gemini-peer-review

jezweb/claude-skills · updated Apr 8, 2026

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$npx skills add https://github.com/jezweb/claude-skills --skill gemini-peer-review
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summary

Consult Gemini as a coding peer for a second opinion on code quality, architecture decisions, debugging, or security reviews.

skill.md

Gemini Peer Review

Consult Gemini as a coding peer for a second opinion on code quality, architecture decisions, debugging, or security reviews.

Setup

API Key: Set GEMINI_API_KEY as an environment variable. Get a key from https://aistudio.google.com/apikey if you don't have one.

export GEMINI_API_KEY="your-key-here"

Workflow

  1. Determine mode from user request (review, architect, debug, security, quick)

  2. Read target files into context

  3. Build prompt using the AI-to-AI template from references/prompt-templates.md

  4. Write prompt to file at .claude/artifacts/gemini-prompt.txt (avoids shell escaping issues)

  5. Call the API — generate a Python script that:

    • Reads GEMINI_API_KEY from environment
    • Reads the prompt from .claude/artifacts/gemini-prompt.txt
    • POSTs to https://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent
    • Payload: {"contents": [{"parts": [{"text": prompt}]}], "generationConfig": {"temperature": 0.3, "maxOutputTokens": 8192}}
    • Extracts text from candidates[0].content.parts[0].text
    • Prints result to stdout

    Write the script to .claude/scripts/gemini-review.py and run it.

  6. Synthesize — present Gemini's findings, add your own perspective (agree/disagree), let the user decide what to implement

Modes

Code Review

Review specific files for bugs, logic errors, security vulnerabilities, performance issues, and best practice violations.

Read the target files, build a prompt using the Code Review template, call with gemini-2.5-flash.

Architecture Advice

Get feedback on design decisions with trade-off analysis. Include project context (CLAUDE.md, relevant source files).

Read project context, build a prompt using the Architecture template, call with gemini-2.5-pro.

Debugging Help

Analyse errors when stuck after 2+ failed fix attempts. Gemini sees the code fresh without your debugging context bias.

Read the problematic files, build a prompt using the Debug template (include error message and previous attempts), call with gemini-2.5-flash.

Security Scan

Scan code for security vulnerabilities (injection, auth bypass, data exposure).

Read the target directory's source files, build a prompt using the Security template, call with gemini-2.5-pro.

Quick Question

Fast question without file context. Build prompt inline, write to file, call with gemini-2.5-flash.

Model Selection

Mode Model Why
review, debug, quick gemini-2.5-flash Fast, good for straightforward analysis
architect, security-scan gemini-2.5-pro Better reasoning for complex trade-offs

Check current model IDs if errors occur — they change frequently:

curl -s "https://generativelanguage.googleapis.com/v1beta/models?key=$GEMINI_API_KEY" | python3 -c "import sys,json; [print(m['name']) for m in json.load(sys.stdin)['models'] if 'gemini' in m['name']]"

When to Use

Good use cases:

  • Before committing major changes (final review)
  • When stuck debugging after multiple attempts
  • Architecture decisions with multiple valid options
  • Security-sensitive code review

Avoid using for:

  • Simple syntax checks (Claude handles these faster)
  • Every single edit (too slow, unnecessary)
  • Questions with obvious answers

Prompt Construction

Critical: Always use the AI-to-AI prompting format. Write the full prompt to a file — never pass code inline via bash arguments (shell escaping will break it).

When building the prompt:

  1. Start with the AI-to-AI header from references/prompt-templates.md
  2. Append the mode-specific template
  3. Append the file contents with clear --- filename --- separators
  4. Write to .claude/artifacts/gemini-prompt.txt
  5. Generate and run the API call script

Reference Files

When Read
Building prompts for any mode references/prompt-templates.md
how to use gemini-peer-review

How to use gemini-peer-review 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 gemini-peer-review
2

Execute installation command

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

$npx skills add https://github.com/jezweb/claude-skills --skill gemini-peer-review

The skills CLI fetches gemini-peer-review from GitHub repository jezweb/claude-skills 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/gemini-peer-review

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

List & Monetize Your Skill

Submit your Claude Code skill and start earning

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

Ratings

4.863 reviews
  • Mia Okafor· Dec 28, 2024

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

  • Hana Zhang· Dec 28, 2024

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

  • Luis Sethi· Dec 24, 2024

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

  • Dhruvi Jain· Dec 8, 2024

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

  • Lucas Perez· Dec 8, 2024

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

  • Oshnikdeep· Nov 27, 2024

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

  • Ama Sanchez· Nov 27, 2024

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

  • Mia Liu· Nov 27, 2024

    gemini-peer-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • Ama Abbas· Nov 19, 2024

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

  • Anika Gonzalez· Nov 19, 2024

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

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