pg-aiguide

Search documentation with hybrid semantic (vector) and keyword (BM25) search. Use semanticWeight to choose keyword-only (0), semantic-only (1), or a blend; mid values fuse rankings with RRF. Supports Tiger Cloud (TimescaleDB), PostgreSQL, and PostGIS.

{
  "type": "object",
  "properties": {
    "source": {
      "type": "string",
      "enum": [
        "tiger",
        "postgres_14",
        "postgres_15",
        "postgres_16",
        "postgres_17",
        "postgres_18",
        "postgis_3.3",
        "postgis_3.4",
        "postgis_3.5",
        "postgis_3.6"
      ],
      "description": "The documentation source to search. \"tiger\" for Tiger Cloud and TimescaleDB, \"postgres\" for PostgreSQL, \"postgis\" for PostGIS spatial extension. Specific versions provided with _X.X suffixes."
    },
    "query": {
      "type": "string",
      "description": "The search query. Used for BM25 when keyword or hybrid search applies, and for the embedding when semantic or hybrid search applies."
    },
    "limit": {
      "anyOf": [
        {
          "type": "integer",
          "minimum": -9007199254740991,
          "maximum": 9007199254740991
        },
        {
          "type": "null"
        }
      ],
      "description": "The maximum number of matches to return. Defaults to 20."
    },
    "semanticWeight": {
      "anyOf": [
        {
          "type": "number",
          "minimum": 0,
          "maximum": 1,
          "multipleOf": 0.1
        },
        {
          "type": "null"
        }
      ],
      "description": "Controls the balance between semantic and keyword search. 0 = keyword only, 0.5 = equal mix, 1 = semantic only. Default is 0.7 (favor semantic search)."
    }
  },
  "required": [
    "source",
    "query",
    "limit",
    "semanticWeight"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#"
}

{
  "type": "object",
  "properties": {
    "results": {
      "type": "array",
      "items": {
        "anyOf": [
          {
            "type": "object",
            "properties": {
              "id": {
                "type": "integer",
                "minimum": -9007199254740991,
                "maximum": 9007199254740991,
                "description": "The unique identifier of the documentation entry."
              },
              "content": {
                "type": "string",
                "description": "The content of the documentation entry."
              },
              "metadata": {
                "type": "string",
                "description": "Additional metadata about the documentation entry, as a JSON encoded string."
              },
              "distance": {
                "type": "number",
                "description": "The distance score indicating the relevance of the entry to the query. Lower values indicate higher relevance."
              }
            },
            "required": [
              "id",
              "content",
              "metadata",
              "distance"
            ],
            "additionalProperties": false
          },
          {
            "type": "object",
            "properties": {
              "id": {
                "type": "integer",
                "minimum": -9007199254740991,
                "maximum": 9007199254740991,
                "description": "The unique identifier of the documentation entry."
              },
              "content": {
                "type": "string",
                "description": "The content of the documentation entry."
              },
              "metadata": {
                "type": "string",
                "description": "Additional metadata about the documentation entry, as a JSON encoded string."
              },
              "score": {
                "type": "number",
                "description": "The score indicating the relevance of the entry to the keywords. Higher values indicate higher relevance."
              }
            },
            "required": [
              "id",
              "content",
              "metadata",
              "score"
            ],
            "additionalProperties": false
          },
          {
            "type": "object",
            "properties": {
              "id": {
                "type": "integer",
                "minimum": -9007199254740991,
                "maximum": 9007199254740991,
                "description": "The unique identifier of the documentation entry."
              },
              "content": {
                "type": "string",
                "description": "The content of the documentation entry."
              },
              "metadata": {
                "type": "string",
                "description": "Additional metadata about the documentation entry, as a JSON encoded string."
              },
              "rrf_score": {
                "type": "number",
                "description": "Hybrid search: fused RRF score from combining semantic and keyword result rankings."
              }
            },
            "required": [
              "id",
              "content",
              "metadata",
              "rrf_score"
            ],
            "additionalProperties": false
          }
        ]
      }
    }
  },
  "required": [
    "results"
  ],
  "$schema": "http://json-schema.org/draft-07/schema#",
  "additionalProperties": false
}

Retrieve detailed skills for TimescaleDB operations and best practices. ## Available Skills <available_skills> [10 ]{name description}: design-postgis-tables Comprehensive PostGIS spatial table design reference covering geometry types, coordinate systems, spatial indexing, and performance patterns for location-based applications design-postgres-tables "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" find-hypertable-candidates "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" migrate-postgres-tables-to-hypertables "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" pgvector-semantic-search "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" postgres "Use this skill for any PostgreSQL database work — table design, indexing, data types, constraints, extensions (pgvector, PostGIS, TimescaleDB), search, and migrations.\n\n**Trigger when user asks to:**\n- Explore an existing PostgreSQL database to understand its objects and relationships\n- Design or modify PostgreSQL tables, schemas, or data models\n- Choose data types, constraints, indexes, or partitioning strategies\n- Work with pgvector embeddings, semantic search, or RAG\n- Set up full-text search, hybrid search, or BM25 ranking\n- Use PostGIS for spatial/geographic data\n- Set up TimescaleDB hypertables for time-series data\n- Migrate tables to hypertables or evaluate migration candidates\n- Plan or execute safe schema migrations with zero downtime\n\n**Keywords:** PostgreSQL, Postgres, SQL, schema, table design, indexes, constraints, pgvector, PostGIS, TimescaleDB, hypertable, semantic search, hybrid search, BM25, time-series, migration\n" postgres-database-migration "Use this skill for planning, testing, and safely executing PostgreSQL schema migrations — especially when working with production data or shared databases.\n\n**Trigger when user asks to:**\n- Test a schema migration before applying it to production\n- Add, remove, or rename columns safely on a live table\n- Change a column's data type without downtime\n- Add or drop indexes, constraints, or foreign keys on large tables\n- Understand which ALTER TABLE operations lock the table\n- Roll back a failed migration\n- Plan a zero-downtime migration strategy\n- Fork a database to test a migration safely\n\n**Keywords:** migration, schema change, ALTER TABLE, add column, drop column, rename column, change type, zero downtime, lock, AccessExclusiveLock, concurrent index, forking, rollback, backfill, deploy\n\nCovers: lock-level reference for every common DDL operation, safe migration patterns, fork-based testing, zero-downtime column changes, index creation, constraint addition, backfill strategies, pre/post-migration validation, and rollback planning.\n" postgres-hybrid-text-search "Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF).\n\n**Trigger when user asks to:**\n- Combine keyword and semantic search\n- Implement hybrid search or multi-modal retrieval\n- Use BM25/pg_textsearch with pgvector together\n- Implement RRF (Reciprocal Rank Fusion) for search\n- Build search that handles both exact terms and meaning\n\n\n**Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder\n\nCovers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.\n" schema-exploration "Explore an existing PostgreSQL database before answering questions about its data or writing SQL. Use this skill whenever a user asks for a query or a data-backed answer against an unfamiliar schema (counts, missing or failed records, recent changes), asks where a business concept lives, or asks how tables, joins, views, routines, triggers, RLS, or extensions work. Find the relevant objects with read-only pg_catalog queries, then request approval before inspecting data-derived statistics or rows. Not a schema-design or migration guide.\n" setup-timescaledb-hypertables "Use this skill when creating database schemas or tables for Timescale, TimescaleDB, TigerData, or Tiger Cloud, especially for time-series, IoT, metrics, events, or log data. Use this to improve the performance of any insert-heavy table.\n\n**Trigger when user asks to:**\n- Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available\n- Set up hypertables, compression, retention policies, or continuous aggregates\n- Configure partition columns, segment_by, order_by, or chunk intervals\n- Optimize time-series database performance or storage\n- Create tables for sensors, metrics, telemetry, events, or transaction logs\n\n**Keywords:** CREATE TABLE, hypertable, Timescale, TimescaleDB, time-series, IoT, metrics, sensor data, compression policy, continuous aggregates, columnstore, retention policy, chunk interval, segment_by, order_by\n\nStep-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.\n" </available_skills>

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    "path": {
      "type": "string",
      "description": "A relative path to a file or directory within the skill to view.\nIf empty, will view the `SKILL.md` file by default.\nUse `.` to list the root directory of the skill."
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  "$schema": "http://json-schema.org/draft-07/schema#"
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