postgresql
Provides best-practice guidance for PostgreSQL schema design, indexing, and advanced features.
Install
mkdir -p .claude/skills/postgresql && curl -L -o skill.zip "https://agentskills.codes/api/skills/download/1068" && unzip -o skill.zip -d .claude/skills/postgresql && rm skill.zipInstalls to .claude/skills/postgresql
Activation
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Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced featuresKey capabilities
- →Models complex relational schemas in PostgreSQL
- →Selects optimal data types for storage efficiency
- →Implements indexing strategies for query paths
- →Designs constraints for data integrity
- →Plans RLS and partitioning for scaling
How it works
It applies relational database normalization and indexing rules specifically to the PostgreSQL feature set, emphasizing data integrity invariants.
Inputs & outputs
When to use postgresql
- →Designing complex relational schemas
- →Optimizing query performance through indexing
- →Defining data constraints and integrity rules
About this skill
PostgreSQL Table Design
Use this skill when
- Designing a schema for PostgreSQL
- Selecting data types and constraints
- Planning indexes, partitions, or RLS policies
- Reviewing tables for scale and maintainability
Do not use this skill when
- You are targeting a non-PostgreSQL database
- You only need query tuning without schema changes
- You require a DB-agnostic modeling guide
Instructions
- Capture entities, access patterns, and scale targets (rows, QPS, retention).
- Choose data types and constraints that enforce invariants.
- Add indexes for real query paths and validate with
EXPLAIN. - Plan partitioning or RLS where required by scale or access control.
- Review migration impact and apply changes safely.
Safety
- Avoid destructive DDL on production without backups and a rollback plan.
- Use migrations and staging validation before applying schema changes.
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; useUUIDonly 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
NUMERICfor exact decimal arithmetic).
PostgreSQL “Gotchas”
- Identifiers: unquoted → lowercased. Avoid quoted/mixed-case names. Convention: use
snake_casefor 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);
CLUSTERis 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 IDENTITYpreferred (GENERATED BY DEFAULTalso fine);UUIDwhen merging/federating/used in a distributed system or for opaque IDs. Generate withuuidv7()(preferred if using PG18+) orgen_random_uuid()(if using an older PG version). - Integers: prefer
BIGINTunless storage space is critical;INTEGERfor smaller ranges; avoidSMALLINTunless constrained. - Floats: prefer
DOUBLE PRECISIONoverREALunless storage space is critical. UseNUMERICfor exact decimal arithmetic. - Strings: prefer
TEXT; if length limits needed, useCHECK (LENGTH(col) <= n)instead ofVARCHAR(n); avoidCHAR(n). UseBYTEAfor 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). DefaultEXTENDEDusually optimal. Control withALTER TABLE tbl ALTER COLUMN col SET STORAGE strategyandALTER 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 onLOWER(col)(preferred unless column needs case-insensitive PK/FK/UNIQUE) orCITEXT. - Money:
NUMERIC(p,s)(never float). - Time:
TIMESTAMPTZfor timestamps;DATEfor date-only;INTERVALfor durations. AvoidTIMESTAMP(without timezone). Usenow()for transaction start time,clock_timestamp()for current wall-clock time. - Booleans:
BOOLEANwithNOT NULLconstraint unless tri-state values are required. - Enums:
CREATE TYPE ... AS ENUMfor 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}'orARRAY[val1,val2]. - Range types:
daterange,numrange,tstzrangefor 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:
INETfor IP addresses,CIDRfor network ranges,MACADDRfor MAC addresses. Support network operators (<<,>>,&&). - Geometric types:
POINT,LINE,POLYGON,CIRCLEfor 2D spatial data. Index with GiST. Consider PostGIS for advanced spatial features. - Text search:
TSVECTORfor full-text search documents,TSQUERYfor search queries. Indextsvectorwith GIN. Always specify language:to_tsvector('english', col)andto_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).fieldsyntax. - 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:
vectortype bypgvectorfor vector similarity search for embeddings.
Do not use the following data types
- DO NOT use
timestamp(without time zone); DO usetimestamptzinstead. - DO NOT use
char(n)orvarchar(n); DO usetextinstead. - DO NOT use
moneytype; DO usenumericinstead. - DO NOT use
timetztype; DO usetimestamptzinstead. - DO NOT use
timestamptz(0)or any other precision specification; DO usetimestamptzinstead - DO NOT use
serialtype; DO usegenerated always as identityinstead.
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/UPDATEaction (CASCADE,RESTRICT,SET NULL,SET DEFAULT). Add explicit index on referencing column—speeds up joins and prevents locking issues on parent deletes/updates. UseDEFERRABLE INITIALLY DEFERREDfor 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. WithNULLS NOT DISTINCT: only one(1, NULL)allowed. PreferNULLS NOT DISTINCTunless 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 withNOT NULLto 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), butWHERE 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 withstatus = '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 bul
Content truncated.
When not to use it
- →Database systems other than PostgreSQL
- →Simple query tuning without schema modifications
Limitations
- →Destructive DDL changes require manual production verification
- →Schema-first approach is slower for ad-hoc query analysis
How it compares
It focuses on schema-level architecture design rather than general query optimization.
Compared to similar skills
postgresql side by side with the closest alternatives in the catalog.
| Skill | Installs | Updated | Safety | Difficulty |
|---|---|---|---|---|
| postgresql (this skill) | 9 | 5mo | No flags | Advanced |
| drizzle-orm | 32 | 2mo | No flags | Intermediate |
| event-store-design | 5 | 2mo | No flags | Advanced |
| backend-development | 17 | 4mo | No flags | Intermediate |
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Example prompts that trigger this skill in your AI assistant.
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