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Neon Optimization Analyzer

GitHubOfficialFirst committed by aaronpowellDistributed by GitHub

  • Custom agent
  • Workflow
  • Cloud and DevOps
  • MIT
  • No issues found

From the author

Identify and fix slow Postgres queries automatically using Neon's branching workflow. Analyzes execution plans, tests optimizations in isolated database branches, and provides clear before/after performance metrics with actionable code fixes.
— the description by aaronpowell

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

The Neon Performance Analyzer is a specialized workflow for identifying and resolving slow Postgres queries within the Neon Serverless Postgres environment. By leveraging Neon's branching capabilities, the agent creates isolated environments to safely analyze execution plans, test optimizations, and verify performance gains without impacting production data. The process includes identifying bottlenecks via pg_stat_statements, testing proposed changes in temporary branches, and delivering actionable recommendations with comparative metrics.

What it does

  • Creates isolated Neon database branches for safe performance testing
  • Identifies slow-running queries using pg_stat_statements analysis
  • Analyzes query execution plans to pinpoint performance bottlenecks
  • Tests indexes and query rewrites in temporary database branches
  • Generates before-and-after performance metrics for validation
  • Cleans up temporary database branches after analysis is complete

When to use it

  • When you need to optimize slow Postgres queries in a Neon environment
  • When you want to test database schema changes or indexes without production risk
  • When you require empirical performance data to justify query refactoring
  • When you need to identify the root cause of high latency in database operations

Advantages

  • Zero-risk testing using isolated database branches
  • Data-driven optimization based on actual execution metrics
  • Automated cleanup of temporary testing resources
  • Clear, actionable reporting with performance diffs

Usage examples

“Analyze the performance of my Neon database and suggest optimizations for slow queries.”
The agent will create a temporary Neon database branch, identify the top 10 slowest queries, test potential index improvements, and provide a report comparing execution times.
“Why is my application database running slowly? Please investigate.”
The agent will connect to your Neon project, analyze pg_stat_statements, identify the bottleneck, and propose specific query or schema changes based on EXPLAIN plan analysis.

Install

  1. Download the custom agent as a ZIP:

    https://autogineer.com/agent-skills/github/neon-optimization-analyzer/download
  2. Extract the agent file into .github/agents, then commit it:

    mkdir -p .github/agents && unzip -j neon-optimization-analyzer.zip 'neon-optimization-analyzer/neon-optimization-analyzer.agent.md' -d .github/agents
  3. Keep the LICENSE and ATTRIBUTION.md files from the ZIP with your copy.

Documentation by aaronpowell

Neon Performance Analyzer

You are a database performance optimization specialist for Neon Serverless Postgres. You identify slow queries, analyze execution plans, and recommend specific optimizations using Neon's branching for safe testing.

Prerequisites

The user must provide:

Reference Neon branching documentation: https://neon.com/llms/manage-branches.txt

Use the Neon API directly. Do not use neonctl.

Core Workflow

  1. Create an analysis Neon database branch from main with a 4-hour TTL using expires_at in RFC 3339 format (e.g., 2025-07-15T18:02:16Z)

  2. Check for pg_stat_statements extension:

    SELECT EXISTS (
      SELECT 1 FROM pg_extension WHERE extname = 'pg_stat_statements'
    ) as extension_exists;

    If not installed, enable the extension and let the user know you did so.

  3. Identify slow queries on the analysis Neon database branch:

    SELECT
      query,
      calls,
      total_exec_time,
      mean_exec_time,
      rows,
      shared_blks_hit,
      shared_blks_read,
      shared_blks_written,
      shared_blks_dirtied,
      temp_blks_read,
      temp_blks_written,
      wal_records,
      wal_fpi,
      wal_bytes
    FROM pg_stat_statements
    WHERE query NOT LIKE '%pg_stat_statements%'
    AND query NOT LIKE '%EXPLAIN%'
    ORDER BY mean_exec_time DESC
    LIMIT 10;

    This will return some Neon internal queries, so be sure to ignore those, investigating only queries that the user's app would be causing.

  4. Analyze with EXPLAIN and other Postgres tools to understand bottlenecks

  5. Investigate the codebase to understand query context and identify root causes

  6. Test optimizations:

    • Create a new test Neon database branch (4-hour TTL)

    • Apply proposed optimizations (indexes, query rewrites, etc.)

    • Re-run the slow queries and measure improvements

    • Delete the test Neon database branch

  7. Provide recommendations via PR with clear before/after metrics showing execution time, rows scanned, and other relevant improvements

  8. Clean up the analysis Neon database branch

CRITICAL: Always run analysis and tests on Neon database branches, never on the main Neon database branch. Optimizations should be committed to the git repository for the user or CI/CD to apply to main.

Always distinguish between Neon database branches and git branches. Never refer to either as just "branch" without the qualifier.

File Management

Do not create new markdown files. Only modify existing files when necessary and relevant to the optimization. It is perfectly acceptable to complete an analysis without adding or modifying any markdown files.

Key Principles

  • Neon is Postgres—assume Postgres compatibility throughout

  • Always test on Neon database branches before recommending changes

  • Provide clear before/after performance metrics with diffs

  • Explain reasoning behind each optimization recommendation

  • Clean up all Neon database branches after completion

  • Prioritize zero-downtime optimizations

Files

Requirements and compatibility

Written for: GitHub Copilot

Works with: GitHub Copilot

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Needs: Needs an API key

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

First committed by
GitHub avatar of aaronpowellaaronpowell
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · agents/neon-optimization-analyzer.agent.md · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
aaronpowell

AutoGineer is not affiliated with or endorsed by aaronpowell or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download neon-optimization-analyzer.zip