DE

design-postgres-tables

Use this guide to design efficient PostgreSQL schemas, select optimal data types, and implement correct indexing strategies.

Install

mkdir -p .claude/skills/design-postgres-tables && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1579" && unzip -o skill.zip -d .claude/skills/design-postgres-tables && rm skill.zip

Installs to .claude/skills/design-postgres-tables

Activation

This is the description your AI agent reads to decide when to run this skill — the better it matches your request, the more reliably it fires.

Use this skill for general PostgreSQL table design.

**Trigger when user asks to:**
- Design PostgreSQL tables, schemas, or data models when creating new tables and when modifying existing ones.
- Choose data types, constraints, or indexes for PostgreSQL
- Create user tables, order tables, reference tables, or JSONB schemas
- Understand PostgreSQL best practices for normalization, constraints, or indexing
- Design update-heavy, upsert-heavy, or OLTP-style tables


**Keywords:** PostgreSQL schema, table design, data types, PRIMARY KEY, FOREIGN KEY, indexes, B-tree, GIN, JSONB, constraints, normalization, identity columns, partitioning, row-level security

Comprehensive reference covering data types, indexing strategies, constraints, JSONB patterns, partitioning, and PostgreSQL-specific best practices.
811 chars · catalog description✓ has a “when” triggerlonger than Claude Code's old 250-char listing cap (fine on current versions)
Intermediate

Key capabilities

  • Implements BIGINT GENERATED ALWAYS AS IDENTITY keys
  • Enforces schema normalization to 3NF
  • Configures GIN/B-tree indexes for specific access paths
  • Adds NOT NULL and DEFAULT constraints
  • Sets up unique constraints with NULLS NOT DISTINCT

How it works

Applies a structured checklist of relational database normalization rules and PostgreSQL-specific optimization patterns.

Inputs & outputs

You give it
Business object requirements or table relationships
You get back
SQL DDL statements for table creation

When to use design-postgres-tables

  • Design normalized table schemas
  • Choose between UUID and identity columns
  • Optimize query performance with GIN/B-tree indexes
  • Implement row-level security

About this skill

PostgreSQL Table Design

Core Rules

  • Define a PRIMARY KEY for reference tables (users, orders, etc.). Not always needed for time-series/event/log data. When used, prefer BIGINT GENERATED ALWAYS AS IDENTITY; use UUID only when global uniqueness/opacity is needed.
  • Normalize first (to 3NF) to eliminate data redundancy and update anomalies; denormalize only for measured, high-ROI reads where join performance is proven problematic. Premature denormalization creates maintenance burden.
  • Add NOT NULL everywhere it’s semantically required; use DEFAULTs for common values.
  • Create indexes for access paths you actually query: PK/unique (auto), FK columns (manual!), frequent filters/sorts, and join keys.
  • Prefer TIMESTAMPTZ for event time; NUMERIC for money; TEXT for strings; BIGINT for integer values, DOUBLE PRECISION for floats (or NUMERIC for exact decimal arithmetic).

PostgreSQL “Gotchas”

  • Identifiers: unquoted → lowercased. Avoid quoted/mixed-case names. Convention: use snake_case for table/column names.
  • Unique + NULLs: UNIQUE allows multiple NULLs. Use UNIQUE (...) NULLS NOT DISTINCT (PG15+) to restrict to one NULL.
  • FK indexes: PostgreSQL does not auto-index FK columns. Add them.
  • No silent coercions: length/precision overflows error out (no truncation). Example: inserting 999 into NUMERIC(2,0) fails with error, unlike some databases that silently truncate or round.
  • Sequences/identity have gaps (normal; don't "fix"). Rollbacks, crashes, and concurrent transactions create gaps in ID sequences (1, 2, 5, 6...). This is expected behavior—don't try to make IDs consecutive.
  • Heap storage: no clustered PK by default (unlike SQL Server/MySQL InnoDB); CLUSTER is one-off reorganization, not maintained on subsequent inserts. Row order on disk is insertion order unless explicitly clustered.
  • MVCC: updates/deletes leave dead tuples; vacuum handles them—design to avoid hot wide-row churn.

Data Types

  • IDs: BIGINT GENERATED ALWAYS AS IDENTITY preferred (GENERATED BY DEFAULT also fine); UUID when merging/federating/used in a distributed system or for opaque IDs. Generate with uuidv7() (preferred if using PG18+) or gen_random_uuid() (if using an older PG version).
  • Integers: prefer BIGINT unless storage space is critical; INTEGER for smaller ranges; avoid SMALLINT unless constrained.
  • Floats: prefer DOUBLE PRECISION over REAL unless storage space is critical. Use NUMERIC for exact decimal arithmetic.
  • Strings: prefer TEXT; if length limits needed, use CHECK (LENGTH(col) <= n) instead of VARCHAR(n); avoid CHAR(n). Use BYTEA for binary data. Large strings/binary (>2KB default threshold) automatically stored in TOAST with compression. TOAST storage: PLAIN (no TOAST), EXTENDED (compress + out-of-line), EXTERNAL (out-of-line, no compress), MAIN (compress, keep in-line if possible). Default EXTENDED usually optimal. Control with ALTER TABLE tbl ALTER COLUMN col SET STORAGE strategy and ALTER TABLE tbl SET (toast_tuple_target = 4096) for threshold. Case-insensitive: for locale/accent handling use non-deterministic collations; for plain ASCII use expression indexes on LOWER(col) (preferred unless column needs case-insensitive PK/FK/UNIQUE) or CITEXT.
  • Money: NUMERIC(p,s) (never float).
  • Time: TIMESTAMPTZ for timestamps; DATE for date-only; INTERVAL for durations. Avoid TIMESTAMP (without timezone). Use now() for transaction start time, clock_timestamp() for current wall-clock time.
  • Booleans: BOOLEAN with NOT NULL constraint unless tri-state values are required.
  • Enums: CREATE TYPE ... AS ENUM for small, stable sets (e.g. US states, days of week). For business-logic-driven and evolving values (e.g. order statuses) → use TEXT (or INT) + CHECK or lookup table.
  • Arrays: TEXT[], INTEGER[], etc. Use for ordered lists where you query elements. Index with GIN for containment (@>, <@) and overlap (&&) queries. Access: arr[1] (1-indexed), arr[1:3] (slicing). Good for tags, categories; avoid for relations—use junction tables instead. Literal syntax: '{val1,val2}' or ARRAY[val1,val2].
  • Range types: daterange, numrange, tstzrange for intervals. Support overlap (&&), containment (@>), operators. Index with GiST. Good for scheduling, versioning, numeric ranges. Pick a bounds scheme and use it consistently; prefer [) (inclusive/exclusive) by default.
  • Network types: INET for IP addresses, CIDR for network ranges, MACADDR for MAC addresses. Support network operators (<<, >>, &&).
  • Geometric types: avoid POINT, LINE, POLYGON, CIRCLE. Index with GiST. Consider PostGIS for spatial features.
  • Text search: TSVECTOR for full-text search documents, TSQUERY for search queries. Index tsvector with GIN. Always specify language: to_tsvector('english', col) and to_tsquery('english', 'query'). Never use single-argument versions. This applies to both index expressions and queries.
  • Domain types: CREATE DOMAIN email AS TEXT CHECK (VALUE ~ '^[^@]+@[^@]+$') for reusable custom types with validation. Enforces constraints across tables.
  • Composite types: CREATE TYPE address AS (street TEXT, city TEXT, zip TEXT) for structured data within columns. Access with (col).field syntax.
  • JSONB: preferred over JSON; index with GIN. Use only for optional/semi-structured attrs. ONLY use JSON if the original ordering of the contents MUST be preserved.
  • Vector types: vector type by pgvector for vector similarity search for embeddings.

Do not use the following data types

  • DO NOT use timestamp (without time zone); DO use timestamptz instead.
  • DO NOT use char(n) or varchar(n); DO use text instead.
  • DO NOT use money type; DO use numeric instead.
  • DO NOT use timetz type; DO use timestamptz instead.
  • DO NOT use timestamptz(0) or any other precision specification; DO use timestamptz instead
  • DO NOT use serial type; DO use generated always as identity instead.
  • DO NOT use POINT, LINE, POLYGON, CIRCLE built-in types, DO use geometry from postgis extension instead.

Table Types

  • Regular: default; fully durable, logged.
  • TEMPORARY: session-scoped, auto-dropped, not logged. Faster for scratch work.
  • UNLOGGED: persistent but not crash-safe. Faster writes; good for caches/staging.

Row-Level Security

Enable with ALTER TABLE tbl ENABLE ROW LEVEL SECURITY. Create policies: CREATE POLICY user_access ON orders FOR SELECT TO app_users USING (user_id = current_user_id()). Built-in user-based access control at the row level.

Constraints

  • PK: implicit UNIQUE + NOT NULL; creates a B-tree index.
  • FK: specify ON DELETE/UPDATE action (CASCADE, RESTRICT, SET NULL, SET DEFAULT). Add explicit index on referencing column—speeds up joins and prevents locking issues on parent deletes/updates. Use DEFERRABLE INITIALLY DEFERRED for circular FK dependencies checked at transaction end.
  • UNIQUE: creates a B-tree index; allows multiple NULLs unless NULLS NOT DISTINCT (PG15+). Standard behavior: (1, NULL) and (1, NULL) are allowed. With NULLS NOT DISTINCT: only one (1, NULL) allowed. Prefer NULLS NOT DISTINCT unless you specifically need duplicate NULLs.
  • CHECK: row-local constraints; NULL values pass the check (three-valued logic). Example: CHECK (price > 0) allows NULL prices. Combine with NOT NULL to enforce: price NUMERIC NOT NULL CHECK (price > 0).
  • EXCLUDE: prevents overlapping values using operators. EXCLUDE USING gist (room_id WITH =, booking_period WITH &&) prevents double-booking rooms. Requires appropriate index type (often GiST).

Indexing

  • B-tree: default for equality/range queries (=, <, >, BETWEEN, ORDER BY)
  • Composite: order matters—index used if equality on leftmost prefix (WHERE a = ? AND b > ? uses index on (a,b), but WHERE b = ? does not). Put most selective/frequently filtered columns first.
  • Covering: CREATE INDEX ON tbl (id) INCLUDE (name, email) - includes non-key columns for index-only scans without visiting table.
  • Partial: for hot subsets (WHERE status = 'active'CREATE INDEX ON tbl (user_id) WHERE status = 'active'). Any query with status = 'active' can use this index.
  • Expression: for computed search keys (CREATE INDEX ON tbl (LOWER(email))). Expression must match exactly in WHERE clause: WHERE LOWER(email) = '[email protected]'.
  • GIN: JSONB containment/existence, arrays (@>, ?), full-text search (@@)
  • GiST: ranges, geometry, exclusion constraints
  • BRIN: very large, naturally ordered data (time-series)—minimal storage overhead. Effective when row order on disk correlates with indexed column (insertion order or after CLUSTER).

Partitioning

  • Use for very large tables (>100M rows) where queries consistently filter on partition key (often time/date).
  • Alternate use: use for tables where data maintenance tasks dictates e.g. data pruned or bulk replaced periodically
  • RANGE: common for time-series (PARTITION BY RANGE (created_at)). Create partitions: CREATE TABLE logs_2024_01 PARTITION OF logs FOR VALUES FROM ('2024-01-01') TO ('2024-02-01'). TimescaleDB automates time-based or ID-based partitioning with retention policies and compression.
  • LIST: for discrete values (PARTITION BY LIST (region)). Example: FOR VALUES IN ('us-east', 'us-west').
  • HASH: for even distribution when no natural key (PARTITION BY HASH (user_id)). Creates N partitions with modulus.
  • Constraint exclusion: requires CHECK constraints on partitions for query planner to prune. Auto-created for declarative partitioning (PG10+).
  • Prefer declarative partitioning or hypertables. Do NOT use table inheritance.
  • **Lim

Content truncated.

When not to use it

  • When working with non-relational NoSQL databases
  • For ad-hoc data exploration without schema requirements

Prerequisites

PostgreSQL installed

Limitations

  • Requires manual review of generated indexes
  • Does not automate complex data migrations

How it compares

Prioritizes database integrity and performance standards over loose, auto-generated schema guesses.

Compared to similar skills

design-postgres-tables side by side with the closest alternatives in the catalog.

SkillInstallsUpdatedSafetyDifficulty
design-postgres-tables (this skill)34moNo flagsIntermediate
sql-optimization-patterns642moNo flagsAdvanced
drizzle-orm322moNo flagsIntermediate
postgres-patterns205moNo flagsIntermediate

Try saying

Example prompts that trigger this skill in your AI assistant.

More by timescale

View all by timescale

pgvector-semantic-search

timescale

Use this skill for setting up vector similarity search with pgvector for AI/ML embeddings, RAG applications, or semantic search. **Trigger when user asks to:** - Store or search vector embeddings in PostgreSQL - Set up semantic search, similarity search, or nearest neighbor search - Create HNSW or IVFFlat indexes for vectors - Implement RAG (Retrieval Augmented Generation) with PostgreSQL - Optimize pgvector performance, recall, or memory usage - Use binary quantization for large vector datasets **Keywords:** pgvector, embeddings, semantic search, vector similarity, HNSW, IVFFlat, halfvec, cosine distance, nearest neighbor, RAG, LLM, AI search Covers: halfvec storage, HNSW index configuration (m, ef_construction, ef_search), quantization strategies, filtered search, bulk loading, and performance tuning.

423

find-hypertable-candidates

timescale

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.

18

migrate-postgres-tables-to-hypertables

timescale

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.

14

postgres-hybrid-text-search

timescale

Use this skill to implement hybrid search combining BM25 keyword search with semantic vector search using Reciprocal Rank Fusion (RRF). **Trigger when user asks to:** - Combine keyword and semantic search - Implement hybrid search or multi-modal retrieval - Use BM25/pg_textsearch with pgvector together - Implement RRF (Reciprocal Rank Fusion) for search - Build search that handles both exact terms and meaning **Keywords:** hybrid search, BM25, pg_textsearch, RRF, reciprocal rank fusion, keyword search, full-text search, reranking, cross-encoder Covers: pg_textsearch BM25 index setup, parallel query patterns, client-side RRF fusion (Python/TypeScript), weighting strategies, and optional ML reranking.

112

setup-timescaledb-hypertables

timescale

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. **Trigger when user asks to:** - Create or design SQL schemas/tables AND Timescale/TimescaleDB/TigerData/Tiger Cloud is available - Set up hypertables, compression, retention policies, or continuous aggregates - Configure partition columns, segment_by, order_by, or chunk intervals - Optimize time-series database performance or storage - Create tables for sensors, metrics, telemetry, events, or transaction logs **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 Step-by-step instructions for hypertable creation, column selection, compression policies, retention, continuous aggregates, and indexes.

05

Search skills

Search the agent skills registry