agentic-seo-skill

name	agentic-seo-skill
description	LLM-first SEO analysis skill with 16 sub-skills, 10 specialist agents, and 89 evidence collection scripts for comprehensive SEO audits
triggers	["analyze this website's SEO","run a full SEO audit","check technical SEO issues","optimize this page for search engines","generate SEO recommendations","validate schema markup","audit Core Web Vitals","create GitHub repository SEO report"]

Agentic SEO Skill

Skill by ara.so — AI Agent Skills collection.

The Agentic SEO Skill is an LLM-first SEO analysis framework designed for AI coding assistants and agent IDEs. It provides 16 specialized sub-skills, 10 specialist agents, and 89 Python scripts for evidence-based SEO analysis. The skill follows a reasoning-first workflow: collect evidence, analyze with LLM proof, apply confidence labels, prioritize by impact, and produce actionable plans.

Installation

Quick Install (No Cloning Required)

Linux/macOS:

# Install to all supported IDEs
curl -fsSL https://raw.githubusercontent.com/Bhanunamikaze/Agentic-SEO-Skill/main/install.sh | bash -s -- --online

# Install to Claude Code only
curl -fsSL https://raw.githubusercontent.com/Bhanunamikaze/Agentic-SEO-Skill/main/install.sh | bash -s -- --online --target claude

# Install to specific project
curl -fsSL https://raw.githubusercontent.com/Bhanunamikaze/Agentic-SEO-Skill/main/install.sh | bash -s -- --online --target all --project-dir /path/to/project

Windows (PowerShell 7+):

irm https://raw.githubusercontent.com/Bhanunamikaze/Agentic-SEO-Skill/main/install.ps1 -OutFile install.ps1
powershell -ExecutionPolicy Bypass -File .\install.ps1 --online

From Source

git clone https://github.com/Bhanunamikaze/Agentic-SEO-Skill.git
cd Agentic-SEO-Skill

# Install to Claude Code with dependencies
bash install.sh --target claude --install-deps

# Install with Playwright for visual analysis
bash install.sh --target claude --install-deps --install-playwright

Installation Targets

Target	Location	Format
claude	~/.claude/skills/seo	Skill directory
codex	~/.codex/skills/seo	Skill directory
cursor	<project>/.cursor/rules/seo.mdc	MDC rule
windsurf	<project>/.windsurf/rules/seo.md	Windsurf rule
copilot	<project>/.github/copilot-instructions.md	Repo instructions
cline	<project>/.clinerules	Project rules
continue	<project>/.continue/prompts/seo.prompt	Slash command
antigravity	<project>/.agent/skills/seo	Skill directory

Core Workflow

The skill follows an LLM-first analysis pattern:

1. Collect Evidence — Use read_url_content MCP tool or evidence collection scripts
2. Analyze with LLM — Apply explicit proof and reasoning for each finding
3. Label Confidence — Mark findings as Confirmed, Likely, or Hypothesis
4. Prioritize — Rank by impact and implementation effort
5. Generate Action Plan — Produce structured, evidence-backed recommendations

All audits must follow the resources/references/llm-audit-rubric.md standard.

16 Specialized Sub-Skills

1. SEO Audit (Full Site)

Run comprehensive site-wide audits with evidence-backed scoring:

# Quick full audit with all outputs
python3 scripts/audit_runner.py https://example.com

# Outputs:
# - audit_results.json
# - audit_report.html
# - FULL-AUDIT-REPORT.md
# - ACTION-PLAN.md

Agent prompt:

Use the seo audit sub-skill to analyze https://example.com — follow the LLM audit rubric, collect evidence first with read_url_content, then reason through technical, content, and performance issues.

2. SEO Page (Single Page Analysis)

Deep analysis of individual pages:

# Fetch page content
python3 scripts/fetch_page.py https://example.com/page

# Parse HTML for SEO signals
python3 scripts/parse_html.py example_com_page.html

# Check Core Web Vitals
python3 scripts/pagespeed.py https://example.com/page

Agent prompt:

Analyze this single page for SEO: https://example.com/about — check title, meta description, headings hierarchy, internal links, schema markup, and Core Web Vitals.

3. SEO Technical

Crawlability, indexability, security, mobile-friendliness, and AI crawler policies:

# Check robots.txt policies
python3 scripts/robots_checker.py https://example.com

# Run indexability matrix (robots, meta robots, canonicals, status codes)
python3 scripts/indexability_matrix.py https://example.com

# Multi-page crawl audit
python3 scripts/crawl_audit.py https://example.com --max-depth 3

Agent prompt:

Run a technical SEO audit on https://example.com — check robots.txt, XML sitemaps, HTTPS, mobile responsiveness, JS rendering, and AI crawler access (GPTBot, ClaudeBot).

4. SEO Content

Content quality assessment with E-E-A-T framework (September 2025 Quality Rater Guidelines):

# Extract article content
python3 scripts/fetch_page.py https://example.com/article

# Run content quality checks (agent analyzes with LLM)
# Check: word count, readability, E-E-A-T signals, topical depth

Agent prompt:

Assess content quality for https://example.com/blog/post using E-E-A-T criteria — check expertise signals, author credentials, content depth, readability, and December 2025 core update alignment.

5. SEO Schema

Schema.org detection, validation, and JSON-LD generation:

# Validate existing schema markup
python3 scripts/validate_schema.py example_com.html

# Check for deprecated schema types
python3 scripts/parse_html.py example_com.html --extract-schema

Agent prompt:

Validate schema markup on https://example.com — check JSON-LD syntax, required fields, deprecated types, and suggest appropriate schema.org types (Article, Product, LocalBusiness, etc.).

Example JSON-LD generation:

# Agent generates schema based on page content
schema = {
    "@context": "https://schema.org",
    "@type": "Article",
    "headline": "Page Title",
    "author": {
        "@type": "Person",
        "name": "Author Name"
    },
    "datePublished": "2026-01-15",
    "dateModified": "2026-05-27",
    "image": "https://example.com/image.jpg",
    "publisher": {
        "@type": "Organization",
        "name": "Publisher Name",
        "logo": {
            "@type": "ImageObject",
            "url": "https://example.com/logo.png"
        }
    }
}

6. SEO Sitemap

XML sitemap validation and generation:

# Discover and validate sitemaps
python3 scripts/sitemap_checker.py https://example.com

# Check for issues: 404s, lastmod quality, URL limits (50,000 per file)

Agent prompt:

Audit XML sitemaps for https://example.com — check discovery (robots.txt reference), URL limits, 404s, lastmod accuracy, and sitemap index structure.

7. SEO Images

Image optimization audit (alt text, formats, lazy loading, CLS):

# Generate image inventory
python3 scripts/image_inventory.py https://example.com

# Outputs: alt text coverage, dimensions, loading behavior, LCP candidates

Agent prompt:

Analyze image SEO for https://example.com — check alt text, file formats (WebP), lazy loading, responsive images (srcset), and LCP image optimization.

8. SEO GEO (Generative Engine Optimization)

Optimize for AI Overviews, ChatGPT, Perplexity:

Agent prompt:

Optimize this content for AI search engines (ChatGPT, Perplexity, Google AI Overviews) — add clear definitions, structured facts, citations, and FAQ sections.

Pattern:

• Use concise definitions (20-30 words)
• Add structured lists and tables
• Include citations and sources
• Create FAQ sections for common queries

9. SEO AEO (Answer Engine Optimization)

Optimize for Featured Snippets, People Also Ask, Knowledge Panels:

Agent prompt:

Optimize for Featured Snippets — structure content with clear question headers, concise answers (40-60 words), and bulleted/numbered lists.

Pattern:

## What is [Topic]?

[Topic] is [concise 40-60 word definition].

### Key Benefits:
1. Benefit one
2. Benefit two
3. Benefit three

10. SEO Links

Link profile analysis — internal links, backlinks, anchor text, orphan pages:

# Extract all links from page
python3 scripts/parse_html.py example_com.html --extract-links

# Agent analyzes: internal link structure, anchor text distribution, orphan pages

Agent prompt:

Audit internal linking structure for https://example.com — identify orphan pages, check anchor text diversity, analyze link depth, and suggest hub pages.

11. SEO Programmatic

Quality gates for programmatic SEO pages:

Agent prompt:

Review programmatic SEO pages for quality — check for thin content, duplicate patterns, template issues, and unique value signals on each page.

Quality gates:

• Minimum 800 words unique content per page
• No placeholder text (e.g., "[City]", "Lorem ipsum")
• Unique titles and meta descriptions
• 3+ internal links to related pages

12. SEO Competitors

Comparison and alternatives page generation:

Agent prompt:

Generate a "Competitors" or "Alternatives" comparison page for [Product] — include feature tables, pricing comparison, use case fit, and neutral tone.

13. SEO Hreflang

International SEO and hreflang validation:

# Parse hreflang tags
python3 scripts/parse_html.py example_com.html --extract-hreflang

Agent prompt:

Validate hreflang implementation for multi-language site — check bidirectional links, correct language codes (ISO 639-1), regional variants (en-US, en-GB), and x-default fallback.

14. SEO Plan

Strategic SEO planning with topical clusters and industry templates:

Agent prompt:

Create an SEO content strategy for a SaaS product — build topical clusters, identify pillar pages, keyword themes, and internal linking structure.

Industry templates available:

• SaaS
• E-commerce
• Local Business
• Publisher/Media
• Agency
• Generic

15. SEO GitHub

GitHub repository SEO optimization:

# Generate GitHub SEO report
python3 scripts/github_seo_report.py owner/repo

# Outputs:
# - GITHUB-SEO-REPORT.md
# - GITHUB-ACTION-PLAN.md

Agent prompt:

Optimize GitHub repository SEO for [owner/repo] — improve description, add topics, enhance README structure, optimize title/headline, and benchmark against query results.

Key checks:

• Repository description (160 chars, keyword-rich)
• Topics (8-12 relevant tags)
• README structure (clear headline, features, installation)
• Social preview image
• Community health files (CODE_OF_CONDUCT.md, CONTRIBUTING.md)

16. SEO Article

Article data extraction and LLM-driven content optimization:

Agent prompt:

Extract article content and optimize — check headline, readability, keyword density, internal links, meta description, and suggest improvements.

10 Specialist Agents

The skill includes specialized agents for domain-specific analysis:

1. Technical SEO Agent — Crawlability, indexability, security, mobile, JS rendering
2. Content Quality Agent — E-E-A-T scoring, AI content detection
3. Performance Agent — Core Web Vitals (LCP, INP, CLS)
4. Schema Markup Agent — JSON-LD validation and generation
5. Sitemap Agent — XML sitemap quality gates
6. Visual Analysis Agent — Screenshots, above-the-fold analysis (requires Playwright)
7. GitHub Analyst Agent — Repository metadata and README optimization
8. GitHub Benchmark Agent — Query ranking and competitor intelligence
9. GitHub Data Agent — API fallback and traffic archival
10. Verifier Agent (Global) — Deduplication and contradiction suppression

Key Scripts Reference

Evidence Collection

# Fetch page with SEO crawler headers
python3 scripts/fetch_page.py https://example.com
# Output: example_com.html

# Parse HTML for all SEO signals
python3 scripts/parse_html.py example_com.html
# Extracts: title, meta, headings, links, images, schema, canonical

# Check robots.txt and crawler policies
python3 scripts/robots_checker.py https://example.com

# Multi-page crawl
python3 scripts/crawl_audit.py https://example.com --max-depth 2 --max-pages 50

Performance & Core Web Vitals

# PageSpeed Insights + Core Web Vitals
python3 scripts/pagespeed.py https://example.com
# Returns: LCP, INP, CLS scores + recommendations

# Note: Requires PAGESPEED_API_KEY environment variable
# Get free key: https://developers.google.com/speed/docs/insights/v5/get-started

Indexability & Crawlability

# Comprehensive indexability matrix
python3 scripts/indexability_matrix.py https://example.com
# Checks: robots.txt, meta robots, X-Robots-Tag, canonical, status codes, sitemaps

# Sitemap validation
python3 scripts/sitemap_checker.py https://example.com
# Validates: URL limits, 404s, lastmod, discovery

Schema Validation

# Validate JSON-LD schema
python3 scripts/validate_schema.py page.html
# Checks: syntax, required fields, deprecated types, placeholders

Report Generation

# Full audit with all outputs
python3 scripts/audit_runner.py https://example.com

# Generate HTML dashboard
python3 scripts/generate_report.py audit_results.json --output seo-report.html

# Verify findings (deduplicate + prioritize)
python3 scripts/finding_verifier.py audit_results.json

Configuration

Environment Variables

# PageSpeed Insights API key (optional but recommended)
export PAGESPEED_API_KEY=your_api_key_here

# GitHub token for repository analysis (optional)
export GITHUB_TOKEN=your_github_token_here

Script Dependencies

Install Python dependencies:

# From project root
pip install -r requirements.txt

# Or with installer flag
bash install.sh --target claude --install-deps

Core dependencies:

• requests — HTTP requests
• beautifulsoup4 — HTML parsing
• lxml — Fast XML/HTML parser
• playwright (optional) — Visual analysis and screenshots

Install Playwright browsers:

playwright install chromium
# Or use installer flag
bash install.sh --target claude --install-deps --install-playwright

Common Patterns

Pattern 1: Quick Page Audit

# 1. Fetch page
python3 scripts/fetch_page.py https://example.com/page

# 2. Parse HTML
python3 scripts/parse_html.py example_com_page.html > page_data.json

# 3. Check Core Web Vitals
python3 scripts/pagespeed.py https://example.com/page > cwv_data.json

# 4. Agent analyzes JSON outputs with LLM reasoning

Agent prompt:

Analyze page_data.json and cwv_data.json — identify Critical/Warning/Pass findings, apply confidence labels, prioritize by impact, output structured action plan following llm-audit-rubric.md.

Pattern 2: Multi-Page Crawl Audit

# 1. Crawl site (respects robots.txt)
python3 scripts/crawl_audit.py https://example.com --max-depth 3 --max-pages 100 > crawl_results.json

# 2. Generate indexability matrix
python3 scripts/indexability_matrix.py https://example.com > indexability.json

# 3. Agent identifies issues: orphan pages, redirect chains, indexability blocks

Pattern 3: Schema Validation & Generation

# 1. Parse existing schema
python3 scripts/parse_html.py page.html --extract-schema > existing_schema.json

# 2. Validate
python3 scripts/validate_schema.py page.html

# 3. Agent generates improved schema based on page content and validation errors

Pattern 4: GitHub Repository SEO

# Generate full GitHub SEO report
python3 scripts/github_seo_report.py owner/repo

# Outputs:
# - GITHUB-SEO-REPORT.md (current state + benchmarks)
# - GITHUB-ACTION-PLAN.md (prioritized improvements)

Agent workflow:

1. Check repository metadata (description, topics, social preview)
2. Analyze README structure and keyword optimization
3. Benchmark title against search results
4. Suggest query-aligned title improvements
5. Identify missing community health files

Pattern 5: Full Site Audit with Reports

# One command generates all outputs
python3 scripts/audit_runner.py https://example.com

# Generates:
# 1. audit_results.json (structured data)
# 2. audit_report.html (interactive dashboard)
# 3. FULL-AUDIT-REPORT.md (markdown report)
# 4. ACTION-PLAN.md (prioritized tasks)

LLM Audit Rubric

All SEO analyses must follow the standard rubric at resources/references/llm-audit-rubric.md:

Finding Format

### [Severity]: [Finding Title]

**Evidence:**
[Specific, verifiable proof — URLs, code snippets, metrics]

**Impact:**
[SEO consequence — traffic, rankings, crawlability, user experience]

**Confidence:**
- Confirmed / Likely / Hypothesis

**Fix:**
[Actionable, specific remediation steps]

**Priority:**
- Impact: High / Medium / Low
- Effort: High / Medium / Low

Severity Levels

• Critical — Blocks indexing, causes crawl errors, security issues
• Warning — Degrades SEO performance, suboptimal implementation
• Pass — Meets best practices
• Info — Opportunities, neutral observations

Confidence Labels

• Confirmed — Direct evidence (status codes, parsed HTML, API responses)
• Likely — Strong inference from multiple signals
• Hypothesis — Reasoned speculation requiring validation

Troubleshooting

Script Errors

Issue: ModuleNotFoundError: No module named 'requests'

Fix:

pip install -r requirements.txt
# Or
pip install requests beautifulsoup4 lxml

---

Issue: playwright._impl._errors.Error: Executable doesn't exist

Fix:

pip install playwright
playwright install chromium

---

Issue: PageSpeed API rate limit (403 or 429 errors)

Fix:

# Get free API key (100 queries/day)
# https://developers.google.com/speed/docs/insights/v5/get-started

export PAGESPEED_API_KEY=your_key_here
python3 scripts/pagespeed.py https://example.com

Installation Issues

Issue: Skill not detected after installation

Fix:

# Verify installation path
ls -la ~/.claude/skills/seo/

# Restart IDE
# Claude Code: restart extension
# Cursor: restart application

# Re-run installer with verbose output
bash install.sh --target claude -v

---

Issue: Windows PowerShell execution policy error

Fix:

# Run once per session
Set-ExecutionPolicy -Scope Process -ExecutionPolicy Bypass

# Then run installer
.\install.ps1 --online --target claude

Audit Quality Issues

Issue: Too many low-confidence findings

Fix:

# Run finding verifier to deduplicate and prioritize
python3 scripts/finding_verifier.py audit_results.json

# Agent: focus on Confirmed/Likely findings first, mark Hypothesis clearly

---

Issue: Missing evidence in audit reports

Fix:

• Always collect evidence first (fetch_page.py, parse_html.py, pagespeed.py)
• Reference specific URLs, line numbers, metric values
• Use read_url_content MCP tool before reasoning
• Apply llm-audit-rubric.md standard for every finding

Reference Data

The skill includes up-to-date reference files (checked by CI):

• Core Web Vitals — LCP (2.5s), INP (200ms), CLS (0.1)
• E-E-A-T Framework — September 2025 Quality Rater Guidelines
• Schema.org Types — Active, restricted, deprecated lists
• Content Quality Gates — Word count minimums, readability thresholds
• Google SEO Quick Reference — Best practices snapshot
• LLM Audit Rubric — Standardized output contract

Reference freshness is validated with:

python3 scripts/reference_freshness.py resources/references --max-age-days 90

Advanced Usage

Custom Industry Strategy

# Agent generates topical cluster strategy
# Example for SaaS product:

# Pillar page: /seo-tools
# Cluster pages:
#   /seo-tools/keyword-research
#   /seo-tools/link-building
#   /seo-tools/technical-audit
# Each cluster page links back to pillar

# Agent uses industry template from:
# resources/skills/seo-plan.md

Multi-Language Hreflang

<!-- Agent generates hreflang tags -->
<link rel="alternate" hreflang="en" href="https://example.com/page" />
<link rel="alternate" hreflang="es" href="https://example.com/es/page" />
<link rel="alternate" hreflang="de" href="https://example.com/de/page" />
<link rel="alternate" hreflang="x-default" href="https://example.com/page" />

Validation checklist:

• Bidirectional links (each page references all others)
• Correct ISO 639-1 language codes
• Regional variants (en-US, en-GB) where applicable
• x-default fallback for unmatched locales
• Self-referential link included

Programmatic SEO Quality Gates

# Agent validates programmatic pages before deployment

quality_gates = {
    "min_words": 800,
    "unique_title": True,
    "unique_meta_description": True,
    "no_placeholders": True,
    "min_internal_links": 3,
    "unique_content_ratio": 0.7  # 70% unique vs template
}

# Reject pages that fail gates
# Log failures for content team review

Wiki & Documentation

Full documentation available at: https://github.com/Bhanunamikaze/Agentic-SEO-Skill/wiki

Key wiki pages:

• Script Inventory — All 89 scripts with purpose notes
• Installation Guide — Detailed per-IDE setup
• Example Prompts — 50+ tested agent prompts
• Troubleshooting — Common issues and fixes
• Report Generation — Custom report templates

License

MIT License — free for commercial and personal use.