{"server":"io.github.timescale/pg-aiguide","count":2,"changes":[{"tool":"view_skill","kind":"changed","observed_at":"2026-10-07T23:56:34.227Z","fields":["description"],"description_before":"Retrieve detailed skills for TimescaleDB operations and best practices.\n\n## Available Skills\n\n<available_skills>\n[10\t]{name\tdescription}:\n  design-postgis-tables\tComprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications\n  design-postgres-tables\t\"Use this skill for general PostgreSQL table design.\\n\\n**Trigger when user asks to:**\\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\\n- Choose data types, constraints, or indexes for PostgreSQL\\n- Create user tables, order tables, reference tables, or JSONB schemas\\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\\n- Design update-heavy, upsert-heavy, or OLTP-style tables\\n\\n\\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\\n\\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\\n\"\n  find-hypertable-candidates\t\"Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\\n\\n**Trigger when user asks to:**\\n- Analyze database tables for hypertable conversion potential\\n- Identify time-series or event tables in an existing schema\\n- Evaluate if a table would benefit from Timescale/TimescaleDB\\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\\n- Score or rank tables for hypertable candidacy\\n\\n\\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\\n\\nProvides 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.\\n\"\n  migrate-postgres-tables-to-hypertables\t\"Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\\n\\n**Trigger when user asks to:**\\n- Migrate or convert PostgreSQL tables to hypertables\\n- Execute hypertable migration with minimal downtime\\n- Plan blue-green migration for large tables\\n- Validate hypertable migration success\\n- Configure compression after migration\\n\\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\\n\\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\\n\\nStep-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.\\n\"\n  pgvector-semantic-search\t\"Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\\n\\n**Trigger when user asks to:**\\n- Store or search vector embeddings in PostgreSQL\\n- Set up semantic search, similarity search, or nearest neighbor search\\n- Create HNSW or IVFFlat indexes for vectors\\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\\n- Optimize pgvector performance, recall, or memory usage\\n- Use binary quantization for large vector datasets\\n\\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\\n\\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\\n\"\n  postgres\t\"Use this skill for any PostgreSQL database work — table design, indexing,","description_after":"Retrieve detailed skills for TimescaleDB operations and best practices.\n\n## Available Skills\n\n<available_skills>\n[11\t]{name\tdescription}:\n  design-postgis-tables\tComprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications\n  design-postgres-tables\t\"Use this skill for general PostgreSQL table design.\\n\\n**Trigger when user asks to:**\\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\\n- Choose data types, constraints, or indexes for PostgreSQL\\n- Create user tables, order tables, reference tables, or JSONB schemas\\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\\n- Design update-heavy, upsert-heavy, or OLTP-style tables\\n\\n\\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\\n\\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\\n\"\n  find-hypertable-candidates\t\"Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\\n\\n**Trigger when user asks to:**\\n- Analyze database tables for hypertable conversion potential\\n- Identify time-series or event tables in an existing schema\\n- Evaluate if a table would benefit from Timescale/TimescaleDB\\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\\n- Score or rank tables for hypertable candidacy\\n\\n\\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\\n\\nProvides 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.\\n\"\n  migrate-postgres-tables-to-hypertables\t\"Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\\n\\n**Trigger when user asks to:**\\n- Migrate or convert PostgreSQL tables to hypertables\\n- Execute hypertable migration with minimal downtime\\n- Plan blue-green migration for large tables\\n- Validate hypertable migration success\\n- Configure compression after migration\\n\\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\\n\\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\\n\\nStep-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.\\n\"\n  pgvector-semantic-search\t\"Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\\n\\n**Trigger when user asks to:**\\n- Store or search vector embeddings in PostgreSQL\\n- Set up semantic search, similarity search, or nearest neighbor search\\n- Create HNSW or IVFFlat indexes for vectors\\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\\n- Optimize pgvector performance, recall, or memory usage\\n- Use binary quantization for large vector datasets\\n\\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\\n\\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\\n\"\n  postgres\t\"Use this skill for any PostgreSQL database work — table design, indexing,","description_similarity":0.836,"before_hash":"2325d7c97d096ec17c6eb4ccfc175457","after_hash":"975c336623375558d9a910b62c281f20"},{"tool":"view_skill","kind":"changed","observed_at":"2026-09-25T15:41:18.886Z","fields":["description"],"description_before":"Retrieve detailed skills for TimescaleDB operations and best practices.\n\n## Available Skills\n\n<available_skills>\n[9\t]{name\tdescription}:\n  design-postgis-tables\tComprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications\n  design-postgres-tables\t\"Use this skill for general PostgreSQL table design.\\n\\n**Trigger when user asks to:**\\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\\n- Choose data types, constraints, or indexes for PostgreSQL\\n- Create user tables, order tables, reference tables, or JSONB schemas\\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\\n- Design update-heavy, upsert-heavy, or OLTP-style tables\\n\\n\\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\\n\\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\\n\"\n  find-hypertable-candidates\t\"Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\\n\\n**Trigger when user asks to:**\\n- Analyze database tables for hypertable conversion potential\\n- Identify time-series or event tables in an existing schema\\n- Evaluate if a table would benefit from Timescale/TimescaleDB\\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\\n- Score or rank tables for hypertable candidacy\\n\\n\\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\\n\\nProvides 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.\\n\"\n  migrate-postgres-tables-to-hypertables\t\"Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\\n\\n**Trigger when user asks to:**\\n- Migrate or convert PostgreSQL tables to hypertables\\n- Execute hypertable migration with minimal downtime\\n- Plan blue-green migration for large tables\\n- Validate hypertable migration success\\n- Configure compression after migration\\n\\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\\n\\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\\n\\nStep-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.\\n\"\n  pgvector-semantic-search\t\"Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\\n\\n**Trigger when user asks to:**\\n- Store or search vector embeddings in PostgreSQL\\n- Set up semantic search, similarity search, or nearest neighbor search\\n- Create HNSW or IVFFlat indexes for vectors\\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\\n- Optimize pgvector performance, recall, or memory usage\\n- Use binary quantization for large vector datasets\\n\\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\\n\\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\\n\"\n  postgres\t\"Use this skill for any PostgreSQL database work — table design, indexing, ","description_after":"Retrieve detailed skills for TimescaleDB operations and best practices.\n\n## Available Skills\n\n<available_skills>\n[10\t]{name\tdescription}:\n  design-postgis-tables\tComprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications\n  design-postgres-tables\t\"Use this skill for general PostgreSQL table design.\\n\\n**Trigger when user asks to:**\\n- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.\\n- Choose data types, constraints, or indexes for PostgreSQL\\n- Create user tables, order tables, reference tables, or JSONB schemas\\n- Understand PostgreSQL best practices for normalization, constraints, or indexing\\n- Design update-heavy, upsert-heavy, or OLTP-style tables\\n\\n\\n**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security\\n\\nComprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.\\n\"\n  find-hypertable-candidates\t\"Use this skill to analyze an existing PostgreSQL database and identify which tables should be converted to Timescale/TimescaleDB hypertables.\\n\\n**Trigger when user asks to:**\\n- Analyze database tables for hypertable conversion potential\\n- Identify time-series or event tables in an existing schema\\n- Evaluate if a table would benefit from Timescale/TimescaleDB\\n- Audit PostgreSQL tables for migration to Timescale/TimescaleDB/TigerData\\n- Score or rank tables for hypertable candidacy\\n\\n\\n**Keywords:** hypertable candidate, table analysis, migration assessment, Timescale, TimescaleDB, time-series detection, insert-heavy tables, event logs, audit tables\\n\\nProvides 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.\\n\"\n  migrate-postgres-tables-to-hypertables\t\"Use this skill to migrate identified PostgreSQL tables to Timescale/TimescaleDB hypertables with optimal configuration and validation.\\n\\n**Trigger when user asks to:**\\n- Migrate or convert PostgreSQL tables to hypertables\\n- Execute hypertable migration with minimal downtime\\n- Plan blue-green migration for large tables\\n- Validate hypertable migration success\\n- Configure compression after migration\\n\\n**Prerequisites:** Tables already identified as candidates (use find-hypertable-candidates first if needed)\\n\\n**Keywords:** migrate to hypertable, convert table, Timescale, TimescaleDB, blue-green migration, in-place conversion, create_hypertable, migration validation, compression setup\\n\\nStep-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.\\n\"\n  pgvector-semantic-search\t\"Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search.\\n\\n**Trigger when user asks to:**\\n- Store or search vector embeddings in PostgreSQL\\n- Set up semantic search, similarity search, or nearest neighbor search\\n- Create HNSW or IVFFlat indexes for vectors\\n- Implement RAG (Retrieval Augmented Generation) with PostgreSQL\\n- Optimize pgvector performance, recall, or memory usage\\n- Use binary quantization for large vector datasets\\n\\n**Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search\\n\\nCovers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.\\n\"\n  postgres\t\"Use this skill for any PostgreSQL database work — table design, indexing,","description_similarity":0.886,"before_hash":"ff3911d676f55f9f1735930464175bd0","after_hash":"2325d7c97d096ec17c6eb4ccfc175457"}],"next_before":null,"next_actions":[]}