database-performance-debugging

aj-geddes/useful-ai-prompts · updated Apr 8, 2026

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$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill database-performance-debugging
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summary

Database performance issues directly impact application responsiveness. Debugging focuses on identifying slow queries and optimizing execution plans.

skill.md

Database Performance Debugging

Table of Contents

Overview

Database performance issues directly impact application responsiveness. Debugging focuses on identifying slow queries and optimizing execution plans.

When to Use

  • Slow application response times
  • High database CPU
  • Slow queries identified
  • Performance regression
  • Under load stress

Quick Start

Minimal working example:

-- Enable slow query log (MySQL)
SET GLOBAL slow_query_log = 'ON';
SET GLOBAL long_query_time = 0.5;

-- View slow queries
SHOW GLOBAL STATUS LIKE 'Slow_queries';
SELECT * FROM mysql.slow_log;

-- PostgreSQL slow queries
CREATE EXTENSION pg_stat_statements;
SELECT mean_exec_time, calls, query
FROM pg_stat_statements
ORDER BY mean_exec_time DESC LIMIT 10;

-- SQL Server slow queries
SELECT TOP 10
  execution_count,
  total_elapsed_time,
  statement_text
FROM sys.dm_exec_query_stats
ORDER BY total_elapsed_time DESC;

-- Query profiling
EXPLAIN ANALYZE
SELECT * FROM orders WHERE user_id = 123;
// ... (see reference guides for full implementation)

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Identify Slow Queries Identify Slow Queries
Common Issues & Solutions Common Issues & Solutions
Execution Plan Analysis Execution Plan Analysis
Debugging Process Debugging Process

Best Practices

✅ DO

  • Follow established patterns and conventions
  • Write clean, maintainable code
  • Add appropriate documentation
  • Test thoroughly before deploying

❌ DON'T

  • Skip testing or validation
  • Ignore error handling
  • Hard-code configuration values
how to use database-performance-debugging

How to use database-performance-debugging on Cursor

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1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your development machine
  • Node.js version 16.0+ with npm package manager (verify with node --version)
  • Active project directory or workspace where you want to add database-performance-debugging
2

Execute installation command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/aj-geddes/useful-ai-prompts --skill database-performance-debugging

The skills CLI fetches database-performance-debugging from GitHub repository aj-geddes/useful-ai-prompts and configures it for Cursor.

3

Select Cursor when prompted

The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ── always included ────
│ • Amp
│ • Antigravity
│ • Cline
│ • Codex
│ ●Cursor(selected)
│ • Cursor
│ • Windsurf
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/database-performance-debugging

Reload or restart Cursor to activate database-performance-debugging. Access the skill through slash commands (e.g., /database-performance-debugging) or your agent's skill management interface.

Security & Verification Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.

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Use Cases

User Story & Requirements Generation

Create detailed user stories, acceptance criteria, and feature specs

Example

Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios

Reduce spec writing time by 50%, ensure comprehensive coverage

Competitive Analysis

Research competitors, compare features, identify gaps

Example

Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities

Complete competitive research in 2 hours instead of 2 days

Roadmap Prioritization

Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs

Example

Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale

Make data-driven prioritization decisions faster

Stakeholder Communication

Draft PRDs, status updates, and stakeholder presentations

Example

Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement

Save 3-5 hours/week on communication overhead

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client
  • Access to product documentation and roadmap tools (Jira, Notion, etc.)
  • Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
  • Stakeholder contact information and communication channels

Time Estimate

30-60 minutes to see productivity improvements

Installation Steps

  1. 1.Install product management skill
  2. 2.Start with user story generation for known feature
  3. 3.Progress to competitive analysis: research 2-3 competitors
  4. 4.Use for roadmap prioritization: apply RICE/ICE scoring
  5. 5.Draft stakeholder communications and refine based on feedback
  6. 6.Build template library for recurring PM tasks
  7. 7.Share effective prompts with product team

Common Pitfalls

  • Not validating competitive research—verify facts before sharing
  • Accepting user stories without involving engineering team
  • Over-relying on frameworks without qualitative judgment
  • Not customizing outputs to company culture and communication style
  • Skipping stakeholder validation of generated requirements

Best Practices

✓ Do

  • +Validate research and competitive analysis with real data
  • +Collaborate with engineering when generating technical requirements
  • +Customize frameworks and templates to your company context
  • +Use skill for first drafts, refine with stakeholder input
  • +Document successful prompt patterns for PM tasks
  • +Combine AI efficiency with human judgment and intuition

✗ Don't

  • Don't publish competitive analysis without fact-checking
  • Don't finalize user stories without engineering review
  • Don't make prioritization decisions solely on AI scoring
  • Don't skip customer validation of generated requirements
  • Don't ignore company-specific context and culture

💡 Pro Tips

  • Provide context: company goals, constraints, customer feedback
  • Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
  • Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
  • Use skill for 70% generation + 30% customization to company needs

When to Use This

✓ Use When

Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.

✗ Avoid When

Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.

Learning Path

  1. 1Basic: user stories, feature specs, status updates
  2. 2Intermediate: competitive analysis, prioritization frameworks, PRDs
  3. 3Advanced: product strategy, go-to-market planning, OKR setting
  4. 4Expert: product vision, market positioning, business model innovation

Discussion

Product Hunt–style comments (not star reviews)
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general reviews

Ratings

4.754 reviews
  • Aanya Robinson· Dec 24, 2024

    database-performance-debugging fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • Shikha Mishra· Dec 16, 2024

    Useful defaults in database-performance-debugging — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Aanya Okafor· Dec 4, 2024

    database-performance-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Aditi Wang· Nov 23, 2024

    Useful defaults in database-performance-debugging — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • Lucas Smith· Nov 15, 2024

    database-performance-debugging is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Yash Thakker· Nov 7, 2024

    database-performance-debugging has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • Dhruvi Jain· Oct 26, 2024

    Solid pick for teams standardizing on skills: database-performance-debugging is focused, and the summary matches what you get after install.

  • Aditi Nasser· Oct 14, 2024

    I recommend database-performance-debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • Lucas Anderson· Oct 6, 2024

    Keeps context tight: database-performance-debugging is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • Diya Abebe· Sep 25, 2024

    I recommend database-performance-debugging for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

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