Skills postgresql-table-design
📦
postgresql-table-design
Content revision r1 Safe ⚙️ External commands
Design PostgreSQL Tables
PostgreSQL schemas can fail when data types, constraints, and indexes are chosen without workload context. This skill guides table design with PostgreSQL-specific rules for durable, performant, and maintainable schemas.
Supports: Claude Codex Code(CC)
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Install with my Agent
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Agent request
Review the Skillstore skill "postgresql-table-design" from https://skillstore.io/skills/wshobson-postgresql-table-design.md and its manifest at https://skillstore.io/api/skills/wshobson-postgresql-table-design/manifest. Verify the artifact. You may proceed after verification, subject to the environment's own policy.Your Agent should still show its plan and request any confirmation required by the security policy.
Agent-readable resources
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Test it
Using "postgresql-table-design". A user and order feature needs durable PostgreSQL tables with searchable email and order history.
Expected outcome:
- A schema outline with separate user and order tables, identity keys, required timestamps, and checked order status values.
- Index recommendations for email lookup, order ownership, and created date filtering.
- Notes explaining why numeric money fields and timestamptz event fields are preferred.
Using "postgresql-table-design". A product profile table stores optional settings and searchable attributes in JSONB.
Expected outcome:
- Guidance to keep core relational fields in columns and reserve JSONB for optional attributes.
- Recommendations for GIN indexing, generated columns for scalar searches, and constraints for valid JSONB shape.
Using "postgresql-table-design". A telemetry table will receive millions of rows per day and most queries filter by time.
Expected outcome:
- A time-based partitioning plan with minimal indexes for insert performance.
- Advice to consider BRIN indexes, bulk loading patterns, and rebuildable staging tables.
Security Audit
Safev8 • 7/8/2026 Open versioned report
All static findings are false positives from Markdown backticks, fenced SQL examples, and PostgreSQL terminology in SKILL.md. No prompt injection, credential handling, network access, filesystem access, or executable command behavior was found.
1
Files scanned
205
Lines analyzed
0
Review items
0
False positives ignored
Risk Factors
⚙️ External commands (84)
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No confirmed security findings were detected by the latest completed static and semantic audit. This does not prove the skill has no side effects.
Audited by: codex View Audit History →
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APA citation
wshobson. (2026). postgresql-table-design security audit report (audit version 8) [Author version unspecified]. Skillstore. https://skillstore.io/skills/wshobson-postgresql-table-design/audits/8BibTeX citation
@techreport{wshobson-wshobson-postgresql-table-design-2026,
author = {wshobson},
title = {postgresql-table-design security audit report (audit version 8)},
institution = {Skillstore},
year = {2026},
number = {8},
url = {https://skillstore.io/skills/wshobson-postgresql-table-design/audits/8},
note = {Author version unspecified}
}CITATION.cff
cff-version: 1.2.0
message: "If you use this Skill, cite its author and this versioned security audit report."
title: "postgresql-table-design security audit report (audit version 8)"
version: "unspecified"
type: report
authors:
- name: "wshobson"
date-released: "2026-07-08"
url: "https://skillstore.io/skills/wshobson-postgresql-table-design/audits/8"
identifiers:
- type: other
value: "skillstore:wshobson-postgresql-table-design:audit:8"
description: "Skillstore immutable audit report identifier"
Skillstore Score
Why this score Evidence Confidence: High 55
Architecture
85
Maintainability
87
Content
70
Community
83
Spec Compliance
What You Can Build
Plan a New Product Schema
Create table structures with suitable keys, constraints, timestamps, and indexes before implementation begins.
Review Database Design Choices
Check an existing design for weak data types, missing foreign key indexes, JSONB misuse, or unsafe defaults.
Model High-Volume Tables
Choose partitioning, BRIN indexes, reduced indexes, and staging patterns for large or insert-heavy PostgreSQL workloads.
Try These Prompts
Design Basic Tables
Design PostgreSQL tables for this feature: [describe feature]. Include keys, required columns, data types, constraints, and basic indexes.
Review Existing Schema
Review this PostgreSQL schema for data type choices, constraints, foreign key indexes, and naming issues: [paste schema].
Optimize for Workload
Given these PostgreSQL tables and query patterns, recommend indexes, partitioning, and schema changes. Explain each tradeoff clearly.
Plan Migration Strategy
Create a PostgreSQL schema evolution plan for these changes: [describe changes]. Include lock risks, concurrent index needs, and rollback considerations.
Best Practices
- Start with normalized tables, then denormalize only after measured query pressure justifies the added maintenance cost.
- Add indexes for proven access paths, including foreign key columns, frequent filters, sort keys, and joins.
- Use PostgreSQL-specific types and constraints to enforce meaning close to the data.
Avoid
- Using JSONB for core relational data that needs constraints, joins, and stable query patterns.
- Adding broad indexes before query patterns are known, especially on insert-heavy tables.
- Choosing legacy or weak types such as serial, timestamp without time zone, money, or fixed char columns.
Frequently Asked Questions
Can this skill generate a full PostgreSQL schema?
Yes. Provide the feature, entities, relationships, and query patterns for the best result.
Does it explain why each design choice matters?
Yes. It focuses on tradeoffs for data types, constraints, indexing, partitioning, and schema evolution.
Can it review an existing schema?
Yes. Paste the schema and workload context so it can identify PostgreSQL-specific risks and improvements.
Does it support JSONB design decisions?
Yes. It covers when to use JSONB, how to index it, and when columns are a better fit.
Can it validate performance in production?
No. It provides design guidance, but real performance still needs measurements, query plans, and workload testing.
Does it work outside PostgreSQL?
It is PostgreSQL-specific. Some ideas are general, but many recommendations rely on PostgreSQL behavior.