Agentic-SEO-Skill

Why use it

Key features

16 sub-skills: technical SEO, content quality, schema, performance, links, GEO/AEO, sitemap, hreflang, strategy
10 specialist agents (technical, content, performance, schema, sitemap, visual, GitHub, benchmarking, data, verification)
33 evidence-collector scripts (32 Python + 1 shell) for auditable findings
Runs on Antigravity (project-local), Claude Code (user-global), and Codex (user-global)
Enforces current Google signals: INP not FID, full E-E-A-T treatment

Live Demo

What it looks like in practice

agentic-seo-skill.replay ▶ ready

0/0

Install

Author repository · Claude Skills documentation

Use Cases

Real-world ways to use Agentic-SEO-Skill

Run an evidence-backed SEO audit with confidence labels

👤 SEO analysts tired of 'trust me' recommendations ⏱ ~60 min intermediate

When to use: You want findings you can defend to a skeptical engineering lead.

Flow

Trigger the full audit

Use agentic-seo-skill on https://site.com — full evidence-backed audit.✓ Copied

→ Scripts collect raw data (meta tags, schema, link graph, CWV); agents analyze
Review findings with proofs

Show the top 10 issues with their supporting evidence and confidence labels.✓ Copied

→ Each finding has a literal quote/data point behind it
Prioritized action plan

Order by impact × effort.✓ Copied

→ Ranked plan with numeric rationale

Outcome: An audit where every finding is citable.

Pitfalls

Low-confidence findings treated as high priority — Use the confidence labels — skip 'low' until high/medium are done

Combine with: firecrawl

Audit a GitHub-hosted docs site with the GitHub-analyst agent

👤 Dev-tool teams with docs on GitHub Pages ⏱ ~40 min intermediate

When to use: Your docs live in a repo and you want a SEO audit tied to the repo structure.

Flow

Point the GitHub agent at the repo

agentic-seo-skill — audit github.com/acme/docs site. Output GITHUB-SEO-REPORT.md.✓ Copied

→ Report in the repo format with per-file recommendations
Open a PR with fixes

Turn the high-confidence findings into a PR.✓ Copied

→ PR with concrete diffs

Outcome: A SEO-improved docs site with a trackable PR.

Pitfalls

PR touches too many files at once — Split by finding type (meta vs content vs schema)

Combine with: github

Optimize pages for Perplexity / ChatGPT / AI Overviews citations

👤 Content teams losing clicks to AI summaries ⏱ ~30 min intermediate

When to use: You want to be the cited source, not just another organic result.

Flow

Run the GEO/AEO sub-skill

agentic-seo-skill — GEO audit on https://site.com/post.✓ Copied

→ Findings tied to snippet-friendliness, entity clarity, citation signals
Apply and verify

Apply recommendations and re-verify.✓ Copied

→ Score deltas with evidence

Outcome: Pages restructured for AI-citation pickup.

Pitfalls

Over-optimization hurts human readability — The content-quality agent catches robotic-sounding edits

Combinations

Pair with other MCPs for X10 leverage

agentic-seo-skill + firecrawl

Firecrawl does the JS-rendered crawl; agentic-seo interprets

Crawl with firecrawl, pipe rendered HTML into agentic-seo-skill for analysis.✓ Copied

agentic-seo-skill + github

Open PRs directly from audit findings

For the high-confidence findings, open a PR in the repo.✓ Copied

agentic-seo-skill + claude-seo-skill

Use the /seo slash commands for fast runs, agentic-seo for evidence-backed depth

First do /seo audit for the quick view, then agentic-seo-skill for the deep defensible audit.✓ Copied

Tools

What this MCP exposes

Tool	Inputs	When to call
Technical SEO agent	URL	Baseline tech audit
Schema agent	URL	Structured data work
Performance agent	URL	CWV review
GitHub-analyst agent	repo URL	Audits on GitHub-hosted sites
Verification agent	prior findings	Before publishing audit

Cost & Limits

What this costs to run

API quota: None for the skill
Tokens per call: 10-30k for full audit — evidence collection uses scripts, not tokens, for the raw data
Monetary: Free — skill is local
Tip: Utility scripts are Python — run them as preflight outside the LLM loop to save tokens.

Security

Permissions, secrets, blast radius

Credential storage: No credentials — scripts hit public URLs only

Data egress: Only to the sites you audit

Utility scripts run on your machine — review before running on sensitive internal URLs.

Troubleshooting

Common errors and fixes

Utility scripts fail to run

Check Python version and required packages; install dependencies from the skill's requirements file.

Verify: python --version

Confidence labels all 'low'

Scripts couldn't collect enough raw evidence — check network access and JS rendering.

GitHub-analyst can't access repo

Set a PAT for private repos; public repos should work unauthenticated.

Alternatives