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Found 155 Skills
Use when you need to optimize database performance, schema design, indexing, and query performance across different database systems
MongoDB and PostgreSQL database administration. Databases: MongoDB (document store, aggregation, Atlas), PostgreSQL (relational, SQL, psql). Capabilities: schema design, query optimization, indexing, migrations, replication, sharding, backup/restore, user management, performance analysis. Actions: design, query, optimize, migrate, backup, restore, index, shard databases. Keywords: MongoDB, PostgreSQL, SQL, NoSQL, BSON, aggregation pipeline, Atlas, psql, pgAdmin, schema design, index, query optimization, EXPLAIN, replication, sharding, backup, restore, migration, ORM, Prisma, Mongoose, connection pooling, transactions, ACID. Use when: designing database schemas, writing complex queries, optimizing query performance, creating indexes, performing migrations, setting up replication, implementing backup strategies, managing database permissions, troubleshooting slow queries.
When the user wants to build an SEO data analysis system, monitor indexing/traffic/keywords/backlinks, or set up benchmarks. Also use when the user mentions "SEO data analysis," "SEO monitoring," "article database," "traffic benchmark," "penalty recovery," "SEO work document," "SEO dashboard," "keyword tracking," "ranking monitoring," "indexing report," or "backlink monitoring."
Architect programmatic SEO systems for 10K-100K+ pages with database-backed data layers, data sufficiency gating, incremental validation, crawl budget management, content enrichment pipelines, and edge delivery. Use when scaling beyond 10K pages, when builds are OOMing, when Google is not indexing all pages, or when the current in-memory architecture has hit its limits.
When the user wants to optimize videos for Google Search, video sitemap, VideoObject schema, or video SEO on websites. Also use when the user mentions "video SEO," "video sitemap," "VideoObject," "video thumbnail," "video indexing," "video preview," "key moments," "Clip schema," or "embedded video optimization."
Semantic search, context management, and document indexing via OpenViking. Use when the user asks to: index/import documents or files into a knowledge base, perform semantic search across indexed content, browse or explore indexed resources, get summaries/overviews of indexed documents, manage an OpenViking instance, or integrate structured context retrieval into workflows. Also use when sub-agents need to retrieve relevant context from a large document collection.
Designs database schemas, indexing strategies, query optimization, and migration patterns for SQL and NoSQL databases. Use when designing tables, optimizing queries, fixing N+1 problems, planning migrations, or when asked about database performance, normalization, ORMs, or data modeling.
Apply when deciding whether and how VTEX IO apps should use Master Data v2 for custom data. Covers entity boundaries, schema lifecycle, indexing strategy, and when Master Data is the right storage mechanism versus another data approach. Use for reviews, wishlists, forms, or other custom data modeling decisions in VTEX IO apps.
Expert Baidu search optimization specialist focused on Chinese search engine ranking, Baidu ecosystem integration, ICP compliance, Chinese keyword research, and mobile-first indexing for the China market.
Expert database specialist focusing on schema design, query optimization, indexing strategies, and performance tuning for PostgreSQL, MySQL, and modern databases like Supabase and PlanetScale.
Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications
Vector search best practices for Azure DocumentDB using `cosmosSearch` — choosing between DiskANN / HNSW / IVF, creating indexes, tuning `lBuild` / `lSearch` / `maxDegree`, Product Quantization (up to 16,000 dims), half-precision (fp16) indexing, and normalizing embeddings for cosine similarity. Use when building RAG / semantic-search applications, creating a vector index, tuning recall/latency, or reducing vector-index memory footprint.