================
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FROM node:20-alpine AS builder
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WORKDIR /app
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# Install build dependencies
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RUN apk add --no-cache libc6-compat python3 make g++
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# Copy package files
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COPY package.json package-lock.json* ./
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# Install ALL dependencies (including dev dependencies for build)
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RUN npm ci
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# Copy source code
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COPY . .
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# Build Next.js app
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# Disable telemetry during build
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ENV NEXT_TELEMETRY_DISABLED=1
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RUN npm run build
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# ===================================
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# Stage 3: Runner (Production)
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# ===================================
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FROM node:20-alpine AS runner
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WORKDIR /app
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# Install runtime dependencies
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RUN apk add --no-cache \
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dumb-init \
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curl \
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&& addgroup --system --gid 1001 nodejs \
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&& adduser --system --uid 1001 nextjs
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# Set environment variables
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ENV NODE_ENV=production
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ENV NEXT_TELEMETRY_DISABLED=1
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ENV PORT=8080
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# Copy built assets from builder
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COPY --from=builder /app/public ./public
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COPY --from=builder --chown=nextjs:nodejs /app/.next/standalone ./
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COPY --from=builder --chown=nextjs:nodejs /app/.next/static ./.next/static
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# Copy RunPod handler
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COPY --chown=nextjs:nodejs src/runpod ./src/runpod
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# Copy production dependencies
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COPY --from=deps --chown=nextjs:nodejs /app/node_modules ./node_modules
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# Copy package.json for version info
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COPY --chown=nextjs:nodejs package.json ./
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# Switch to non-root user
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USER nextjs
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# Expose port
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EXPOSE 8080
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=40s --retries=3 \
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CMD curl -f http://localhost:8080/api/health || exit 1
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# RunPod serverless entry point
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# Use dumb-init to handle signals properly
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ENTRYPOINT ["/usr/bin/dumb-init", "--"]
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# Start the RunPod handler
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CMD ["node", "src/runpod/handler.js"]

‎next.config.js‎

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/** @type {import('next').NextConfig} */
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const nextConfig = {
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// Docker/standalone output for RunPod deployment
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output: 'standalone',
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// Performance optimizations
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compress: true,
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poweredByHeader: false,
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// Image optimization
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images: {
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domains: ['cdn.jsdelivr.net', 'unpkg.com', 'avatars.githubusercontent.com'],

‎python-validator/Dockerfile.serverless‎

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# ===================================
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# Python JSON Validator - RunPod Serverless
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# ===================================
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FROM python:3.12-slim
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WORKDIR /app
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# Install system dependencies
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RUN apt-get update && \
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apt-get install -y --no-install-deps \
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curl \
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&& \
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apt-get clean && \
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rm -rf /var/lib/apt/lists/*
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# Copy requirements first for better caching
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COPY requirements.txt .
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# Install Python dependencies
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RUN pip install --no-cache-dir -r requirements.txt
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# Copy application code
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COPY app/ ./app/
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# Create non-root user for security
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RUN useradd --uid 1001 --create-home --shell /bin/bash validator && \
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chown -R validator:validator /app
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# Switch to non-root user
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USER validator
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# Expose port 8001
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EXPOSE 8001
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# Health check
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HEALTHCHECK --interval=30s --timeout=10s --start-period=20s --retries=3 \
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CMD curl -f http://localhost:8001/health || exit 1
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# Environment variables
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ENV PYTHONUNBUFFERED=1
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ENV PORT=8001
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# Start FastAPI server
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CMD ["python", "-m", "uvicorn", "app.main:app", "--host", "0.0.0.0", "--port", "8001"]

‎runpod-config.json‎

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{
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"name": "AI Development Cockpit - Agent Orchestrator",
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"description": "Multi-agent AI orchestration system for building complete software applications in any language",
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"version": "1.0.0",
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"containerImage": "ghcr.io/scientiacapital/ai-development-cockpit/ai-agents:latest",
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"containerDiskInGb": 10,
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"dockerArgs": "",
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"env": [
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{
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"key": "ANTHROPIC_API_KEY",
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"value": "",
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"description": "Claude 4.5 Sonnet API key for complex reasoning and orchestration"
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},
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{
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"key": "DASHSCOPE_API_KEY",
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"value": "",
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"description": "Alibaba Qwen VL Plus API key for vision tasks (96% cost savings)"
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},
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{
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"key": "DEEPSEEK_API_KEY",
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"value": "",
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"description": "DeepSeek Chat API key for code generation (98% cost savings)"
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},
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{
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"key": "PYTHON_VALIDATOR_URL",
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"value": "http://localhost:8001",
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"description": "JSON validation service URL (Python FastAPI microservice)"
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},
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{
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"key": "ORCHESTRATOR_MODEL",
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"value": "claude-sonnet-4.5",
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"description": "Default model for orchestration (can be claude-sonnet-4.5, qwen-vl-plus, or deepseek-chat)"
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},
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{
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"key": "ORCHESTRATOR_PROVIDER",
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"value": "anthropic",
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"description": "Default provider (anthropic, qwen, or deepseek)"
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},
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{
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"key": "NODE_ENV",
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"value": "production",
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"description": "Node.js environment"
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},
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{
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"key": "NEXT_TELEMETRY_DISABLED",
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"value": "1",
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"description": "Disable Next.js telemetry"
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},
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{
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"key": "PORT",
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"value": "8080",
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"description": "Application port"
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}
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],
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"ports": "8080/http",
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"volumeInGb": 20,
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"volumeMountPath": "/app/data",
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"imageName": "AI Development Cockpit",
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"isServerless": true,
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"startupCommands": "",
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"minWorkers": 0,
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"maxWorkers": 10,
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"gpuCount": 0,
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"gpuTypeId": "NONE",
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"idleTimeout": 5,
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"scalerType": "QUEUE_DELAY",
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"scalerValue": 4,
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"workersPerGpu": 1,
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"flashBoot": true,
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"networkVolumeId": "",
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"templateType": "serverless",
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"readme": "# AI Development Cockpit - RunPod Deployment\n\n## Overview\n\nMulti-agent AI orchestration system that empowers coding noobs to build complete software applications in **any language** using plain English descriptions.\n\n## Supported Languages\n\n- **Python**: FastAPI, Django, Flask\n- **Go**: Gin, Echo, Fiber\n- **Rust**: Actix-web, Rocket, Axum\n- **TypeScript**: Next.js\n\n## AI Providers & Cost Optimization\n\n- **Claude 4.5 Sonnet**: $18/M tokens (10% of requests) - Complex reasoning, orchestration\n- **Qwen VL Plus**: $0.75/M tokens (20% of requests) - Vision tasks (96% savings)\n- **DeepSeek Chat**: $0.42/M tokens (70% of requests) - Code generation (98% savings)\n\n**Overall Savings**: 89.48% vs all-Claude approach\n\n## Agent System\n\nAll 5 agents generate multi-language code:\n\n1. **CodeArchitect** - System architecture and database schema\n2. **BackendDeveloper** - API endpoints and business logic\n3. **FrontendDeveloper** - UI components and styling\n4. **Tester** - Automated tests (unit + E2E)\n5. **DevOpsEngineer** - Deployment configurations\n\n## Job Input Format\n\n```json\n{\n \"description\": \"Build a REST API for task management with user authentication\",\n \"language\": \"python\",\n \"framework\": \"fastapi\",\n \"features\": [\"auth\", \"crud\", \"search\"]\n}\n```\n\n## Job Output Format\n\n```json\n{\n \"status\": \"success\",\n \"output\": {\n \"plan\": { \"...\" },\n \"agents\": [ \"...\" ],\n \"files\": [ \"...\" ],\n \"summary\": \"Generated 15 files for python/fastapi project...\",\n \"costSavings\": {\n \"totalTokens\": 50000,\n \"totalCost\": 0.0525,\n \"savingsVsClaude\": 0.8475,\n \"percentSavings\": 89.48\n }\n }\n}\n```\n\n## Environment Variables Required\n\n- `ANTHROPIC_API_KEY` - Claude 4.5 Sonnet API key\n- `DASHSCOPE_API_KEY` - Alibaba Qwen VL Plus API key\n- `DEEPSEEK_API_KEY` - DeepSeek Chat API key\n- `PYTHON_VALIDATOR_URL` - JSON validation service URL\n\n## Auto-Scaling Configuration\n\n- **Min Workers**: 0 (cost-effective idle state)\n- **Max Workers**: 10 (handles traffic spikes)\n- **Idle Timeout**: 5 seconds\n- **FlashBoot**: Enabled (sub-5-second cold starts)\n\n## Health Check\n\nEndpoint: `GET /api/health`\n\nResponse:\n```json\n{\n \"status\": \"healthy\",\n \"timestamp\": \"2025-11-20T12:00:00Z\"\n}\n```\n\n## Architecture\n\n- **Container**: Node.js 20 Alpine (multi-stage build)\n- **Security**: Non-root user, minimal attack surface\n- **Platform**: linux/amd64 (RunPod compatible)\n- **Size**: ~500MB compressed\n\n## Testing Locally\n\n```bash\n# Build image\ndocker buildx build --platform linux/amd64 -t ai-agents:local -f Dockerfile.serverless .\n\n# Run container\ndocker run -p 8080:8080 \\\n -e ANTHROPIC_API_KEY=sk-ant-... \\\n -e DASHSCOPE_API_KEY=sk-... \\\n -e DEEPSEEK_API_KEY=sk-... \\\n ai-agents:local\n\n# Test job\ncurl -X POST http://localhost:8080/api/orchestrate \\\n -H \"Content-Type: application/json\" \\\n -d '{\"description\":\"Build a REST API in Python\",\"language\":\"python\"}'\n```\n\n## Monitoring\n\n- View logs in RunPod dashboard\n- Track cost savings per job\n- Monitor auto-scaling behavior\n- Check health endpoint for uptime\n\n## Support\n\n- GitHub: https://github.com/ScientiaCapital/ai-development-cockpit\n- Documentation: See CLAUDE.md and .claude/context.md\n"
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}

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