Skill UI

Explores Feishu/Lark native OpenAPI docs when existing lark-* skills or CLI commands lack coverage, fetching markdown indexes stepwise to surface raw API specs and ready-to-run `lark-cli api` invocations.

Assistant for PostgreSQL developers that highlights native features, advanced data types, performance tuning patterns, and extension usage to optimize queries, indexes, and analytics in PostgreSQL projects.

A comprehensive tool for validating, generating, and auditing hreflang tags for multilingual and multi-regional websites. It checks critical SEO requirements, including self-referencing tags, bidirectional return links, proper x-default implementation, and adherence to ISO language/region codes. Use this when optimizing content for global audiences or ensuring Google correctly indexes all language versions.

A comprehensive guide to troubleshooting the most common Algolia API errors, including 400, 403, 404, and 429. This document details the root causes and provides step-by-step fixes for issues related to invalid API keys, rate limits, missing indexes, and data structure validation, ensuring reliable search functionality.

This comprehensive guide provides advanced techniques to diagnose and significantly boost ClickHouse query performance. It covers analyzing slow queries using system tables, optimizing ORDER BY keys, implementing data skipping indexes (Bloom, Set, MinMax), utilizing projections for pre-aggregation, and fine-tuning server settings and materialized views. Essential for high-volume OLAP environments.

This skill automates the complex process of creating formal drawing descriptions and reference numeral indexes for patent applications. It analyzes user-provided technical diagrams (PNG, JPG, SVG, PDF), identifies components, assigns sequential reference numerals according to standard patent rules (e.g., 100-199 for FIG. 1), and generates structured descriptions in both Chinese (CN) and English (US/EP). It also cross-references the generated index against patent claims to ensure completeness.

A comprehensive guide for diagnosing and reducing high memory consumption in Qdrant. This resource is crucial when dealing with reports of 'memory leak', 'out of memory', or excessive RAM growth. It details how Qdrant manages resident memory and OS page cache, and provides advanced techniques such as quantization, using float16/int8 data types, and strategically storing components on disk (like HNSW indexes or sparse vectors) to minimize the memory footprint and ensure stable, scalable operation.

This tool indexes local files or directories into a persistent FTS5 knowledge base. This allows subsequent search operations to efficiently retrieve highly relevant, focused snippets from the project source without needing to re-read the raw files every time. It is crucial for managing and searching large, complex codebases or detailed project documentation.