Skill UI

Provides strategies to optimize costs when utilizing the Cohere API. This guide covers model tier selection (economy, standard, premium), implementing token budgets, reducing embedding storage costs (e.g., using int8), and establishing comprehensive usage trackers. It is essential for maintaining cost predictability in production AI deployments.

This guide details how to set up and execute large-scale, distributed GPU training jobs on the CoreWeave platform. It covers both single-node multi-GPU setups and multi-node training utilizing PyTorch DDP and Kubernetes resources (A100/H100). This workflow is ideal for fine-tuning large language models (LLMs) or running complex deep learning models that require high-performance, shared-storage compute clusters.

A comprehensive checklist for ensuring GPU workloads, such as inference services or model training pipelines, are fully prepared for production deployment on CoreWeave. It covers critical MLOps and DevOps aspects including autoscaling, resource limits, security policies, persistent storage validation, monitoring setup, and rollback procedures.

This reference architecture provides a comprehensive blueprint for deploying Machine Learning models on CoreWeave's GPU cloud infrastructure. It details the structure for multi-model serving (e.g., vLLM, TGI), scalable Kubernetes deployments, shared persistent storage (PVC), and autoscaling mechanisms using KServe/Knative. Use this when designing robust MLOps pipelines, planning high-performance multi-model inference services, or establishing standardized best practices for GPU cloud deployment.

This guide provides best practices for optimizing Fathom API usage and managing associated cloud costs. It covers strategies like implementing caching for transcripts, switching from API polling to webhooks, batching CRM writes, and establishing data retention policies (e.g., archiving after 90 days) to minimize unnecessary spending on storage and API calls.

This skill guides users through setting up robust, production-grade infrastructure on Fly.io. It covers creating and connecting to Fly Postgres clusters for reliable data storage, provisioning persistent NVMe Volumes for durable file storage, and configuring private internal networking (6PN) for secure service-to-service communication. It is essential for modern microservices requiring stable databases and persistent state.

This guide details the security best practices for managing and using Hootsuite API credentials and tokens. It covers crucial topics such as secure storage of Client IDs, Client Secrets, and various tokens (Access/Refresh), emphasizing token rotation, implementing least privilege access, and securing OAuth 2.0 workflows in production environments.

A comprehensive checklist and guide for securely deploying HubSpot CRM integrations to a production environment. It covers mandatory pre-deployment verification steps (like token scoping and secure storage), implementing robust health check endpoints, configuring critical monitoring alerts (rate limits, 401/429 errors), and executing post-deployment validation scripts to ensure system stability and reliability upon launch.

This comprehensive guide outlines essential security best practices for integrating with Intercom. It covers secure token storage, robust webhook signature verification (X-Hub-Signature), implementing Identity Verification, and adhering to least-privilege OAuth scopes. Use this guide when building secure, production-ready applications that interact with Intercom APIs.

This skill guides you through installing and setting up the official Klaviyo SDK for both Node.js and Python. It covers essential steps including authenticating with a private API key, managing environment variables for secure credential storage, and initializing resource-specific API clients (e.g., Profiles, Events). Use this when building integrations that require programmatic access to Klaviyo's marketing data, such as tracking user events or managing contact profiles.

This comprehensive skill guides users through the entire Fluid storage extension lifecycle within KubeSphere. It allows for installing, upgrading, and uninstalling the Fluid extension, and managing various data resources. Users can generate YAML manifests for Datasets and specialized runtimes (Alluxio, JuiceFS, Thin), perform data loading/cache warming, scale operations, and troubleshoot issues using both kubectl and KubeSphere APIs.

This guide provides comprehensive strategies for minimizing operational costs within the MindTickle learning platform. It details cost drivers across various components, including seat licenses, content storage, quiz assessments, call recording analysis, and API integrations. Users can learn best practices for content deduplication, implementing incremental sync, setting proper retention policies, and optimizing resource usage to reduce unnecessary spend and improve overall platform efficiency for sales organizations.