Skill UI

A comprehensive runbook designed to guide developers through responding to Anthropic API incidents, including outages, sustained errors (like 529), and rate limit issues. It covers systematic steps like checking the official status page, classifying incident severity, implementing model fallback logic (e.g., downgrading Opus to Sonnet), and conducting post-incident impact assessments to ensure service resilience.

A comprehensive guide detailing 10 common mistakes and best practices when integrating with the Anthropic API (Claude). It covers critical technical aspects such as correctly setting `max_tokens`, using the top-level `system` parameter, managing message alternation, checking `stop_reason`, and optimizing client initialization for robust AI application development.

This guide sets up a fast, cost-effective local development loop for building applications powered by the Anthropic API (Claude). It includes instructions for hot reloading, cost-saving tips (e.g., using Haiku, token limits), and mock client implementation for robust unit testing. It supports both TypeScript/Node.js and Python environments, streamlining the AI development workflow.

Provides best practices for configuring Large Language Model (LLM) access, specifically for Anthropic Claude, across development, staging, and production environments. This guide details setting up separate API keys, selecting appropriate models (e.g., Haiku, Sonnet, Opus), and managing unique rate limits for robust, scalable, and secure LLM integration in enterprise systems.

This guide details essential best practices for securing applications integrated with Anthropic's Claude API. It covers critical security measures including secure API key management (server-side only), robust input validation to prevent cost attacks, defense mechanisms against prompt injection, and implementing per-user rate limiting. Essential reading for maintaining data privacy and system integrity in production environments.

This guide provides strategies to minimize API overhead when integrating with Clari. Users can optimize export frequency (e.g., daily vs. quarterly), implement data caching, and restrict data exports to only the necessary types, thereby reducing operational costs and improving system performance.

This checklist guides the secure and robust deployment of Clari API integrations into a production data pipeline. It covers crucial areas like secret management, rate limit handling, data deduplication, error resilience (circuit breakers, backoff), and comprehensive monitoring setup, ensuring reliable revenue forecasting and reporting.

This guide outlines best practices for securing your Clari integration. It covers secure API token management, emphasizing storage in secrets managers (like AWS Secrets Manager), and implementing robust PII redaction methods for exported forecast data to maintain data privacy and compliance.

Automate the execution of integration tests against a live ClickHouse database instance within a Continuous Integration (CI) pipeline using Docker containers. This setup eliminates the need for mocks, ensuring thorough schema validation and query reliability in environments mirroring production deployments. Ideal for robust CI/CD setup and schema enforcement.

Provides comprehensive methods for executing ClickHouse schema migrations, including column changes, engine migrations, and complex modifications like changing ORDER BY. It teaches best practices for zero-downtime schema updates and implements a versioned migration runner pattern for robust data evolution.

A comprehensive guide and code pattern for configuring ClickHouse instances across development, staging, and production environments. It emphasizes best practices for infrastructure-as-code (IaC), secure secrets management (integrating AWS Secrets Manager, Vault, etc.), and ensuring environment-specific settings are correctly applied for reliable data analytics pipelines.

Provides a comprehensive, production-ready reference architecture for building advanced analytics platforms powered by ClickHouse. It details everything from project layout (TypeScript/Node.js structure) and data ingestion pipelines (Kafka, Webhooks) to the optimal 3-layer schema design (Raw, Hourly, Daily) and multi-tenancy patterns, ensuring scalable and efficient OLAP query performance.