plugins/salesforce-master/skills/hyperforce-2025/SKILL.md
Salesforce Hyperforce public cloud infrastructure and architecture (2025). PROACTIVELY activate for: (1) understanding Hyperforce architecture (multi-region, public cloud), (2) Hyperforce migration planning, (3) data residency and regional deployments, (4) Hyperforce security model (BYOK, Shield encryption), (5) network architecture (PrivateLink, public IP allowlists), (6) Hyperforce vs first-generation infrastructure differences, (7) backup and disaster recovery on Hyperforce, (8) Hyperforce performance and SLA, (9) compliance certifications (HIPAA, FedRAMP, GDPR). Provides: Hyperforce overview, migration checklist, network architecture patterns, BYOK setup, and DR/backup configuration.
npx skillsauth add JosiahSiegel/claude-plugin-marketplace hyperforce-2025Install this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
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MANDATORY: Always Use Backslashes on Windows for File Paths
When using Edit or Write tools on Windows, you MUST use backslashes (\) in file paths, NOT forward slashes (/).
Examples:
D:/repos/project/file.tsxD:\repos\project\file.tsxThis applies to:
NEVER create new documentation files unless explicitly requested by the user.
Hyperforce is Salesforce's next-generation infrastructure architecture built on public cloud platforms (AWS, Azure, Google Cloud). It represents a complete re-architecture of Salesforce from data center-based infrastructure to cloud-native, containerized microservices.
Key Innovation: Infrastructure as code that can be deployed anywhere, giving customers choice, control, and data residency compliance.
Traditional: Patch and update existing servers Hyperforce: Destroy and recreate servers with each deployment
Old Architecture:
Server → Patch → Patch → Patch → Configuration Drift
Hyperforce:
Container Image v1 → Deploy
New Code → Build Container Image v2 → Replace v1 with v2
Result: Every deployment is identical, reproducible
Benefits:
Architecture:
Region: US-East (Virginia)
├─ Availability Zone A (Data Center 1)
│ ├─ App Servers (Kubernetes pods)
│ ├─ Database Primary
│ └─ Load Balancer
├─ Availability Zone B (Data Center 2)
│ ├─ App Servers (Kubernetes pods)
│ ├─ Database Replica
│ └─ Load Balancer
└─ Availability Zone C (Data Center 3)
├─ App Servers (Kubernetes pods)
├─ Database Replica
└─ Load Balancer
Traffic Distribution: Round-robin across all AZs
Failure Handling: If AZ fails, traffic routes to remaining AZs
RTO (Recovery Time Objective): <5 minutes
RPO (Recovery Point Objective): <30 seconds
Impact on Developers:
Traditional: Perimeter security (firewall protects everything inside) Hyperforce: No implicit trust - verify everything, always
Zero Trust Model:
├─ Identity Verification (MFA required for all users by 2025)
├─ Device Trust (managed devices only)
├─ Network Segmentation (micro-segmentation between services)
├─ Least Privilege Access (minimal permissions by default)
├─ Continuous Monitoring (real-time threat detection)
└─ Encryption Everywhere (TLS 1.3, data at rest encryption)
Code Impact:
// OLD: Assume internal traffic is safe
public without sharing class InternalService {
// No auth checks - trusted network
}
// HYPERFORCE: Always verify, never trust
public with sharing class InternalService {
// Always enforce sharing rules
// Always validate session
// Always check field-level security
public List<Account> getAccounts() {
// WITH SECURITY_ENFORCED prevents data leaks
return [SELECT Id, Name FROM Account WITH SECURITY_ENFORCED];
}
}
2025 Requirements:
Everything defined as code, version-controlled:
# Hyperforce deployment manifest (conceptual)
apiVersion: hyperforce.salesforce.com/v1
kind: SalesforceOrg
metadata:
name: production-org
region: aws-us-east-1
spec:
edition: enterprise
features:
- agentforce
- dataCloud
- einstein
compute:
pods: 50
autoScaling:
min: 10
max: 100
targetCPU: 70%
storage:
size: 500GB
replication: 3
backup:
frequency: hourly
retention: 30days
networking:
privateLink: enabled
ipWhitelist:
- 203.0.113.0/24
Benefits for Developers:
Hyperforce rebuilt from scratch:
┌────────────────────────────────────────────────────────┐
│ AWS Region (us-east-1) │
├────────────────────────────────────────────────────────┤
│ VPC (Virtual Private Cloud) │
│ ├─ Public Subnets (3 AZs) │
│ │ └─ Application Load Balancer (ALB) │
│ ├─ Private Subnets (3 AZs) │
│ │ ├─ EKS Cluster (Kubernetes) │
│ │ │ ├─ Salesforce App Pods (autoscaling) │
│ │ │ ├─ Metadata Service Pods │
│ │ │ ├─ API Gateway Pods │
│ │ │ └─ Background Job Pods (Batch, Scheduled) │
│ │ ├─ RDS Aurora PostgreSQL (multi-AZ) │
│ │ ├─ ElastiCache Redis (session storage) │
│ │ └─ S3 Buckets (attachments, documents) │
│ └─ Database Subnets (3 AZs) │
│ └─ Aurora Database Cluster │
├────────────────────────────────────────────────────────┤
│ Additional Services │
│ ├─ CloudWatch (monitoring, logs) │
│ ├─ CloudTrail (audit logs) │
│ ├─ AWS Shield (DDoS protection) │
│ ├─ AWS WAF (web application firewall) │
│ ├─ KMS (encryption key management) │
│ └─ PrivateLink (secure connectivity) │
└────────────────────────────────────────────────────────┘
AWS Services Used:
Azure Region (East US)
├─ Virtual Network (VNet)
│ ├─ AKS (Azure Kubernetes Service)
│ │ └─ Salesforce workloads
│ ├─ Azure Database for PostgreSQL (Hyperscale)
│ ├─ Azure Cache for Redis
│ └─ Azure Blob Storage
├─ Azure Front Door (CDN + Load Balancer)
├─ Azure Monitor (logging, metrics)
├─ Azure Active Directory (identity)
└─ Azure Key Vault (secrets, encryption)
GCP Region (us-central1)
├─ VPC Network
│ ├─ GKE (Google Kubernetes Engine)
│ ├─ Cloud SQL (PostgreSQL)
│ ├─ Memorystore (Redis)
│ └─ Cloud Storage (GCS)
├─ Cloud Load Balancing
├─ Cloud Armor (DDoS protection)
├─ Cloud Monitoring (Stackdriver)
└─ Cloud KMS (encryption)
Available Hyperforce Regions:
Americas:
├─ US East (Virginia) - AWS, Azure
├─ US West (Oregon) - AWS
├─ US Central (Iowa) - GCP
├─ Canada (Toronto) - AWS
└─ Brazil (São Paulo) - AWS
Europe:
├─ UK (London) - AWS
├─ Germany (Frankfurt) - AWS, Azure
├─ France (Paris) - AWS
├─ Ireland (Dublin) - AWS
└─ Switzerland (Zurich) - AWS
Asia Pacific:
├─ Japan (Tokyo) - AWS
├─ Australia (Sydney) - AWS
├─ Singapore - AWS
├─ India (Mumbai) - AWS
└─ South Korea (Seoul) - AWS
Middle East:
└─ UAE (Dubai) - AWS
What stays in region:
What may leave region:
Code Implication:
// Data residency automatically enforced
// No code changes needed - Hyperforce handles it
// Example: File stored in org's region
ContentVersion cv = new ContentVersion(
Title = 'Customer Contract',
PathOnClient = 'contract.pdf',
VersionData = Blob.valueOf('contract data')
);
insert cv;
// File automatically stored in:
// - AWS S3 in org's region
// - Encrypted at rest (AES-256)
// - Replicated across 3 AZs in region
// - Never leaves region boundary
Hyperforce maintains:
Old Architecture (data center-based):
User (Germany) → Transatlantic cable → US Data Center → Response
Latency: 150-200ms
Hyperforce:
User (Germany) → Frankfurt Hyperforce Region → Response
Latency: 10-30ms
Result: 5-10x faster for regional users
Traditional: Fixed capacity, must provision for peak load Hyperforce: Dynamic scaling based on demand
Business Hours (9 AM - 5 PM):
├─ High user load
├─ Kubernetes scales up pods: 50 → 150
└─ Response times maintained
Off Hours (6 PM - 8 AM):
├─ Low user load
├─ Kubernetes scales down pods: 150 → 30
└─ Cost savings (pay for what you use)
Black Friday (peak event):
├─ Extreme load
├─ Kubernetes scales to maximum: 30 → 500 pods in minutes
└─ No downtime, no performance degradation
Governor Limits - No Change:
// Hyperforce does NOT change governor limits
// Limits remain the same as classic Salesforce:
// - 100 SOQL queries per transaction
// - 150 DML statements
// - 6 MB heap size (sync), 12 MB (async)
// But: Infrastructure scales to handle more concurrent users
Detailed Hyperforce migration phases, readiness checks, pre/post-migration testing, rollback considerations, developer workflow changes, CLI/API notes, sandbox strategy, endpoint handling, and deployment considerations live in references/migration-and-developer-workflow.md. Load that reference when planning or executing a Hyperforce move.
Expected Enhancements:
Hyperforce represents Salesforce's commitment to modern, cloud-native infrastructure that scales globally while meeting the most stringent compliance and performance requirements.
development
This skill should be used when the user asks to train, debug, scale, or improve ML models. PROACTIVELY activate for: (1) PyTorch, TensorFlow/Keras, JAX, Flax, Hugging Face Trainer/Accelerate training loops, (2) distributed training, DDP/FSDP/DeepSpeed, TPU/GPU setup, (3) mixed precision AMP/bf16, gradient accumulation, checkpointing, seeding, (4) overfitting, imbalance, loss functions, regularization, LR schedules, warmup, (5) memory optimization, gradient checkpointing, offloading, quantization-aware training. Provides: reproducible training best practices across deep learning and classical ML.
development
This skill should be used when the user asks to productionize, track, version, govern, monitor, or automate ML systems. PROACTIVELY activate for: (1) MLflow, Weights & Biases, Neptune, Comet, ClearML experiment tracking, (2) model registry, model versioning, artifact lineage, reproducibility, (3) Kubeflow, SageMaker Pipelines, Vertex AI Pipelines, Azure ML pipelines, Databricks workflows, (4) CI/CD, continuous training/evaluation, A/B tests, canary/shadow deployments, (5) drift detection, model monitoring, data validation, responsible AI governance. Provides: end-to-end MLOps architecture and operational safeguards.
development
This skill should be used when the user asks to optimize, export, serve, compress, or accelerate ML inference. PROACTIVELY activate for: (1) latency, throughput, p95/p99, batching, concurrency, KV cache, memory, or cost issues, (2) quantization INT8/INT4, GPTQ, AWQ, bitsandbytes, pruning, sparsity, distillation, (3) ONNX export, ONNX Runtime, TensorRT, TorchScript, torch.compile, XLA, OpenVINO, Core ML, TFLite, (4) Triton, TorchServe, TF Serving, BentoML, Seldon, KServe configuration, (5) edge deployment, CPU/GPU/TPU/Inferentia serving. Provides: hardware-aware inference optimization and safe benchmarking.
testing
This skill should be used when the user asks to tune hyperparameters, run sweeps, optimize search spaces, or use AutoML. PROACTIVELY activate for: (1) Optuna, Ray Tune, FLAML, AutoGluon, Hyperopt, Nevergrad, KerasTuner, W&B sweeps, (2) grid search, random search, Bayesian optimization, TPE, Gaussian processes, evolutionary search, (3) ASHA, Hyperband, successive halving, multi-fidelity optimization, population-based training, (4) learning-rate finder, batch-size search, early stopping, pruning, (5) reproducible sweep design and experiment analysis. Provides: budget-aware hyperparameter search strategy.