Loading...
Loading...
Found 206 Skills
Review SQL and query code for injection risk, parameterization, indexing and performance, transactions, NULL and constraints, and dialect portability. Language-only atomic skill; output is a findings list.
Check Meilisearch index status, tasks, health, and settings. Use for debugging search issues, monitoring indexing tasks, and inspecting index configuration. Read-only admin operations.
Use this skill when designing backend systems, databases, APIs, or services. Triggers on schema design, database migrations, indexing strategies, distributed systems architecture, microservices, caching, message queues, observability setup, logging, metrics, tracing, SLO/SLI definition, performance optimization, query tuning, security hardening, authentication, authorization, API design (REST, GraphQL, gRPC), rate limiting, pagination, and failure handling patterns. Acts as a senior backend engineering advisor for mid-level engineers leveling up.
Vector search indexing and querying workflows using MCP Vector Search, including setup, reindexing, auto-index strategies, and MCP integration.
Elasticsearch development best practices for indexing, querying, and search optimization
Complete guide for OpenAI's Assistants API v2: stateful conversational AI with built-in tools (Code Interpreter, File Search, Function Calling), vector stores for RAG (up to 10,000 files), thread/run lifecycle management, and streaming patterns. Both Node.js SDK and fetch approaches. ⚠️ DEPRECATION NOTICE: OpenAI plans to sunset Assistants API in H1 2026 in favor of Responses API. This skill remains valuable for existing apps and migration planning. Use when: building stateful chatbots with OpenAI, implementing RAG with vector stores, executing Python code with Code Interpreter, using file search for document Q&A, managing conversation threads, streaming assistant responses, or encountering errors like "thread already has active run", vector store indexing delays, run polling timeouts, or file upload issues. Keywords: openai assistants, assistants api, openai threads, openai runs, code interpreter assistant, file search openai, vector store openai, openai rag, assistant streaming, thread persistence, stateful chatbot, thread already has active run, run status polling, vector store error
PostgreSQL-based semantic and hybrid search with pgvector and ParadeDB. Use when implementing vector search, semantic search, hybrid search, or full-text search in PostgreSQL. Covers pgvector setup, indexing (HNSW, IVFFlat), hybrid search (FTS + BM25 + RRF), ParadeDB as Elasticsearch alternative, and re-ranking with Cohere/cross-encoders. Supports vector(1536) and halfvec(3072) types for OpenAI embeddings. Triggers: pgvector, vector search, semantic search, hybrid search, embedding search, PostgreSQL RAG, BM25, RRF, HNSW index, similarity search, ParadeDB, pg_search, reranking, Cohere rerank, pg_trgm, trigram, fuzzy search, LIKE, ILIKE, autocomplete, typo tolerance, fuzzystrmatch
Database design specialist for schema modeling, query optimization, indexing strategies, and data integrityUse when "database design, schema, indexes, query optimization, migrations, normalization, database scaling, foreign keys, data modeling, database, sql, postgres, mysql, mongodb, schema, indexes, migrations, normalization, optimization" mentioned.
Comprehensive PostgreSQL database engineering skill covering indexing strategies, query optimization, performance tuning, partitioning, replication, backup and recovery, high availability, and production database management. Master advanced PostgreSQL features including MVCC, VACUUM operations, connection pooling, monitoring, and scalability patterns.
Implement persistence layers with Spring Data JPA. Use when creating repositories, configuring entity relationships, writing queries (derived and @Query), setting up pagination, database auditing, transactions, UUID primary keys, multiple databases, and database indexing. Covers repository interfaces, JPA entities, custom queries, relationships, and performance optimization patterns.
Query Google Search Console for SEO data - search queries, top pages, CTR opportunities, URL inspection, and sitemaps. Use when analyzing search performance, finding optimization opportunities, or checking indexing status.
Automatically identifies prompt type, saves to corresponding category (technical/content/teaching/product/general), and updates index. Use when user says save prompt, record, or organize prompt. Supports 5 major classifications with automatic file naming and indexing.