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Sql Optimization Patterns

Checked
wshobson·vlatest·database·MIT

Helps with optimization, patterns as part of working with databases and data persistence workflows.

Repository

Install on your platform

Showing Claude Code. One command installs this skill into any of the agents below.

  1. 1

    Run this in your project

    terminal
    $ npx skills add wshobson/agents --skill sql-optimization-patterns -a claude-code

  2. Uses the open-source skills CLI and needs Node.js. Add -g to install it for your user instead of this project.

What we know about this skill

Publisher

wshobson

Taken from the source listing. We haven't verified who controls this account.

Install target

Live repository

Not pinned. Installing fetches whatever the repository holds at the time you run the command.

No human has reviewed this code. The checks below are a static scan of the files at one commit — nobody has run this skill or judged whether it works. A skill runs with whatever access you give your agent, so read the source before you install it.

Automated safety checks

scanned

This skill passed all 6 checks. We read its files at one commit and looked for the things that make a skill dangerous to run — not for whether it works.

  • Declares what it does: passed

    SKILL.md parses and declares a name and a description.

    SKILL.md exists, its frontmatter parses, and it declares a name and a description.

  • No undeclared network calls: passed

    Makes no network calls.

    Every host the skill dials — from its scripts, its config endpoints or the commands it tells the agent to run — is either well-known package and API infrastructure or declared in its own frontmatter. Links in documentation are not counted.

  • No obfuscated payloads: passed

    No encoded or obfuscated payloads.

    No base64, hex or compressed blob is decoded and then executed, and no encoded shell commands.

  • No credential access: passed

    Reads no keys, tokens, keychains or credential files.

    Neither the code nor the SKILL.md instructions read SSH keys, cloud credentials, keychains, token stores, browser cookie databases or dotfiles that hold secrets.

  • No pipe-to-shell installers: passed

    No pipe-to-shell or download-and-run installers.

    No `curl … | sh` pattern, in the code or in the instructions, that runs code fetched at run time.

  • Pinned to a commit: passed

    Scanned wshobson/agents at 46891e7e60.

    The exact upstream commit SHA that was scanned is recorded, so the result refers to specific bytes.

Commit
46891e7e60
Manifest
plugins/developer-essentials/skills/sql-optimization-patterns/SKILL.md
Scanned
2 files, 464 lines
This is a static scan, not a review. It reads the skill's files; it does not run them, and it cannot tell you whether the skill is any good or whether the code does what its description says. A skill runs with whatever access you give your agent.
What each check looks for →

About this skill

What it does

This skill helps AI agents identify and apply common SQL optimization patterns to improve query performance. It can analyze queries for inefficiencies, suggest index creation strategies, and recommend restructuring complex joins or subqueries.

When to use it

  • You're experiencing slow query execution in a production database.
  • You need to optimize a poorly performing SQL statement before deployment.
  • Your application is returning large result sets that impact performance.
  • You want to ensure your queries are using indexes effectively.

Key capabilities

  • Detect redundant or inefficient subqueries
  • Suggest index creation for frequently queried columns
  • Identify opportunities to reduce table joins
  • Recommend query restructuring for better execution plans

Example prompts

  • "Optimize this SQL query for faster performance."
  • "Suggest index strategies for this database schema."
  • "Rewrite this complex join to improve efficiency."

Tips & gotchas

  • Ensure the AI agent has access to the relevant database schema and query execution plans for accurate recommendations.
  • Optimization suggestions may vary based on specific database systems (e.g., PostgreSQL vs. MySQL), so review results in context of your environment.