Official agent skill

Postgresql Optimization

by github in github/awesome-copilot

PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities.

OfficialMITAuto-check passedDatabases

Install Postgresql Optimization

skills CLI
$ npx skills add github/awesome-copilot --skill postgresql-optimization -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install github/awesome-copilot postgresql-optimization --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/github/awesome-copilot.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/postgresql-optimization .claude/skills/postgresql-optimization && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
postgresql-optimization
GitHub stars
40k
Used in
2 other repos
Token cost
~2.8k tokens
SKILL.md length
299 words
Files
1
Skills in repo
417
Repo updated
First seen
Licence
MIT

At a glance

PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities.

  • Tasks that involve Search implementation
  • SKILL.md covers � PostgreSQL-Specific Features, � PostgreSQL Performance Tuning, �️ PostgreSQL Advanced Data… and 📊 PostgreSQL Extensions & Tools, plus 3 more sections
  • Instructions only: no scripts, shell commands, URLs or credentials in SKILL.md
  • Tasks that involve SQL

What it does

Postgresql Optimization is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. It is a single SKILL.md file with no bundled scripts.

It sits in Databases, covering Search implementation, SQL and Query optimization. It works with PostgreSQL. The repository describes itself as: Community-contributed instructions, agents, skills, and configurations to help you make the most of GitHub Copilot. The licence is MIT.

When your agent uses it

  • Tasks that involve Search implementation
  • Tasks that involve SQL
  • Tasks that involve Query optimization

Example prompts

  • “/postgresql-optimization”

What it can do on your machine

Read from SKILL.md and the folder at commit 727ff2e. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    No scripts in the folder and no shell commands in SKILL.md (its code samples are sql).

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

Postgresql Optimization loads about 2.8k tokens when it runs. Until then it costs about 78 tokens; SKILL.md has 299 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~78
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); files beside SKILL.md are not scanned.

SKILL.md

The full file from github/awesome-copilot at commit 727ff2e, republished under its MIT licence (© github). 299 words, ~2,834 tokens.

Download SKILL.mdSave it as .claude/skills/postgresql-optimization/SKILL.md (or your agent's skills folder).
name
postgresql-optimization
description
PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Covers JSONB operations, array types, custom types, range/geometric types, full-text search, window functions, and PostgreSQL extensions ecosystem.

PostgreSQL Development Assistant

Expert PostgreSQL guidance for ${selection} (or entire project if no selection). Focus on PostgreSQL-specific features, optimization patterns, and advanced capabilities.

� PostgreSQL-Specific Features

JSONB Operations
sql
-- Advanced JSONB queries
CREATE TABLE events (
    id SERIAL PRIMARY KEY,
    data JSONB NOT NULL,
    created_at TIMESTAMPTZ DEFAULT NOW()
);

-- GIN index for JSONB performance
CREATE INDEX idx_events_data_gin ON events USING gin(data);

-- JSONB containment and path queries
SELECT * FROM events 
WHERE data @> '{"type": "login"}'
  AND data #>> '{user,role}' = 'admin';

-- JSONB aggregation
SELECT jsonb_agg(data) FROM events WHERE data ? 'user_id';
Array Operations
sql
-- PostgreSQL arrays
CREATE TABLE posts (
    id SERIAL PRIMARY KEY,
    tags TEXT[],
    categories INTEGER[]
);

-- Array queries and operations
SELECT * FROM posts WHERE 'postgresql' = ANY(tags);
SELECT * FROM posts WHERE tags && ARRAY['database', 'sql'];
SELECT * FROM posts WHERE array_length(tags, 1) > 3;

-- Array aggregation
SELECT array_agg(DISTINCT category) FROM posts, unnest(categories) as category;
Window Functions & Analytics
sql
-- Advanced window functions
SELECT 
    product_id,
    sale_date,
    amount,
    -- Running totals
    SUM(amount) OVER (PARTITION BY product_id ORDER BY sale_date) as running_total,
    -- Moving averages
    AVG(amount) OVER (PARTITION BY product_id ORDER BY sale_date ROWS BETWEEN 2 PRECEDING AND CURRENT ROW) as moving_avg,
    -- Rankings
    DENSE_RANK() OVER (PARTITION BY EXTRACT(month FROM sale_date) ORDER BY amount DESC) as monthly_rank,
    -- Lag/Lead for comparisons
    LAG(amount, 1) OVER (PARTITION BY product_id ORDER BY sale_date) as prev_amount
FROM sales;
sql
-- PostgreSQL full-text search
CREATE TABLE documents (
    id SERIAL PRIMARY KEY,
    title TEXT,
    content TEXT,
    search_vector tsvector
);

-- Update search vector
UPDATE documents 
SET search_vector = to_tsvector('english', title || ' ' || content);

-- GIN index for search performance
CREATE INDEX idx_documents_search ON documents USING gin(search_vector);

-- Search queries
SELECT * FROM documents 
WHERE search_vector @@ plainto_tsquery('english', 'postgresql database');

-- Ranking results
SELECT *, ts_rank(search_vector, plainto_tsquery('postgresql')) as rank
FROM documents 
WHERE search_vector @@ plainto_tsquery('postgresql')
ORDER BY rank DESC;

� PostgreSQL Performance Tuning

Query Optimization
sql
-- EXPLAIN ANALYZE for performance analysis
EXPLAIN (ANALYZE, BUFFERS, FORMAT TEXT) 
SELECT u.name, COUNT(o.id) as order_count
FROM users u
LEFT JOIN orders o ON u.id = o.user_id
WHERE u.created_at > '2024-01-01'::date
GROUP BY u.id, u.name;

-- Identify slow queries from pg_stat_statements
SELECT query, calls, total_time, mean_time, rows,
       100.0 * shared_blks_hit / nullif(shared_blks_hit + shared_blks_read, 0) AS hit_percent
FROM pg_stat_statements 
ORDER BY total_time DESC 
LIMIT 10;
Index Strategies
sql
-- Composite indexes for multi-column queries
CREATE INDEX idx_orders_user_date ON orders(user_id, order_date);

-- Partial indexes for filtered queries
CREATE INDEX idx_active_users ON users(created_at) WHERE status = 'active';

-- Expression indexes for computed values
CREATE INDEX idx_users_lower_email ON users(lower(email));

-- Covering indexes to avoid table lookups
CREATE INDEX idx_orders_covering ON orders(user_id, status) INCLUDE (total, created_at);
Connection & Memory Management
sql
-- Check connection usage
SELECT count(*) as connections, state 
FROM pg_stat_activity 
GROUP BY state;

-- Monitor memory usage
SELECT name, setting, unit 
FROM pg_settings 
WHERE name IN ('shared_buffers', 'work_mem', 'maintenance_work_mem');

�️ PostgreSQL Advanced Data Types

Custom Types & Domains
sql
-- Create custom types
CREATE TYPE address_type AS (
    street TEXT,
    city TEXT,
    postal_code TEXT,
    country TEXT
);

CREATE TYPE order_status AS ENUM ('pending', 'processing', 'shipped', 'delivered', 'cancelled');

-- Use domains for data validation
CREATE DOMAIN email_address AS TEXT 
CHECK (VALUE ~* '^[A-Za-z0-9._%+-]+@[A-Za-z0-9.-]+\.[A-Za-z]{2,}$');

-- Table using custom types
CREATE TABLE customers (
    id SERIAL PRIMARY KEY,
    email email_address NOT NULL,
    address address_type,
    status order_status DEFAULT 'pending'
);
Range Types
sql
-- PostgreSQL range types
CREATE TABLE reservations (
    id SERIAL PRIMARY KEY,
    room_id INTEGER,
    reservation_period tstzrange,
    price_range numrange
);

-- Range queries
SELECT * FROM reservations 
WHERE reservation_period && tstzrange('2024-07-20', '2024-07-25');

-- Exclude overlapping ranges
ALTER TABLE reservations 
ADD CONSTRAINT no_overlap 
EXCLUDE USING gist (room_id WITH =, reservation_period WITH &&);
Geometric Types
sql
-- PostgreSQL geometric types
CREATE TABLE locations (
    id SERIAL PRIMARY KEY,
    name TEXT,
    coordinates POINT,
    coverage CIRCLE,
    service_area POLYGON
);

-- Geometric queries
SELECT name FROM locations 
WHERE coordinates <-> point(40.7128, -74.0060) < 10; -- Within 10 units

-- GiST index for geometric data
CREATE INDEX idx_locations_coords ON locations USING gist(coordinates);

📊 PostgreSQL Extensions & Tools

Useful Extensions
sql
-- Enable commonly used extensions
CREATE EXTENSION IF NOT EXISTS "uuid-ossp";    -- UUID generation
CREATE EXTENSION IF NOT EXISTS "pgcrypto";     -- Cryptographic functions
CREATE EXTENSION IF NOT EXISTS "unaccent";     -- Remove accents from text
CREATE EXTENSION IF NOT EXISTS "pg_trgm";      -- Trigram matching
CREATE EXTENSION IF NOT EXISTS "btree_gin";    -- GIN indexes for btree types

-- Using extensions
SELECT uuid_generate_v4();                     -- Generate UUIDs
SELECT crypt('password', gen_salt('bf'));      -- Hash passwords
SELECT similarity('postgresql', 'postgersql'); -- Fuzzy matching
Monitoring & Maintenance
sql
-- Database size and growth
SELECT pg_size_pretty(pg_database_size(current_database())) as db_size;

-- Table and index sizes
SELECT schemaname, tablename,
       pg_size_pretty(pg_total_relation_size(schemaname||'.'||tablename)) as size
FROM pg_tables 
ORDER BY pg_total_relation_size(schemaname||'.'||tablename) DESC;

-- Index usage statistics
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes 
WHERE idx_scan = 0;  -- Unused indexes
PostgreSQL-Specific Optimization Tips
  • Use EXPLAIN (ANALYZE, BUFFERS) for detailed query analysis
  • Configure postgresql.conf for your workload (OLTP vs OLAP)
  • Use connection pooling (pgbouncer) for high-concurrency applications
  • Regular VACUUM and ANALYZE for optimal performance
  • Partition large tables using PostgreSQL 10+ declarative partitioning
  • Use pg_stat_statements for query performance monitoring

📊 Monitoring and Maintenance

Query Performance Monitoring
sql
-- Identify slow queries
SELECT query, calls, total_time, mean_time, rows
FROM pg_stat_statements 
ORDER BY total_time DESC 
LIMIT 10;

-- Check index usage
SELECT schemaname, tablename, indexname, idx_scan, idx_tup_read, idx_tup_fetch
FROM pg_stat_user_indexes 
WHERE idx_scan = 0;
Database Maintenance
  • VACUUM and ANALYZE: Regular maintenance for performance
  • Index Maintenance: Monitor and rebuild fragmented indexes
  • Statistics Updates: Keep query planner statistics current
  • Log Analysis: Regular review of PostgreSQL logs

🛠️ Common Query Patterns

Pagination
sql
-- ❌ BAD: OFFSET for large datasets
SELECT * FROM products ORDER BY id OFFSET 10000 LIMIT 20;

-- ✅ GOOD: Cursor-based pagination
SELECT * FROM products 
WHERE id > $last_id 
ORDER BY id 
LIMIT 20;
Aggregation
sql
-- ❌ BAD: Inefficient grouping
SELECT user_id, COUNT(*) 
FROM orders 
WHERE order_date >= '2024-01-01' 
GROUP BY user_id;

-- ✅ GOOD: Optimized with partial index
CREATE INDEX idx_orders_recent ON orders(user_id) 
WHERE order_date >= '2024-01-01';

SELECT user_id, COUNT(*) 
FROM orders 
WHERE order_date >= '2024-01-01' 
GROUP BY user_id;
JSON Queries
sql
-- ❌ BAD: Inefficient JSON querying
SELECT * FROM users WHERE data::text LIKE '%admin%';

-- ✅ GOOD: JSONB operators and GIN index
CREATE INDEX idx_users_data_gin ON users USING gin(data);

SELECT * FROM users WHERE data @> '{"role": "admin"}';

📋 Optimization Checklist

Query Analysis
  • Run EXPLAIN ANALYZE for expensive queries
  • Check for sequential scans on large tables
  • Verify appropriate join algorithms
  • Review WHERE clause selectivity
  • Analyze sort and aggregation operations
Index Strategy
  • Create indexes for frequently queried columns
  • Use composite indexes for multi-column searches
  • Consider partial indexes for filtered queries
  • Remove unused or duplicate indexes
  • Monitor index bloat and fragmentation
Security Review
  • Use parameterized queries exclusively
  • Implement proper access controls
  • Enable row-level security where needed
  • Audit sensitive data access
  • Use secure connection methods
Performance Monitoring
  • Set up query performance monitoring
  • Configure appropriate log settings
  • Monitor connection pool usage
  • Track database growth and maintenance needs
  • Set up alerting for performance degradation

🎯 Optimization Output Format

Query Analysis Results
## Query Performance Analysis

**Original Query**:
[Original SQL with performance issues]

**Issues Identified**:
- Sequential scan on large table (Cost: 15000.00)
- Missing index on frequently queried column
- Inefficient join order

**Optimized Query**:
[Improved SQL with explanations]

**Recommended Indexes**:
```sql
CREATE INDEX idx_table_column ON table(column);

Performance Impact: Expected 80% improvement in execution time


## 🚀 Advanced PostgreSQL Features

### Window Functions
```sql
-- Running totals and rankings
SELECT 
    product_id,
    order_date,
    amount,
    SUM(amount) OVER (PARTITION BY product_id ORDER BY order_date) as running_total,
    ROW_NUMBER() OVER (PARTITION BY product_id ORDER BY amount DESC) as rank
FROM sales;
Common Table Expressions (CTEs)
sql
-- Recursive queries for hierarchical data
WITH RECURSIVE category_tree AS (
    SELECT id, name, parent_id, 1 as level
    FROM categories 
    WHERE parent_id IS NULL
    
    UNION ALL
    
    SELECT c.id, c.name, c.parent_id, ct.level + 1
    FROM categories c
    JOIN category_tree ct ON c.parent_id = ct.id
)
SELECT * FROM category_tree ORDER BY level, name;

Focus on providing specific, actionable PostgreSQL optimizations that improve query performance, security, and maintainability while leveraging PostgreSQL's advanced features.

© github, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

Just SKILL.md in skills/postgresql-optimization of github/awesome-copilot.

Open the folder on GitHubat commit 727ff2e

Used in 2 other repositories

We found 2 copies of this SKILL.md (exact, near-identical or edited) in other folders, from 2 other GitHub owners. This page covers the copy in github/awesome-copilot, which our catalogue first saw on October 7, 2026.

Compare with similar skills

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Works with

Categories

Questions about Postgresql Optimization

What does Postgresql Optimization do?

PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities. Postgresql Optimization is an agent skill from github/awesome-copilot, published by the product's own GitHub organization. PostgreSQL-specific development assistant focusing on unique PostgreSQL features, advanced data types, and PostgreSQL-exclusive capabilities.

When should I use Postgresql Optimization?

Postgresql Optimization fits situations like: tasks that involve Search implementation; tasks that involve SQL; tasks that involve Query optimization.

How do I install Postgresql Optimization in Claude Code?

Run `npx skills add github/awesome-copilot --skill postgresql-optimization -a claude-code`. Or copy the skill folder (skills/postgresql-optimization in github/awesome-copilot) into .claude/skills/postgresql-optimization in your project. Claude Code loads it when a task matches its description.

How do I install Postgresql Optimization in Codex?

Run `npx skills add github/awesome-copilot --skill postgresql-optimization -a codex`. Or copy the skill folder (skills/postgresql-optimization in github/awesome-copilot) into .agents/skills/postgresql-optimization in your project. Codex loads it when a task matches its description.

Can I use Postgresql Optimization in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add github/awesome-copilot --skill postgresql-optimization -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/postgresql-optimization, .gemini/skills/postgresql-optimization, .github/skills/postgresql-optimization and .opencode/skills/postgresql-optimization in your project.

What does Postgresql Optimization need to run?

SKILL.md names no scripts, command-line tools or credentials: Postgresql Optimization is instructions for the agent only.

Does Postgresql Optimization access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is Postgresql Optimization safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. Review the folder before installing.

What licence does Postgresql Optimization use?

Postgresql Optimization is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does Postgresql Optimization use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Postgresql Optimization?

Skills that share tags, products or a category with Postgresql Optimization: Postgres (timescale/pg-aiguide, 1.9k stars), PostgreSQL Pro (Jeffallan/claude-skills, 12k stars), Supabase Postgres Best Practices (supabase/agent-skills, 2.7k stars) and PostgreSQL Documentation Reference (2025Emma/vibe-coding-cn, 23k stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Postgresql Optimization?

github (a GitHub organization, an official publisher) maintains it in github/awesome-copilot, which has 39,748 GitHub stars. The repository holds 417 skills in this directory. The repository was last updated on October 7, 2026.

Source: github/awesome-copilot on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.