postgresql
Helps your agent design schemas for PostgreSQL. It covers best practices, data types, and optimization for better performance.
What it does
This agent helps design the structure (schema) for a PostgreSQL database. It suggests the best data types, rules (constraints), indexes, and other optimizations to ensure your database performs well and is easy to maintain.
When to use it
- ✓When starting a new project and needing to define how data will be organized.
- ✓When needing to optimize an existing database for better performance.
- ✓When choosing the correct information types for each column (e.g., text, number, date).
Example of what to ask your AI
“I need to design a database schema for an online store. I want to organize customer, product, and order information.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
The agent analyzes your needs, such as the type of information you will store and how you plan to access it. Based on this, it proposes an optimized structure, choosing the most suitable data types and suggesting indexes to speed up searches.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add sickn33/agentic-awesome-skills --skill postgresqlOr download just the file:
curl -L https://raw.githubusercontent.com/sickn33/agentic-awesome-skills/main/plugins/agentic-awesome-skills-claude/skills/postgresql/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
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).
…
FAQ
What are 'data types'?
They are like categories that define what can be stored in a column. Examples: 'text' for names, 'integer' for ages, 'date' for birthdays.
What are 'indexes'?
They are like the index at the back of a book. They help the database find specific information much faster, without needing to read everything.
What is a 'schema'?
It's the blueprint or overall structure of your database, defining which tables exist, what columns each table has, and how they relate to each other.
Details
Original description by the author
Design a PostgreSQL-specific schema. Covers best-practices, data types, indexing, constraints, performance patterns, and advanced features
Popularity
- GitHub stars
- 48k
- Ranking position
- #3860
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
More skills from this repository
00-andruia-consultant
Helps your agent start or improve AI projects. It analyzes code, suggests tools, and creates action plans in Spanish.
sickn33/agentic-awesome-skills
007
Lets your agent perform security audits and threat modeling. It's useful for protecting projects against attacks and vulnerabilities.
sickn33/agentic-awesome-skills
10-andruia-skill-smith
Helps your agent create or improve AI tools. It defines how tools work, writes documentation, and validates the structure.
sickn33/agentic-awesome-skills
20-andruia-niche-intelligence
Lets your agent create a summary about a specific industry. It's useful for understanding the context, rules, usage patterns, and risks of a project, using reliable sources.
sickn33/agentic-awesome-skills
Similar skills
clickhouse-io
Helps your agent optimize queries and schemas in ClickHouse databases. It applies best practices for high-performance analytical workloads. Use when analytical queries are too slow.
affaan-m/ECC
scientific-db-pubmed-database
Helps your agent search for scientific articles in the biomedical field. It's useful for research needing specific medical information.
affaan-m/ECC
scientific-db-uspto-database
Allows the agent to look up official US patent and trademark data. It's useful for intellectual property research.
affaan-m/ECC