Consult Gemini as a coding peer for a second opinion on code quality, architecture decisions, debugging, or security reviews.
Works with
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versiongemini-peer-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches gemini-peer-review from jezweb/claude-skills and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate gemini-peer-review. Access via /gemini-peer-review in your agent's command palette.
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 environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
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
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
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Consult Gemini as a coding peer for a second opinion on code quality, architecture decisions, debugging, or security reviews.
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"
Determine mode from user request (review, architect, debug, security, quick)
Read target files into context
Build prompt using the AI-to-AI template from references/prompt-templates.md
Write prompt to file at .claude/artifacts/gemini-prompt.txt (avoids shell escaping issues)
Call the API — generate a Python script that:
GEMINI_API_KEY from environment.claude/artifacts/gemini-prompt.txthttps://generativelanguage.googleapis.com/v1beta/models/{model}:generateContent{"contents": [{"parts": [{"text": prompt}]}], "generationConfig": {"temperature": 0.3, "maxOutputTokens": 8192}}candidates[0].content.parts[0].textWrite the script to .claude/scripts/gemini-review.py and run it.
Synthesize — present Gemini's findings, add your own perspective (agree/disagree), let the user decide what to implement
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.
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.
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.
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.
Fast question without file context. Build prompt inline, write to file, call with gemini-2.5-flash.
| 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']]"
Good use cases:
Avoid using for:
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:
--- filename --- separators.claude/artifacts/gemini-prompt.txt| When | Read |
|---|---|
| Building prompts for any mode | references/prompt-templates.md |
Make data-driven prioritization decisions faster
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
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ 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.
jezweb/claude-skills
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
Solid pick for teams standardizing on skills: gemini-peer-review is focused, and the summary matches what you get after install.
gemini-peer-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
gemini-peer-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
gemini-peer-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
I recommend gemini-peer-review for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
Solid pick for teams standardizing on skills: gemini-peer-review is focused, and the summary matches what you get after install.
gemini-peer-review fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
gemini-peer-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
gemini-peer-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Registry listing for gemini-peer-review matched our evaluation — installs cleanly and behaves as described in the markdown.
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