Claude Skills by jeremylongshore

'Optimize TwinMind transcription accuracy and speed with Ear-3 model

'Complete production deployment checklist for TwinMind integrations.

'Implement TwinMind rate limiting, backoff, and optimization patterns.

'Production architecture for meeting AI systems using TwinMind: transcription

'Apply production-ready TwinMind SDK patterns for TypeScript and Python.

'Security best practices for TwinMind: on-device audio processing, encrypted

'Upgrade between TwinMind plan tiers and migrate configurations.

'Handle TwinMind meeting events including transcription completion, action

'Configure Vast.ai CI/CD integration with GitHub Actions and automated

'Diagnose and fix Vast.ai common errors and exceptions.

'Execute Vast.ai primary workflow: GPU instance provisioning and job

'Execute Vast.ai secondary workflow: multi-instance orchestration, spot

'Optimize Vast.ai GPU cloud costs through smart instance selection and

'Manage training data and model artifacts securely on Vast.ai GPU instances.

'Collect Vast.ai debug evidence for support tickets and troubleshooting.

'Deploy ML training jobs and inference services on Vast.ai GPU cloud.

'Implement team access control and spending governance for Vast.ai GPU

'Rent your first GPU instance on Vast.ai and run a workload.

'Execute Vast.ai incident response for GPU instance failures and outages.

'Install and configure Vast.ai CLI and REST API authentication.

'Configure Vast.ai local development with testing and fast iteration.

'Migrate GPU workloads to or from Vast.ai, or between GPU providers.

'Configure Vast.ai GPU cloud across dev, staging, and production environments.

'Monitor Vast.ai GPU instance health, utilization, and costs.

'Optimize Vast.ai GPU instance selection, startup time, and training

'Execute Vast.ai production deployment checklist for GPU workloads.

'Handle Vast.ai API rate limits with backoff and request optimization.

'Implement Vast.ai reference architecture for GPU compute workflows.

'Apply production-ready Vast.ai SDK patterns for Python and REST API.

'Apply Vast.ai security best practices for API keys and instance access.

'Upgrade Vast.ai CLI, migrate API versions, and handle breaking changes.

'Build event-driven workflows around Vast.ai instance lifecycle events.

'Advanced debugging for hard-to-diagnose Vercel issues including cold

'Choose and implement Vercel architecture blueprints for different scales

'Configure Vercel CI/CD with GitHub Actions, preview deployments, and

'Diagnose and fix common Vercel deployment and function errors.

'Optimize Vercel costs through plan selection, function efficiency, and

'Implement data handling, PII protection, and GDPR/CCPA compliance for

'Collect Vercel debug evidence for support tickets and troubleshooting.

'Deploy and manage Vercel production deployments with promotion, rollback,

'Create and manage Vercel preview deployments for branches and pull requests.

'Build and deploy Vercel Edge Functions for ultra-low latency at the

'Configure Vercel enterprise RBAC, access groups, SSO integration, and

'Create a minimal working Vercel deployment with a serverless API route.

'Vercel incident response procedures with triage, instant rollback, and

'Install Vercel CLI and configure API token authentication.

'Identify and avoid Vercel anti-patterns and common integration mistakes.

'Load test and scale Vercel deployments with concurrency tuning and capacity

'Configure Vercel local development with vercel dev, environment variables,

'Migrate to Vercel from other platforms or re-architecture existing Vercel