Skill UI

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.

Provides a systematic guide for diagnosing and improving poor search quality in vector databases like Qdrant. This guide covers troubleshooting issues such as low recall, when approximate search degrades, selecting optimal embedding models, and correctly tuning HNSW parameters, quantization, and filtering strategies. Essential for optimizing RAG and semantic search pipelines.

This guide covers advanced retrieval strategies that significantly boost search quality beyond basic vector similarity search. Techniques include Hybrid Search (combining dense and sparse indexing), Reranking (improving result order), Relevance Feedback (guiding search with iterative user scores), and Maximal Marginal Relevance (MMR) for diversity. Use these methods when standard search results lack relevance, miss critical keywords, or exhibit poor precision.

This skill allows for the generation and execution of complex, optimized SQL queries to derive deep business insights. It supports sophisticated data workflows, including multi-table joins, multi-dimensional analysis (e.g., trend analysis, attribution modeling, comparative analysis), and subsequent recommendations for appropriate data visualizations (e.g., line charts, waterfall charts). It goes beyond simple SELECT statements by planning the query structure, handling schema relationships, and ensuring adherence to best practices.

A powerful command-line interface (CLI) tool, modeled after kubectl/docker, designed for comprehensive management of GPU compute jobs on the Qizhi platform. It supports job submission (interactive/non-interactive), resource discovery, node availability checking, and batch processing for distributed training and HPC workloads. Ideal for machine learning engineers and data scientists running large-scale scientific computations.

This advanced skill is an end-to-end tool for transforming complex research topics into professional, visualized knowledge graph PDFs. It automatically conducts multi-round web searches, extracts key entities, structures related knowledge, and designs the optimal visualization (supporting Graphviz, PlantUML, etc.). Ideal for deep academic analysis, mapping character relationships, or visualizing complex technical architectures where existing structured data is unavailable.

A comprehensive workflow for scraping and extracting structured data from Google search results using the SerpApi. It captures all key components, including organic listings, knowledge graphs, answer boxes, People Also Ask (PAA), local pack results, and related searches. Ideal for SEO monitoring, market research, competitive analysis, or building search-powered features that require deep, structured data insights.

This workflow utilizes the SerpApi to perform advanced, multi-engine searches across platforms including YouTube, Bing, Google News, Google Shopping, and Google Maps. It is essential for data scraping, SEO research, and building comprehensive search features that require aggregating results from diverse sources beyond standard Google search. Use it to collect videos metadata, product pricing, local business details, and news articles efficiently.

A comprehensive guide for deep debugging complex Snowflake issues. This guide covers advanced techniques including detailed query profiling (spill analysis), diagnosing lock contention and transaction conflicts, analyzing metadata operations, and optimizing partition usage. Use it when standard troubleshooting fails to address slow queries or warehouse performance bottlenecks.

This comprehensive guide provides blueprints for implementing optimal Snowflake data architectures. It covers diverse patterns, including traditional data warehouses, Lakehouse designs using Iceberg tables, decentralized Data Mesh structures, and advanced data sharing and Snowpark native patterns. Use this when designing enterprise data platforms across varying scales.

A comprehensive quick reference guide for diagnosing and resolving common operational and compilation errors encountered when working with Snowflake. It provides actionable solutions for issues like 'Object Does Not Exist', connectivity failures, authentication problems, and SQL syntax mistakes across various programming languages and drivers (Python, Node.js).

A comprehensive guide for executing core Snowflake data loading workflows. This covers setting up stages (S3, GCS, Azure), defining file formats (CSV, JSON, Parquet), and using COPY INTO for bulk loading. It also details advanced continuous ingestion using Snowpipe, enabling real-time data synchronization from cloud storage into Snowflake tables.