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Found 206 Skills
Ingest and normalize market data into OHLCV vectors with HNSW indexing
MySQL and MariaDB schema, query, indexing, transaction, replication, and connection-pool patterns for production backends.
OpenSearch development best practices for indexing, querying, search optimization, vector search, and cluster management
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications
Comprehensive skill for the `kb` CLI and the Karpathy Knowledge Base pattern. Covers the full KB lifecycle — topic scaffolding, multi-source ingestion (URLs, files, YouTube, bookmarks, codebases), wiki article compilation, cross-article querying with file-back, lint-and-heal passes, QMD indexing, and hybrid search. Also covers codebase-specific analysis via inspect commands for complexity, coupling, blast radius, dead code, circular dependencies, symbol/file lookups, backlinks, and code smells. Use when working with kb CLI commands, knowledge base workflows, code vault generation, code graph analysis, code metrics inspection, wiki compilation, or the ingest-compile-query-lint cycle. Do not use for general code review, linting, formatting, building Go projects, or writing application code.
Design and optimize database schemas for SQL and NoSQL databases. Use when creating new databases, designing tables, defining relationships, indexing strategies, or database migrations. Handles PostgreSQL, MySQL, MongoDB, normalization, and performance optimization.
Design robust, scalable database schemas for SQL and NoSQL databases. Provides normalization guidelines, indexing strategies, migration patterns, constraint design, and performance optimization. Ensures data integrity, query performance, and maintainable data models.
When the user wants to audit, review, or diagnose SEO issues on their site. Uses live web data via the Bright Data CLI for accurate detection of JS-injected schema, hreflang, canonicals, and live SERP-based ranking checks. Also use when the user mentions "SEO audit," "technical SEO," "why am I not ranking," "SEO issues," "on-page SEO," "meta tags review," "SEO health check," "my traffic dropped," "lost rankings," "not showing up in Google," "site isn't ranking," "Google update hit me," "page speed," "core web vitals," "crawl errors," or "indexing issues." Use this even if the user just says something vague like "my SEO is bad" or "help with SEO" — start with an audit. For building pages at scale to target keywords, see programmatic-seo. For implementing structured data, see schema-markup. For AI search optimization, see ai-seo.
Use this skill when the user asks about Goldsky Mirror pipelines — creating, deploying, operating, or troubleshooting Mirror. Triggers on: 'Mirror pipeline', 'goldsky pipeline apply', 'sync subgraph to database', 'mirror vs turbo', 'direct indexing', 'mirror pipeline YAML', 'mirror pipeline pause/stop/restart'. Also use this skill when the user wants to sync a Goldsky subgraph into a database or message queue — Mirror is the only pipeline product that supports subgraph sources. For new pipelines that don't need a subgraph source, the turbo-builder skill is usually a better fit. Do NOT trigger on 'goldsky turbo' commands or generic 'build a pipeline' requests without subgraph context — those belong to the turbo skills.
bkend.ai database expert skill. Covers table creation, CRUD operations, 7 column types, constraints, filtering (AND/OR, 8 operators), sorting, pagination, relations, joins, indexing, and schema management via MCP and REST API. Triggers: table, column, CRUD, schema, index, filter, query, data model, 테이블, 컬럼, 스키마, 인덱스, 필터, 쿼리, 데이터 모델, テーブル, カラム, スキーマ, インデックス, フィルター, 数据表, 列, 模式, 索引, 过滤, 查询, tabla, columna, esquema, indice, filtro, consulta, tableau, colonne, schema, index, filtre, requete, Tabelle, Spalte, Schema, Index, Filter, Abfrage, tabella, colonna, schema, indice, filtro, query Do NOT use for: authentication (use bkend-auth), file storage (use bkend-storage), platform management (use bkend-quickstart).
Build modular Agentic RAG systems with LangGraph, featuring hierarchical indexing, conversation memory, and multi-agent query processing
Azure AI Search SDK for Python. Use for vector search, hybrid search, semantic ranking, indexing, and skillsets. Triggers: "azure-search-documents", "SearchClient", "SearchIndexClient", "vector search", "hybrid search", "semantic search".