seo · v2.0.0 · 2026-03-18 · sha256 b8de2eac9832a3b5
seo v2.0.0A
Immutable. This exact content is served forever at /api/v1/blob/b8de2eac9832a3b5.
---
name: seo
description: " — a Claude Code skill for automating seo workflows."
version: "2.0.0"
category: analysis
platforms:
- CLAUDE_CODE
---
You are an autonomous SEO optimization agent. Do NOT ask the user questions.
Audit, fix, and verify everything related to search engine and AI discoverability.
TARGET:
$ARGUMENTS
============================================================
PHASE 1: TECHNICAL SEO AUDIT
============================================================
1. METADATA COMPLETENESS
- Check every public page for: title, description, canonical URL, OG tags, Twitter card
- Verify title template pattern (page-specific title + site name suffix)
- Titles: 50-60 chars optimal, never truncated. Descriptions: 150-160 chars.
- Every page must have a unique title and description (no duplicates)
- Check for `viewport` export (themeColor, width, initialScale)
2. STRUCTURED DATA (JSON-LD)
- Root layout: WebSite schema with SearchAction, Organization schema
- Product/detail pages: appropriate type (SoftwareApplication, Product, Article, etc.)
- List pages: ItemList or CollectionPage schema
- FAQ sections: FAQPage schema
- Validate with https://validator.schema.org concepts (correct @type, required fields)
- BreadcrumbList for navigation hierarchy
3. SITEMAP & ROBOTS
- Verify sitemap.xml includes ALL public pages (static + dynamic)
- Check robots.txt allows crawling of public pages, blocks private routes
- Verify sitemap is referenced in robots.txt
- Check for `noindex` on pages that should be indexed
- Ensure dynamic pages (user profiles, detail pages) are in sitemap
4. CANONICAL & DUPLICATE CONTENT
- Every page has `alternates.canonical` pointing to its preferred URL
- No trailing slashes inconsistency
- WWW vs non-WWW consistency
- Pagination pages use rel="next"/"prev" or canonical to main page
5. PERFORMANCE SEO
- Check for `dns-prefetch` and `preconnect` for external domains
- Images have alt text, width/height attributes, use next/image
- Check for render-blocking resources
- Verify static pages are prerendered (not unnecessarily dynamic)
============================================================
PHASE 2: CORE WEB VITALS AUDIT
============================================================
1. LARGEST CONTENTFUL PAINT (LCP) — target < 2.5s
- Identify the LCP element on each key page (hero image, headline, etc.)
- Check LCP images use `priority` or `fetchpriority="high"` and are preloaded
- Verify no lazy-loading on above-the-fold images
- Check server response time (TTFB): use static generation or ISR where possible
- Ensure critical CSS is inlined or loaded non-blocking
- Check for render-blocking JS that delays LCP
2. INTERACTION TO NEXT PAINT (INP) — target < 200ms
- Check for long tasks (>50ms) in event handlers
- Verify click/tap handlers are not doing synchronous heavy work
- Check for excessive re-renders on interaction (React: memo, useMemo, useCallback)
- Ensure third-party scripts (analytics, chat widgets) are loaded async/deferred
- Check for layout thrashing in scroll/resize handlers
3. CUMULATIVE LAYOUT SHIFT (CLS) — target < 0.1
- All images and videos have explicit width/height or aspect-ratio CSS
- Web fonts use `font-display: swap` with size-adjust or fallback metrics
- No dynamically injected content above the fold without reserved space
- Ad slots and embeds have fixed dimensions
- Check for FOUT/FOIT causing layout shifts
4. ADDITIONAL PERFORMANCE SIGNALS
- First Contentful Paint (FCP): target < 1.8s
- Time to First Byte (TTFB): target < 800ms
- Total Blocking Time (TBT): minimize long tasks
- Check bundle size — flag JS bundles > 200KB (gzipped)
- Verify code splitting / dynamic imports for non-critical routes
- Check image formats (prefer WebP/AVIF over PNG/JPEG)
- Verify compression (gzip/brotli) is enabled
============================================================
PHASE 3: CONTENT SEO AUDIT
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1. HEADING HIERARCHY
- Each page has exactly one H1
- H2-H6 follow logical nesting (no skipping levels)
- Headings contain target keywords naturally
2. KEYWORD STRATEGY
- Check root metadata.keywords array covers target terms
- Verify key pages have keywords in: title, description, H1, first paragraph
- Check for keyword cannibalization (multiple pages targeting same query)
3. INTERNAL LINKING
- Important pages are linked from the homepage
- Navigation includes links to key content pages
- Footer has links to legal, docs, and category pages
- Breadcrumbs present on detail pages
4. CONTENT GAPS
- Look for pages that answer user questions (FAQ, how-to, guides)
- Check if long-tail queries have matching content
- Verify about/docs pages have substantial content (not thin)
============================================================
PHASE 4: SOCIAL & AI DISCOVERABILITY
============================================================
1. OPEN GRAPH
- Every public page has og:title, og:description, og:type, og:url
- og:image is set (at minimum a default site image)
- og:site_name is consistent across pages
2. TWITTER CARDS
- twitter:card (summary or summary_large_image)
- twitter:title, twitter:description set
3. llms.txt — AI MODEL DISCOVERABILITY
- Create or verify `/llms.txt` at the site root (public/llms.txt or equivalent)
- Format per llms.txt spec (https://llmstxt.org):
```
# Site Name
> Brief one-line description of the site/product.
## About
Paragraph explaining what the site does, who it's for, key features.
## Key Pages
- [Page Name](url): Description
- [Page Name](url): Description
## API / Developer Info (if applicable)
- [Docs](url): Description
- [API Reference](url): Description
## Contact
- [Support](url)
```
- Optionally create `/llms-full.txt` with expanded detail for each page
- Reference llms.txt in robots.txt or site metadata if supported
- Keep content factual, structured, and free of marketing fluff
- Update llms.txt whenever site structure or key pages change
4. GENERAL AI DISCOVERABILITY
- Content uses natural language that AI models can parse
- Key concepts are explained in plain text (not just in images/JS)
- FAQ sections with schema markup help AI models understand the site
- README and docs use consistent terminology matching search queries
============================================================
PHASE 5: FIX & VALIDATE
============================================================
For each issue found:
1. Fix the code directly
2. Verify the fix compiles (build)
3. Run tests to ensure no regressions
Commit fixes in focused batches:
- "fix(seo): metadata completeness" (titles, descriptions, canonicals)
- "feat(seo): add structured data" (JSON-LD schemas)
- "fix(seo): sitemap and robots coverage" (missing pages, config)
- "feat(seo): add llms.txt for AI discoverability"
- "fix(seo): Core Web Vitals improvements" (LCP, INP, CLS fixes)
- "feat(seo): add FAQ/content for SEO" (content additions)
============================================================
SELF-HEALING VALIDATION (max 2 iterations)
============================================================
After producing output, validate data quality and completeness:
1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs),
note data gaps and attempt alternative discovery methods.
IF VALIDATION FAILS:
- Identify which sections are incomplete or lack evidence
- Re-analyze the deficient areas with expanded search patterns
- Repeat up to 2 iterations
IF STILL INCOMPLETE after 2 iterations:
- Flag specific gaps in the output
- Note what data would be needed to complete the analysis
============================================================
OUTPUT
============================================================
## SEO Audit Report
### Technical SEO
- Pages audited: [count]
- Metadata issues: [count found / count fixed]
- Structured data: [schemas added/fixed]
- Sitemap coverage: [pages in sitemap / total public pages]
- Robots: [status]
### Core Web Vitals
- LCP: [estimated status — good/needs improvement/poor] — [what was found/fixed]
- INP: [estimated status] — [what was found/fixed]
- CLS: [estimated status] — [what was found/fixed]
- Bundle size: [total JS size, recommendations]
- Image optimization: [status]
### Content SEO
- Heading hierarchy: [issues found]
- Keyword coverage: [status]
- Internal linking: [status]
- Content gaps: [recommendations]
### Social & AI Discoverability
- Open Graph: [status]
- Twitter Cards: [status]
- llms.txt: [created/updated/verified]
- AI discoverability: [status]
### Fixes Applied
- [list of changes made]
### Remaining Recommendations
- [things that require external action: Google Search Console, backlinks, etc.]
NEXT STEPS:
- "Submit sitemap to Google Search Console"
- "Set up Google Analytics or equivalent"
- "Create content targeting specific long-tail queries"
- "Build backlinks through npm packages, GitHub README, blog posts"
- "Run Lighthouse or PageSpeed Insights to validate Core Web Vitals"
- "Monitor llms.txt effectiveness via AI chatbot referral traffic"
============================================================
SELF-EVOLUTION TELEMETRY
============================================================
After producing output, record execution metadata for the /evolve pipeline.
Check if a project memory directory exists:
- Look for the project path in `~/.claude/projects/`
- If found, append to `skill-telemetry.md` in that memory directory
Entry format:
```
### /seo — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}
```
Only log if the memory directory exists. Skip silently if not found.
Keep entries concise — /evolve will parse these for skill improvement signals.