Loading...
Loading...
Found 139 Skills
Use the JetBrains IDE MCP Server (IntelliJ IDEA 2025.2+) to let an external client drive IDE-backed actions: run Run Configurations, execute commands in the IDE terminal, read/create/edit project files, search via IDE indexes (text/regex), retrieve code inspections for a file, fetch symbol info, perform rename refactoring, list modules/dependencies/repos, open files in the editor, and reformat code. Use when you want IDE-grade indexing/refactoring/inspection instead of raw shell scripting.
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."
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."
When the user wants to optimize for mobile-first indexing or fix mobile usability. Also use when the user mentions "mobile-friendly," "mobile-first indexing," "mobile SEO," "responsive design," "mobile adaptation," "mobile viewport," "viewport meta," "touch targets," "font size mobile," "AMP," or "Accelerated Mobile Pages."
PostgreSQL expert for query optimization, indexing, extensions, and database administration
Use when the user wants embeddings, vector indexing, retrieval, or retrieval-backed answers, including embedding-agent setup, Chroma-backed collections, collection add/query, and KB-to-answer flows.
Use this skill when designing database schemas, optimizing queries, creating indexes, planning migrations, or choosing between database technologies. Triggers on schema design, normalization, indexing strategies, query optimization, EXPLAIN plans, migrations, partitioning, replication, connection pooling, and any task requiring database architecture or performance decisions.
Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations. **Trigger when user asks to:** - Design or modify PostgreSQL tables, schemas, or data models - Choose data types, constraints, indexes, or partitioning strategies - Work with pgvector embeddings, semantic search, or RAG - Set up full-text search, hybrid search, or BM25 ranking - Use PostGIS for spatial/geographic data - Set up TimescaleDB hypertables for time-series data - Migrate tables to hypertables or evaluate migration candidates **Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series
Ingest and normalize market data into OHLCV vectors with HNSW indexing
Refactor Pandas code to improve maintainability, readability, and performance. Identifies and fixes loops/.iterrows() that should be vectorized, overuse of .apply() where vectorized alternatives exist, chained indexing patterns, inplace=True usage, inefficient dtypes, missing method chaining opportunities, complex filters, merge operations without validation, and SettingWithCopyWarning patterns. Applies Pandas 2.0+ features including PyArrow backend, Copy-on-Write, vectorized operations, method chaining, .query()/.eval(), optimized dtypes, and pipeline patterns.
Senior Backend Architect for Convex.dev (2026). Specialized in reactive database design, type-safe full-stack synchronization, and hardened authorization patterns. Expert in building low-latency, real-time applications using Convex v2+ features like RLS (Row Level Security), HTTP Actions, File Storage, and advanced indexing.