postgresql-optimization

A development guide for PostgreSQL, a relational database with features beyond standard SQL. It focuses on PostgreSQL-specific tools such as JSONB documents, arrays, custom data types, full-text search, window functions, and extensions.

In plain words
What is it for?
Use it to write and optimize queries involving JSON data, arrays, ranges, geometric values, full-text search, analytical window functions, and PostgreSQL extensions.
Why use it?
It helps you use PostgreSQL's native capabilities correctly when ordinary SQL patterns are not enough or would be less efficient.

Skill for Claude CodeCodex

Install

Getting it into your agent

One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.

agentmods
npx agentmods add skills/andersonlemesc/oryntra/postgresql-optimization
Any agent
npx skills add andersonlemesc/Oryntra --skill postgresql-optimization
Clone the repo
git clone --depth 1 https://github.com/andersonlemesc/Oryntra

Made for: Claude Code, Codex.

Per session 57 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,695 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. Scan, not verified.
Origin original No closer match found in the catalogue.
Token cost

What it costs to keep this loaded

Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.

ModelPer sessionOnce invoked
Fable 5 $0.00057 $0.02695
Opus 5 $0.00028 $0.01347
Sonnet 5 $0.00011 $0.00539
Haiku 4.5 $0.00006 $0.00269

Measured 2d ago against content hash 9b511a7d7063, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

postgresql-optimization scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.

A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.

Nothing flagged

None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.

.agents/skills/postgresql-optimization/SKILL.md · 405 lines

How it starts

The opening of the file, as written. The whole thing — 405 lines — stays where its author put it; the contents beside it link to each section on GitHub.

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

-- 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

-- 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

-- 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;

Full-Text Search

-- 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;

Read the full file on GitHub · 405 lines

Changes

What this file has done since we first saw it

Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.

  1. 2d ago First seen · 405 lines · 57 tokens per session scan A 9b511a7d7063

Subscribe to this mod's changes

postgresql-optimization is a skill published in the GitHub repository andersonlemesc/Oryntra (5 stars, last pushed 12d ago), licensed Apache-2.0. It adds 57 tokens to every session and 2,695 once invoked, about $0.0003 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.

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