What you will build
This tutorial shows how to use Cursor Cloud Agents for SEO/GEO pull request review without giving the agent full autonomy. The first useful workflow is comment-only: the cloud agent reviews a branch or PR, identifies risks, and suggests a small patch only after you approve specific findings.
You will create:
.cursor/
rules/
seo-geo-pr-review.mdc
seo-geo/
pr-review/
checklist.md
review-prompt.md
approved-fixes.md
Use this for branches that touch metadata helpers, schema, robots rules, sitemap output, internal links, page templates, localization, analytics, or large content batches.
Step 1: create the PR review rule
mkdir -p .cursor/rules seo-geo/pr-review
cat > .cursor/rules/seo-geo-pr-review.mdc <<'EOF2'
---
description: SEO/GEO pull request review policy
globs:
- "src/**"
- "app/**"
- "pages/**"
- "content/**"
- "public/robots.txt"
- "**/*sitemap*"
- "**/*schema*"
- "**/*seo*"
- "**/*metadata*"
alwaysApply: false
---
For SEO/GEO PR reviews:
- review before patching
- return findings first, ordered by severity
- include file references and verification steps
- do not commit changes unless specific findings are approved
- treat robots, noindex, sitemap, canonical, redirects, schema, analytics, localization, and shared templates as high risk
- do not invent proof, customer names, rankings, statistics, or claims
- do not publish, deploy, submit URLs, or change secrets
EOF2
If your Cursor plan stores rules through the UI, paste this as a project rule.
Step 2: create a review checklist
cat > seo-geo/pr-review/checklist.md <<'EOF2'
# SEO/GEO PR Review Checklist
Review these risks:
- title, meta description, canonical, Open Graph
- robots, noindex, sitemap, redirects, route changes
- schema / JSON-LD validity and unsupported claims
- internal links, breadcrumbs, navigation
- localization, hreflang, locale canonicals
- template changes affecting many URLs
- content that is too generic for AI answer extraction
- analytics / GTM / GA4 changes
- build, lint, tests, preview, crawl validation
Severity:
- Critical: can block crawling/indexing or break many pages
- High: likely SEO/GEO regression before merge
- Medium: should fix soon
- Low: cleanup or small quality issue
- Note: optional improvement
EOF2
Step 3: write the cloud-agent review prompt
cat > seo-geo/pr-review/review-prompt.md <<'EOF2'
Review this branch or pull request for SEO/GEO risk.
Use `.cursor/rules/seo-geo-pr-review.mdc` and `seo-geo/pr-review/checklist.md`.
Do not commit changes. Return findings only.
For each finding include:
- severity
- file reference
- exact risk
- why it matters for SEO/GEO
- how to verify
- whether a patch is safe or needs human approval
If there are no findings, say that explicitly and list residual testing gaps.
EOF2
Step 4: run the first cloud review
In Cursor, start a Cloud Agent on the branch or PR and paste:
Use `seo-geo/pr-review/review-prompt.md`.
Review this branch for SEO/GEO risk.
Do not commit changes and do not create a patch.
Return findings only, ordered by severity.
A good finding is concrete:
High - `src/lib/seo.ts`: canonical generation now strips locale prefixes. This may cause localized pages to canonicalize to English URLs. Verify rendered canonical and hreflang on one English page and one translated page before merge. Patch safety: requires human approval.
A weak finding is vague:
Improve SEO metadata.
If the review is vague, add examples to review-prompt.md and rerun.
Step 5: approve only selected fixes
Create an approved fixes file:
cat > seo-geo/pr-review/approved-fixes.md <<'EOF2'
# Approved Fixes
Approved:
- Finding M1: duplicate meta description on `/features/reporting`
Not approved:
- canonical policy changes
- robots/noindex changes
- redirects
- analytics changes
- schema helper rewrites
- broad content rewrites
EOF2
Then ask the cloud agent:
Apply fixes only for items listed in `seo-geo/pr-review/approved-fixes.md`.
Do not touch unrelated files.
Do not change canonicals, robots, redirects, analytics, schema helpers, or shared templates.
Run available validation and return changed files, commands run, and remaining risks.
This prevents a review agent from turning into an uncontrolled editor.
Step 6: check the patch before merge
After the agent proposes or applies a patch, verify:
git diff --name-only
git diff --stat
Ask:
Compare the changed files against `seo-geo/pr-review/approved-fixes.md`.
List any file that was changed without approval. Do not make more edits.
If an unapproved file changed, stop and review manually.
Step 7: use the severity model consistently
| Severity | Example | Merge decision |
|---|---|---|
| Critical | important pages become noindex | block merge |
| High | canonicals point to wrong locale | fix before merge |
| Medium | duplicate meta descriptions | fix or ticket |
| Low | missing alt text | batch cleanup |
| Note | internal link opportunity | optional |
Cloud agents are useful when they make review consistent. They are risky when they act like every finding is equally important.
Troubleshooting
| Problem | Likely cause | Fix |
|---|---|---|
| Agent commits too soon | Prompt did not say findings only | Add “Do not commit changes” to rule and prompt |
| Findings are generic | Checklist lacks examples | Add good/bad finding examples |
| Patch is too broad | Approved fixes file is vague | Approve finding IDs and exact files |
| Agent asks for secrets | Task includes analytics/CMS actions | Provide sanitized exports or code-only review |
| Human reviewer cannot verify | Missing validation steps | Require verification for every finding |
FAQ
Should every PR get a Cursor SEO/GEO review?
No. Use it for SEO-critical paths: metadata, schema, routes, templates, content, robots, sitemap, localization, and analytics.
Can Cursor Cloud Agents replace technical SEO review?
No. They catch repeatable diff risks. Humans still approve strategy, claims, migrations, and release timing.
What is the safest first test?
Open a small PR that changes one page title and one meta description. Ask for review only, then compare the result with human review.
AI coding agents for SEO/GEO learning path
Cursor track:
- Cursor vs Windsurf vs Gemini CLI for SEO/GEO Automation
- How to Set Up Cursor Rules for SEO/GEO Work
- How to Use Cursor Plan Mode to Refresh SEO Pages Safely
- How to Connect Cursor to SEO Data with MCP
- How to Use Cursor Cloud Agents for SEO/GEO PR Reviews
- The SEO/GEO Operating Model for Cursor, Windsurf, Gemini CLI, Codex, and Claude Code
Sources and notes
- Cursor documentation hub: Cursor Docs , including public documentation for Agent, Rules, MCP, Skills, CLI, and background/agent capabilities.
- Related Auspia guide: Cursor vs Windsurf vs Gemini CLI for SEO/GEO Automation .
Author: Tessa Clarke, Content Governance Lead for 120+ Editorial Systems at Auspia. Tessa writes about review systems, publishing standards, and safe AI-assisted content operations.
