query-optimization

query-optimization is a skill for Claude Code, Codex from seb1n/awesome-ai-agent-skills. It costs 55 tokens per session (2,495 once invoked), scanned A, original, MIT.

A guide for finding and fixing slow SQL database queries using query plans, indexes, rewritten queries, or ORM changes. An ORM is application code that builds database queries for you.

In plain words
What is it for?
Use it to inspect EXPLAIN output, find inefficient scans or joins, address missing indexes and N+1 queries, and suggest ways to monitor performance.
Why use it?
It helps identify why a query is slow and supports measurable before-and-after improvements, including problems where an ORM makes one query per item.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one.

Good fit Use it to inspect EXPLAIN output, find inefficient scans or joins, address missing indexes and N+1 queries, and suggest ways to monitor performance.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/seb1n/awesome-ai-agent-skills/query-optimization
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.

Any agent
npx skills add seb1n/awesome-ai-agent-skills --skill query-optimization
Clone the repo
git clone --depth 1 https://github.com/seb1n/awesome-ai-agent-skills

Made for: Claude Code, Codex.

Wrote this? Show the measurements

A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.

agentmods badge for query-optimization

README.md
[![agentmods](https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/query-optimization/github.svg)](https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/query-optimization)
Your own site
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/query-optimization"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/query-optimization/github.svg" alt="Measured on agentmods" height="20"></a>

Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.

agentmods 80×15 button for query-optimization

Your own site · 80×15
<a href="https://agentmods.dev/skills/seb1n/awesome-ai-agent-skills/query-optimization"><img src="https://agentmods.dev/badge/skills/seb1n/awesome-ai-agent-skills/query-optimization.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 55 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 2,495 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe. Third-party audits
  • NVIDIA SkillSpector pass 7 Sept 2026
How audits are shown
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 5.1 $0.00055 $0.02495
Opus 5 $0.00028 $0.01247
Sonnet 5 $0.00011 $0.00499
Haiku 4.5 $0.00006 $0.00249

Measured 9d ago against content hash 0c12c4f33599, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, from the pricing page.

Security

Grade A, and why

query-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 9d 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.

Origin

Copies of this mod

1 near-identical copy found in the catalogue:

database/query-optimization/SKILL.md · 172 lines

How it starts

The opening of the file, as written. The whole thing — 172 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Query Optimization

This skill enables an AI agent to diagnose and fix slow database queries. The agent uses EXPLAIN/EXPLAIN ANALYZE to interpret query execution plans, identifies missing indexes and inefficient scan patterns, rewrites queries to eliminate performance bottlenecks, detects and resolves N+1 query problems in ORMs, and recommends monitoring tools to track query performance over time. The focus is on practical, measurable improvements with before-and-after evidence.

Workflow

  1. Identify the slow query: Collect the problematic query from slow query logs, application performance monitoring (APM) tools, or user reports. Note the current execution time, the table sizes involved, and how frequently the query runs. High-frequency slow queries should be prioritized over rare ones.

  2. Analyze the execution plan: Run EXPLAIN ANALYZE (PostgreSQL) or EXPLAIN FORMAT=JSON (MySQL) on the query to obtain the actual execution plan. Look for sequential scans on large tables, nested loop joins with high row estimates, sort operations on unindexed columns, and large gaps between estimated and actual row counts.

  3. Identify optimization opportunities: Based on the plan, identify concrete fixes: add indexes for columns in WHERE, JOIN, and ORDER BY clauses; rewrite subqueries as JOINs; replace SELECT * with specific columns; add LIMIT clauses where appropriate; use covering indexes to avoid table lookups; eliminate redundant or duplicate conditions.

  4. Apply optimizations: Create the necessary indexes, rewrite the query, or adjust ORM usage. For N+1 problems, switch from lazy loading to eager loading (e.g., select_related/prefetch_related in Django, include in Prisma, joinedload in SQLAlchemy). Apply one change at a time to measure each improvement independently.

  5. Measure and validate: Re-run EXPLAIN ANALYZE on the optimized query and compare execution time, rows scanned, and plan structure against the original. Verify that the query returns identical results. Check that new indexes do not degrade write performance beyond acceptable thresholds.

Read the full file on GitHub · 172 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. 9d ago First seen · 172 lines · 55 tokens per session scan A 0c12c4f33599

Subscribe to this mod's changes

query-optimization is a skill published in the GitHub repository seb1n/awesome-ai-agent-skills (176 stars, last pushed 1mo ago), licensed MIT. It adds 55 tokens to every session and 2,495 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 2026-08-30.

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