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code-review-analysis▌

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 code-review-analysis
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summary

Comprehensive code reviews covering quality, security, performance, and best practices.

  • ›Systematic review process across five dimensions: code quality, security vulnerabilities, performance, testing, and maintainability
  • ›Includes initial assessment, detailed analysis guides, and constructive feedback frameworks
  • ›Covers pull request analysis, coding standards compliance, and developer mentoring through structured review
  • ›Best practices emphasize respectful feedback with explanations a
skill.md

Code Review Analysis

Table of Contents

Overview

Systematic code review process covering code quality, security, performance, maintainability, and best practices following industry standards.

When to Use

  • Reviewing pull requests and merge requests
  • Analyzing code quality before merging
  • Identifying security vulnerabilities
  • Providing constructive feedback to developers
  • Ensuring coding standards compliance
  • Mentoring through code review

Quick Start

Minimal working example:

# Check the changes
git diff main...feature-branch

# Review file changes
git diff --stat main...feature-branch

# Check commit history
git log main...feature-branch --oneline

Reference Guides

Detailed implementations in the references/ directory:

Guide Contents
Initial Assessment Initial Assessment
Code Quality Analysis Code Quality Analysis
Security Review Security Review
Performance Review Performance Review
Testing Review Testing Review
Best Practices Best Practices

Best Practices

✅ DO

  • Be constructive and respectful
  • Explain the "why" behind suggestions
  • Provide code examples
  • Ask questions if unclear
  • Acknowledge good practices
  • Focus on important issues
  • Consider the context
  • Offer to pair program on complex issues

❌ DON'T

  • Be overly critical or personal
  • Nitpick minor style issues (use automated tools)
  • Block on subjective preferences
  • Review too many changes at once (>400 lines)
  • Forget to check tests
  • Ignore security implications
  • Rush the review
how to use code-review-analysis

How to use code-review-analysis on Cursor

AI-first code editor with Composer

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 code-review-analysis
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 code-review-analysis

The skills CLI fetches code-review-analysis 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/code-review-analysis

Reload or restart Cursor to activate code-review-analysis. Access the skill through slash commands (e.g., /code-review-analysis) 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.

Additional Resources

›View source code on GitHub›Skills CLI documentation›Learn more about Cursor›What are agent skills?

List & Monetize Your Skill

Submit your Claude Code skill and start earning

GET_STARTED →

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)
  • No comments yet — start the thread.
general reviews

Ratings

4.6★★★★★55 reviews
  • ★★★★★Kabir Srinivasan· Dec 24, 2024

    code-review-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★James Brown· Dec 20, 2024

    Useful defaults in code-review-analysis — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • ★★★★★Hiroshi Khanna· Dec 20, 2024

    I recommend code-review-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★Diya Malhotra· Dec 12, 2024

    code-review-analysis has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • ★★★★★Diya Mehta· Dec 4, 2024

    code-review-analysis reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • ★★★★★Dev Gonzalez· Nov 23, 2024

    We added code-review-analysis from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.

  • ★★★★★Kaira Dixit· Nov 15, 2024

    I recommend code-review-analysis for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • ★★★★★James Taylor· Nov 11, 2024

    code-review-analysis fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • ★★★★★Ishan Torres· Nov 7, 2024

    Registry listing for code-review-analysis matched our evaluation — installs cleanly and behaves as described in the markdown.

  • ★★★★★Diya Sethi· Nov 3, 2024

    Solid pick for teams standardizing on skills: code-review-analysis is focused, and the summary matches what you get after install.

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