Skill UI

Implements automated policy enforcement and guardrails for Canva Connect API integrations. This system enforces best practices such as preventing hardcoded credentials, ensuring proper rate limit handling (429 responses), and validating robust OAuth configurations. It is essential for maintaining high code quality and security in CI/CD pipelines and pre-commit hooks.

Integrates CAST AI cost validation into CI/CD pipelines (GitHub Actions, Terraform). This skill automatically enforces cost governance by checking minimum savings thresholds, validating Terraform plans to prevent destructive changes, and ensuring agent health status before deployment, guaranteeing cost-aware and safe deployments.

A comprehensive guide for testing and validating Claude API integrations within Continuous Integration/Continuous Deployment (CI/CD) pipelines. Learn best practices for managing API keys securely, implementing effective mocking strategies for unit tests, and executing real integration tests while controlling costs using tools like GitHub Actions.

Builds a robust, automated workflow to export critical sales data—including forecasts, quota details, adjustments, and CRM totals—from Clari CRM to a designated data warehouse. The pipeline supports multiple targets like Snowflake, BigQuery, and PostgreSQL, ensuring that real-time revenue intelligence is available for advanced analytics and business reporting.

This guide provides a comprehensive walkthrough for interacting with the Clari API to export critical business data. It covers listing available forecasts, initiating the export of forecast and quota snapshots, monitoring the job status, and ultimately downloading and parsing the structured data. This skill is ideal for testing API connectivity and integrating Clari's revenue intelligence into external data pipelines.

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 reference architecture provides a comprehensive blueprint for building a robust revenue intelligence data platform integrated with Clari. It covers the entire data lifecycle, including API data extraction (ETL pipeline), data warehousing design (Snowflake/BQ), change detection logic, and advanced analytics/alerting layers. Use this guide when designing a scalable forecast tracking system, planning complex data integrations, or establishing best practices for enterprise revenue analytics.

This skill manages the complexities of Clari API version changes, ensuring smooth transitions (e.g., v3 to v4). It guides developers through detecting schema drift, performing data field compatibility checks, and executing necessary database migrations (SQL/Python), ensuring reliable data pipelines after platform updates.

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.

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.

This guide provides production-ready patterns for using the @clickhouse/client library in Node.js. It covers critical, real-world scenarios such as safe typed queries, streaming large data inserts (with backpressure handling), robust batch operations with retries, and streaming SELECT queries for low memory consumption. It also includes best practices for comprehensive error handling and connection lifecycle management, ensuring stable and scalable ClickHouse integrations for data pipelines.