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Found 242 Skills
PostgreSQL database specialist for query optimization, schema design, security, and performance. Use PROACTIVELY when writing SQL, creating migrations, designing schemas, or troubleshooting database performance. Incorporates Supabase best practices.
Comprehensive Supabase expert with access to 2,616 official documentation files covering PostgreSQL database, authentication, real-time subscriptions, storage, edge functions, vector embeddings, and all platform features. Invoke when user mentions Supabase, PostgreSQL, database, auth, real-time, storage, edge functions, backend-as-a-service, or pgvector.
Use when user needs PostgreSQL database administration, performance optimization, high availability setup, backup/recovery, or advanced PostgreSQL feature implementation.
Production incident response procedures for Python/React applications. Use when responding to production outages, investigating error spikes, diagnosing performance degradation, or conducting post-mortems. Covers severity classification (SEV1-SEV4), incident commander role, communication templates, diagnostic commands for FastAPI/ PostgreSQL/Redis, rollback procedures, and blameless post-mortem process. Does NOT cover monitoring setup (use monitoring-setup) or deployment procedures (use deployment-pipeline).
JOOQ type-safe SQL patterns - use for database queries, repositories, complex SQL operations, and PostgreSQL-specific features
Python full-stack with FastAPI, React, PostgreSQL, and Docker.
Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables. **Trigger when user asks to:** - Analyze database tables for hypertable conversion potential - Identify time-series or event tables in an existing schema - Evaluate if a table would benefit from Timescale/TimescaleDB - Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData - Score or rank tables for hypertable candidacy **Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables Provides SQL queries to analyze table statistics, index patterns, and query patterns. Includes scoring criteria (8+ points = good candidate) and pattern recognition for IoT, events, transactions, and sequential data.
Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation. **Trigger when user asks to:** - Migrate or convert PostgreSQL tables to hypertables - Execute hypertable migration with minimal downtime - Plan blue-green migration for large tables - Validate hypertable migration success - Configure compression after migration **Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed) **Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup Step-by-step migration planning including: partition column selection, chunk interval calculation, PK/constraint handling, migration execution (in-place vs blue-green), and performance validation queries.
Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads.
pg-boss expert for PostgreSQL-backed job queues with exactly-once delivery, perfect for applications already using Postgres (Supabase, Neon, etc.). Use when "pg-boss, postgres queue, postgresql job, supabase background job, neon job queue, postgres scheduling, database job queue, pg-boss, postgresql, job-queue, background-jobs, supabase, neon, exactly-once, scheduling" mentioned.
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
Supabase PostgreSQL backend-as-a-service with realtime. Use for serverless PostgreSQL.