# SWARMCARE EXECUTION - Instance instance_01 - Phase 0

## AUTONOMOUS EXECUTION MODE
- TAKE FULL CONTROL: Do not ask for confirmation
- PRODUCTION-READY ONLY: Every output must be deployment-ready
- 100% SUCCESS RATE: Build comprehensive validation at every step
- PARALLEL EVERYTHING: Run all independent tasks simultaneously

## PHASE: Foundation & Infrastructure

### Phase Overview
- **Phase ID:** 0
- **Story Points:** 37
- **Priority:** P0
- **Dependencies:** None

### User Stories to Implement

#### User Story 1.1: Development Environment Setup
**As a** Developer
**I want** a fully configured development environment
**So that** I can start building features immediately

**Acceptance Criteria:**
- [ ] Python 3.11+ installed with virtual environment
- [ ] Node.js 20+ installed with npm/pnpm
- [ ] Docker Desktop running with all containers
- [ ] VS Code with Claude Code extension
- [ ] Git configured with SSH keys
- [ ] Access to all required repositories

**Implementation Tasks:**
1. Install Python 3.11 (pyenv recommended)
2. Install Node.js 20 (nvm recommended)
3. Install Docker Desktop
4. Install VS Code + extensions (Python, ESLint, Prettier, Claude Code)
5. Configure Git (name, email, SSH key)
6. Clone repositories: RAG Heat, SWARMCARE

**Story Points:** 3
**Priority:** P0


#### User Story 1.2: Cloud Infrastructure Provisioning
**As a** DevOps Engineer
**I want** production-ready cloud infrastructure
**So that** all services can be deployed and scaled

**Acceptance Criteria:**
- [ ] GCP/AWS account created and configured
- [ ] Kubernetes cluster (GKE/EKS) provisioned
- [ ] VPC, subnets, security groups configured
- [ ] Cloud databases provisioned (PostgreSQL, Redis)
- [ ] Monitoring stack deployed (Prometheus, Grafana)
- [ ] CI/CD pipeline active (GitHub Actions)

**Implementation Tasks:**
1. Create GCP project or AWS account
2. Provision GKE cluster (3 nodes minimum)
3. Set up VPC with public/private subnets
4. Deploy PostgreSQL (Cloud SQL or RDS)
5. Deploy Redis (Cloud Memorystore or ElastiCache)
6. Set up Prometheus + Grafana
7. Configure GitHub Actions for CI/CD
8. Set up secrets management (GCP Secret Manager or AWS Secrets Manager)

**Story Points:** 13
**Priority:** P0


#### User Story 1.3: Neo4j Knowledge Graph Setup
**As a** Data Engineer
**I want** a Neo4j instance with medical ontologies loaded
**So that** the RAG system can perform knowledge graph queries

**Acceptance Criteria:**
- [ ] Neo4j 5.x deployed (cloud or self-hosted)
- [ ] 13 medical ontologies downloaded
- [ ] Ontology loading scripts created
- [ ] All ontologies loaded into Neo4j
- [ ] Indexes created for performance
- [ ] Sample queries validated

**Implementation Tasks:**
1. Deploy Neo4j (Neo4j Aura or self-hosted on GCP/AWS)
2. Download 13 ontologies (SNOMED-CT, ICD-10/11, RxNorm, LOINC, CPT, HL7 FHIR, UMLS, HPO, GO, DO, DrugBank, CQL, OMOP CDM)
3. Create Python scripts for ontology parsing
4. Transform ontologies to graph format
5. Batch load into Neo4j
6. Create indexes on common fields
7. Write validation queries
8. Test query performance (<100ms for simple queries)

**Story Points:** 21
**Priority:** P0


### Success Criteria
- All user stories implemented
- All tests passing (>80% coverage)
- Code reviewed and optimized
- Documentation complete
- No critical security vulnerabilities

### Deliverables
- Production-ready code in Git
- Comprehensive test suite
- API documentation (if applicable)
- Integration tests passing

### Tracking
- Mark each user story complete in the phase state file
- Create checkpoint every 30 minutes
- Update progress percentage
- Log all significant events

### Next Steps After Completion
1. Run all tests
2. Generate documentation
3. Create checkpoint
4. Mark phase as COMPLETED
5. Notify integration coordinator

BEGIN EXECUTION NOW.
