Skill UI

Learn how to build complex, multi-step AI agents using Cohere's Chat API v2. This skill demonstrates defining external tools (like weather APIs or database searches) and implementing the full agent loop: model calls tool -> execute tool -> feed result back -> final answer. Ideal for creating autonomous, tool-using workflows that interact with external systems.

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 skill provides robust mechanisms to ensure data privacy when interacting with Cohere APIs (Chat, Embed, etc.). It implements advanced PII detection (emails, SSNs, IP addresses, etc.), performs pre-call redaction to mask sensitive information, and includes post-call scrubbing and detailed audit logging. Essential for achieving GDPR/CCPA compliance and safely handling sensitive user data.

This skill provides comprehensive deployment guides for Cohere API v2 applications across major cloud platforms including Vercel, Fly.io, and Google Cloud Run. It covers critical aspects like secret management, setting up reliable health checks, optimizing performance, and implementing streaming chat endpoints to ensure production readiness.

This comprehensive runbook provides structured procedures for handling Cohere API outages. It guides users through incident triage (P1-P4), quick troubleshooting steps, and detailed mitigation strategies for common error codes (401, 429, 5xx). Includes best practices for graceful degradation and post-incident review, ensuring rapid service restoration.

This comprehensive guide details the process of installing and configuring the Cohere SDK (v2) in both Node.js and Python environments. It covers initial setup, setting API keys via environment variables, and provides full code examples for verifying connectivity across various Cohere endpoints, including Chat, Embed, and Rerank, enabling immediate integration into your AI projects.

This guide sets up a comprehensive, fast, and reproducible local development workflow for integrating with the Cohere API v2. It utilizes mocking, vitest for unit testing, and hot reload capabilities (tsx watch) to ensure rapid iteration cycles without constant reliance on external APIs. Ideal for developers building AI applications with Cohere.

This comprehensive guide provides a deep dive into migrating AI applications from major LLM providers (like OpenAI or Anthropic) to the Cohere platform. It covers critical technical steps, including API call adaptation, re-vectorizing embeddings, migrating tool definitions, and implementing the Adapter Pattern for seamless, gradual re-platforming of existing AI services.

A comprehensive guide for configuring and integrating the Cohere API v2 across development, staging, and production environments. This pattern ensures environment-specific settings—including API keys, model selections, token limits, and caching strategies—are correctly applied. It covers best practices for secure secret management using tools like Vault, AWS Secrets Manager, and GCP Secret Manager, ensuring robust and scalable deployments.

A comprehensive solution for setting up production-grade observability for Cohere API v2. It integrates Prometheus metrics, OpenTelemetry tracing, and robust alerting mechanisms. This skill tracks critical operational data including per-endpoint latency, token consumption (input/output), error rates, and usage costs, ensuring reliable monitoring for AI-powered applications.

A comprehensive guide for optimizing Cohere API v2 performance. Learn advanced strategies to improve throughput, reduce latency, and manage costs when using Chat, Embed, and Rerank endpoints. Techniques covered include dynamic model selection based on latency budgets, implementing streaming for faster perceived response times, efficient batch processing for embeddings, vector compression, and robust caching mechanisms.

This comprehensive checklist guides the safe and robust deployment of Cohere API v2 integrations from staging to production. It covers critical steps including API key management, code quality standards, performance tuning, implementing health checks and circuit breakers, and defining full rollback procedures, ensuring mission-critical AI features are reliable for launch.