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Postgres Tuning

Checked
yuniorglez·vlatest·database

Optimizes PostgreSQL performance through query analysis, index recommendations, and configuration adjustments for improved speed and efficiency.

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 yuniorglez/gemini-elite-core --skill postgres-tuning -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

yuniorglez

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 yuniorglez/gemini-elite-core at efbf87fe15.

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

Commit
efbf87fe15
Manifest
skills/postgres-tuning/SKILL.md
Scanned
4 files, 255 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 provides PostgreSQL database tuning recommendations. It analyzes your database configuration and workload to suggest improvements for performance, resource utilization, and stability. The skill aims to optimize query execution, reduce latency, and enhance overall database efficiency.

When to use it

  • You're experiencing slow query times or high database load.
  • Your PostgreSQL server is consuming excessive resources (CPU, memory, disk I/O).
  • You want to proactively improve the performance of your PostgreSQL database before issues arise.
  • You are troubleshooting a specific performance bottleneck in your application that you suspect involves the database.

Key capabilities

  • Database configuration analysis
  • Workload assessment
  • Performance optimization recommendations
  • Resource utilization suggestions

Example prompts

  • "Analyze my PostgreSQL database and suggest tuning improvements."
  • "What are some ways to reduce query latency in my Postgres database?"
  • "How can I optimize resource usage for my PostgreSQL server?"

Tips & gotchas

The skill's effectiveness depends on providing accurate information about your database configuration and workload. Review the suggested changes carefully before implementing them, as incorrect adjustments could negatively impact performance.