PostgreSQL best practices for indexing, schema design, query optimization, and security.
Works with
Covers six index types with specific use cases: B-tree for equality/range queries, composite for multi-column filters, GIN for JSONB and full-text search, and BRIN for time-series data
Includes data type guidance (bigint for IDs, text over varchar, timestamptz for timestamps, numeric for money) and anti-pattern detection queries for unindexed foreign keys and slow queries
Provides ready-to-use SQ
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionpostgres-patternsExecute the skills CLI command in your project's root directory to begin installation:
Fetches postgres-patterns from affaan-m/everything-claude-code and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate postgres-patterns. Access via /postgres-patterns in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Quick reference for PostgreSQL best practices. For detailed guidance, use the database-reviewer agent.
| Query Pattern | Index Type | Example |
|---|---|---|
WHERE col = value |
B-tree (default) | CREATE INDEX idx ON t (col) |
WHERE col > value |
B-tree | CREATE INDEX idx ON t (col) |
WHERE a = x AND b > y |
Composite | CREATE INDEX idx ON t (a, b) |
WHERE jsonb @> '{}' |
GIN | CREATE INDEX idx ON t USING gin (col) |
WHERE tsv @@ query |
GIN | CREATE INDEX idx ON t USING gin (col) |
| Time-series ranges | BRIN | CREATE INDEX idx ON t USING brin (col) |
| Use Case | Correct Type | Avoid |
|---|---|---|
| IDs | bigint |
int, random UUID |
| Strings | text |
varchar(255) |
| Timestamps | timestamptz |
timestamp |
| Money | numeric(10,2) |
float |
| Flags | boolean |
varchar, int |
Composite Index Order:
-- Equality columns first, then range columns
CREATE INDEX idx ON orders (status, created_at);
-- Works for: WHERE status = 'pending' AND created_at > '2024-01-01'
Covering Index:
CREATE INDEX idx ON users (email) INCLUDE (name, created_at);
-- Avoids table lookup for SELECT email, name, created_at
Partial Index:
CREATE INDEX idx ON users (email) WHERE deleted_at IS NULL;
-- Smaller index, only includes active users
RLS Policy (Optimized):
CREATE POLICY policy ON orders
USING ((SELECT auth.uid()) = user_id); -- Wrap in SELECT!
UPSERT:
INSERT INTO settings (user_id, key, value)
VALUES (123, 'theme', 'dark')
ON CONFLICT (user_id, key)
DO UPDATE SET value = EXCLUDED.value;
Cursor Pagination:
SELECT * FROM products WHERE id > $last_id ORDER BY id LIMIT 20;
-- O(1) vs OFFSET which is O(n)
Queue Processing:
UPDATE jobs SET status = 'processing'
WHERE id = (
SELECT id FROM jobs WHERE status = 'pending'
ORDER BY created_at LIMIT 1
FOR UPDATE SKIP LOCKED
) RETURNING *;
-- Find unindexed foreign keys
SELECT conrelid::regclass, a.attname
FROM pg_constraint c
JOIN pg_attribute a ON a.attrelid = c.conrelid AND a.attnum = ANY(c.conkey)
WHERE c.contype = 'f'
AND NOT EXISTS (
SELECT 1 FROM pg_index i
WHERE i.indrelid = c.conrelid AND a.attnum = ANY(i.indkey)
);
-- Find slow queries
SELECT query, mean_exec_time, calls
FROM pg_stat_statements
WHERE mean_exec_time > 100
ORDER BY mean_exec_time DESC;
-- Check table bloat
SELECT relname, n_dead_tup, last_vacuum
FROM pg_stat_user_tables
WHERE n_dead_tup > 1000
ORDER BY n_dead_tup DESC;
-- Connection limits (adjust for RAM)
ALTER SYSTEM SET max_connections = 100;
ALTER SYSTEM SET work_mem = '8MB';
-- Timeouts
ALTER SYSTEM SET idle_in_transaction_session_timeout = '30s';
ALTER SYSTEM SET statement_timeout = '30s';
-- Monitoring
CREATE EXTENSION IF NOT EXISTS pg_stat_statements;
-- Security defaults
REVOKE ALL ON SCHEMA public FROM public;
SELECT pg_reload_conf();
database-reviewer - Full database review workflowclickhouse-io - ClickHouse analytics patternsbackend-patterns - API and backend patternsBased on Supabase Agent Skills (credit: Supabase team) (MIT License)
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
postgres-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend postgres-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Keeps context tight: postgres-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for postgres-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
I recommend postgres-patterns for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
postgres-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
postgres-patterns has been reliable in day-to-day use. Documentation quality is above average for community skills.
Keeps context tight: postgres-patterns is the kind of skill you can hand to a new teammate without a long onboarding doc.
Registry listing for postgres-patterns matched our evaluation — installs cleanly and behaves as described in the markdown.
postgres-patterns reduced setup friction for our internal harness; good balance of opinion and flexibility.
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