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Postgresql Code Review

Official
github·vlatest·database·MIT

Official github skill covering PostgreSQL, code review for working with databases and data persistence.

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 github/awesome-copilot --skill postgresql-code-review -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

github

Matched to the vendor's own GitHub organisation.

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 failed 1 of 6 checks. A failure is not proof of bad intent; it means something in the code needs a human to look at it before you run it.

  • Declares what it does: failed

    No SKILL.md was found in the skill's directory.

    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 github/awesome-copilot at 6c4d33b9cf.

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

Scanned
0 files
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

The postgresql-code-review skill enables AI agents to analyze PostgreSQL code for best practices, security vulnerabilities, and performance improvements. It identifies issues like inefficient queries, improper indexing, or insecure configurations in SQL scripts and provides actionable feedback.

When to use it

  • Reviewing SQL scripts before deployment to ensure they follow PostgreSQL best practices.
  • Auditing legacy databases for potential optimization opportunities.
  • Ensuring compliance with security standards when writing new database schemas or queries.
  • Helping developers understand why a particular query is slow or causing errors.

Key capabilities

  • Detects inefficient or insecure SQL queries
  • Suggests performance improvements and best practices
  • Identifies missing indexes or improper schema design
  • Highlights potential security risks in PostgreSQL configurations

Example prompts

  • "Review this PostgreSQL query for performance issues."
  • "Check if this SQL script follows secure coding practices."
  • "Analyze the schema design of this database for optimization opportunities."

Tips & gotchas

  • Ensure your code is formatted correctly and includes full context for accurate analysis.
  • This skill works best with well-documented queries; ambiguous or incomplete scripts may yield less useful results.