sql-optimization-patterns

A guide to improving SQL, the language used to query databases, through indexes, query-plan analysis, and database design techniques. It focuses on how databases find and combine stored data.

In plain words
What is it for?
Use it to inspect EXPLAIN query plans, choose indexes, design efficient schemas, improve response times, reduce database load, and resolve N+1 query problems.
Why use it?
It helps diagnose slow queries and reduce unnecessary database work, especially as datasets grow. It also addresses common problems such as scanning whole tables and making repeated queries for related data.

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/luizedupp/rememb/sql-optimization-patterns
Any agent
npx skills add LuizEduPP/Rememb --skill sql-optimization-patterns
Clone the repo
git clone --depth 1 https://github.com/LuizEduPP/Rememb

Made for: Claude Code, Codex.

Per session 45 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,365 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 $0.00045 $0.03365
Opus 5 $0.00023 $0.01682
Sonnet 5 $0.00009 $0.00673
Haiku 4.5 $0.00005 $0.00336

Measured yesterday against content hash 0c6e9dd8fb5c, method: parsed. Prices are Anthropic first-party input rates as of 2026-08-30, from the pricing page.

Security

Grade A, and why

sql-optimization-patterns 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 yesterday.

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.

src/rememb_skills/sql-optimization-patterns/SKILL.md · 516 lines

How it starts

The opening of the file, as written. The whole thing — 516 lines — stays where its author put it; the contents beside it link to each section on GitHub.

SQL Optimization Patterns

Transform slow database queries into lightning-fast operations through systematic optimization, proper indexing, and query plan analysis.

When to Use

  • Debugging slow-running queries
  • Designing performant database schemas
  • Optimizing application response times
  • Reducing database load and costs
  • Improving scalability for growing datasets
  • Analyzing EXPLAIN query plans
  • Implementing efficient indexes
  • Resolving N+1 query problems

Core Concepts

1. Query Execution Plans (EXPLAIN)

Understanding EXPLAIN output is fundamental to optimization.

PostgreSQL EXPLAIN:

-- Basic explain
EXPLAIN SELECT * FROM users WHERE email = '[email protected]';

-- With actual execution stats
EXPLAIN ANALYZE
SELECT * FROM users WHERE email = '[email protected]';

-- Verbose output with more details
EXPLAIN (ANALYZE, BUFFERS, VERBOSE)
SELECT u.*, o.order_total
FROM users u
JOIN orders o ON u.id = o.user_id
WHERE u.created_at > NOW() - INTERVAL '30 days';

Key Metrics to Watch:

  • Seq Scan: Full table scan (usually slow for large tables)
  • Index Scan: Using index (good)
  • Index Only Scan: Using index without touching table (best)
  • Nested Loop: Join method (okay for small datasets)
  • Hash Join: Join method (good for larger datasets)
  • Merge Join: Join method (good for sorted data)
  • Cost: Estimated query cost (lower is better)
  • Rows: Estimated rows returned
  • Actual Time: Real execution time

2. Index Strategies

Indexes are the most powerful optimization tool.

Index Types:

  • B-Tree: Default, good for equality and range queries
  • Hash: Only for equality (=) comparisons
  • GIN: Full-text search, array queries, JSONB
  • GiST: Geometric data, full-text search
  • BRIN: Block Range INdex for very large tables with correlation
-- Standard B-Tree index
CREATE INDEX idx_users_email ON users(email);

-- Composite index (order matters!)
CREATE INDEX idx_orders_user_status ON orders(user_id, status);

-- Partial index (index subset of rows)
CREATE INDEX idx_active_users ON users(email)
WHERE status = 'active';

-- Expression index
CREATE INDEX idx_users_lower_email ON users(LOWER(email));

-- Covering index (include additional columns)
CREATE INDEX idx_users_email_covering ON users(email)
INCLUDE (name, created_at);

-- Full-text search index
CREATE INDEX idx_posts_search ON posts
USING GIN(to_tsvector('english', title || ' ' || body));

-- JSONB index
CREATE INDEX idx_metadata ON events USING GIN(metadata);

Read the full file on GitHub · 516 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. yesterday First seen · 516 lines · 45 tokens per session scan A 0c6e9dd8fb5c

Subscribe to this mod's changes

sql-optimization-patterns is a skill published in the GitHub repository LuizEduPP/Rememb (4 stars, last pushed 1mo ago), licensed MIT. It adds 45 tokens to every session and 3,365 once invoked, about $0.0002 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.

Related

Other skills, from other repositories

claude-api

Build, debug, and optimize Claude API / Anthropic SDK apps. Apps built with this skill should include prompt caching. Also handles migrating existing Claude API code between Claude model versions (4.5 → 4.6, 4.6 → 4.7, retired-model replacements). TRIGGER when: code imports anthropic/@anthropic-ai/sdk; user asks for…

Prismer-AI/PrismerCloud · 193 tokens

agent-coordination

Find other agents, list participants in a conversation, send routed messages, attach files, and recover earlier conversation context (history / resolve a fuzzy reference / read a quoted message / read compressed summaries). Use whenever you need to delegate to another agent, address a peer in a multi-agent…

Prismer-AI/PrismerCloud · 122 tokens

image-generate

Generate an image from a text prompt via the cloud LLM image proxy, persist it as a content-addressed workspace asset, and return a ContentBlock that downstream renderers can attach. Use whenever the user asks "draw / generate / make an image of …", an agent needs a diagram / illustration as a follow-up artifact, or a…

Prismer-AI/PrismerCloud · 103 tokens

claim-agent-ownership

Orchestrator skill for resolving multi-daemon binding contention. Use when you (the orchestrator) detect an agent.binding.contested sync event indicating two daemons are racing for the same agent — explicitly rebind ownership to a chosen target daemon so subsequent dispatches route deterministically. Implements Gap…

Prismer-AI/PrismerCloud · 101 tokens

prismer-im-collab

Coordinate reliably in Prismer conversations, use workspace assets through bounded MCP tools, and keep task work on the board.

Prismer-AI/PrismerCloud · 30 tokens

human-approval

Request human approval before performing a SAFETY-CRITICAL, IRREVERSIBLE, or SCOPE-EXPANDING action — submit a structured context (action, scope, risk, consequence) plus options, then STOP the current turn. The platform redispatches the agent after the human decides. NEVER use for routine deliverables (writing docs /…

Prismer-AI/PrismerCloud · 168 tokens