Skill UI

This guide provides comprehensive steps for setting up and authenticating the official ClickHouse client for both Node.js and Python. It covers connecting to ClickHouse Cloud or a self-hosted instance, managing environment variables, and verifying the connection using sample code snippets. Ideal for new ClickHouse projects or integrating data analytics pipelines into existing applications.

This skill provides a comprehensive workflow for generating professional and structured financial reports. It guides the user through planning, writing, and editing various sections, and can also generate necessary chart configurations using visual tools. Ideal for condensing complex financial analysis into clear, actionable reports.

A comprehensive guide to setting up and securing API access for Clari. Learn how to generate API tokens, configure environment variables, and test connectivity to export critical revenue intelligence and forecast data to data warehouses like Snowflake or BigQuery. Includes steps for both standard Clari API and Copilot API integration.

This comprehensive checklist guides users through critical operational readiness steps for a ClickHouse production deployment. It covers server tuning (config.xml), schema design best practices, robust backup strategies (S3), detailed monitoring setup (Prometheus/Grafana), security hardening, and application integration verification. Use this document before go-live, during major upgrades, or when auditing production systems to ensure maximum stability and performance.

This comprehensive guide details best practices for securing a ClickHouse deployment. It covers essential security measures including role-based access control (RBAC) for granular user permissions, implementing row-level security for multi-tenancy, configuring network IP restrictions, enabling TLS encryption, and setting up detailed audit logging. Use this when hardening a production ClickHouse instance or enforcing strict data governance.

This skill provides comprehensive patterns for extracting and transforming Navan booking and transaction data. It addresses complex data pipeline challenges such as API pagination, date range chunking, and UUID-based deduplication. It supports both direct REST API implementation (via code examples) and configuration for managed connectors like Fivetran and Airbyte. Ideal for populating data warehouses, building robust analytics dashboards, or ensuring data quality from Navan sources.

A comprehensive guide to diagnosing and enhancing search relevance within Qdrant. This skill addresses reported issues such as low precision, low recall, or irrelevant matches. It guides users through determining if the problem lies in the embedding model, Qdrant configuration, or the query strategy. Techniques covered include optimizing chunk splitting, tuning HNSW parameters, implementing hybrid search, and utilizing reranking for superior result quality.

This guide provides a comprehensive methodology for load testing and capacity planning within Snowflake data warehouses. It covers optimizing query performance by adjusting warehouse sizes (Scale UP), simulating high concurrency using multi-threading (Scale OUT), and configuring advanced scaling policies (Standard, Economy, Maximized). Users can benchmark critical queries using SQL and simulate real-world user traffic using Python, ensuring robust and cost-efficient production environments.

This comprehensive checklist guides the deployment of Snowflake data pipelines and integrations into a production environment. It covers critical areas such as authentication best practices, warehouse configuration, data pipeline readiness, access control, monitoring setup, and disaster recovery planning. Use it when preparing for a go-live or validating the stability of your production Snowflake setup.

This guide provides comprehensive instructions on managing performance and concurrency within Snowflake. It covers key concepts like warehouse sizing, multi-cluster configuration, and detecting query queuing issues. Users learn how to right-size resources and implement application-level rate limiting to prevent throttling and optimize data warehouse throughput for complex analytics workloads.

Nextflow is a powerful workflow language and runtime for building reproducible, portable, and scalable data pipelines. It is dominant in bioinformatics, enabling complex dataflow programming and managing workflows across diverse environments, including local machines, HPC clusters (SLURM), and cloud platforms (AWS, Azure). This skill covers the full lifecycle: running established pipelines (like nf-core), developing custom modular workflows, configuring containerization, and ensuring computational reproducibility.