Agent skill

SEO Geo

by ericrisco in ericrisco/rsc-harness

A skill your agent uses when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web…

MITAuto-check passedMarketing & SEO

Install SEO Geo

skills CLI
$ npx skills add ericrisco/rsc-harness --skill seo-geo -a claude-code

Project install by default; add -g for ~/.claude/skills/.

GitHub CLI
$ gh skill install ericrisco/rsc-harness seo-geo --agent claude-code

Project scope by default; add --scope user for a personal install. Needs GitHub CLI 2.90.0 or later (public preview).

Manual copy
$ git clone --depth 1 https://github.com/ericrisco/rsc-harness.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/seo-geo .claude/skills/seo-geo && rm -rf skills-src

Use ~/.claude/skills/ instead of .claude/skills for a personal install. The folder must contain SKILL.md.

Claude Code skills documentation · loads skills from .claude/skills/

Facts

Skill name
seo-geo
GitHub stars
174
Token cost
~2.8k tokens
SKILL.md length
1,363 words
Files
6 (incl. scripts, references)
Skills in repo
233
Repo updated
First seen
Licence
MIT

At a glance

A skill your agent uses when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web…

  • Works in 5 steps: On-page SEO pass → Schema / JSON-LD — ship the live types,… → GEO pass — the measured citation levers → …
  • One existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO
  • SKILL.md covers When to use / When NOT to use, The one rule both surfaces share, 1. On-page SEO pass and 2. Schema / JSON-LD — ship the…, plus 5 more sections
  • Runs Shell scripts from its folder

What it does

SEO Geo is an agent skill from ericrisco/rsc-harness. Use when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web Vitals, crawlability, and AI-bot access in robots.txt. NOT writing the article body (that is article-writing), NOT keyword research or content strategy (that is content-engine).

Its SKILL.md is about 2.8k tokens, which your agent loads only when the skill is triggered. The skill folder holds 8 other files, including scripts and reference files (for example `evals/README.md`, `evals/cases.yaml` and `references/ai-crawler-control.md`).

It sits in Marketing & SEO, covering Schema markup, Technical SEO and Web performance. It works with Next.js. The repository describes itself as: Your agent invents things because it has no memory, and can't touch your database because it has no arms. rsc is the meta-harness that gives it both, plus the trade to know the… The licence is MIT.

When your agent uses it

  • One existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO
  • Structured-data JSON-LD
  • GEO citation levers
  • Core Web Vitals

Example prompts

  • “/seo-geo”

Requirements

  • A Bash shell

Workflow steps

5 steps, taken from the step headings in SKILL.md.

  1. On-page SEO pass
  2. Schema / JSON-LD — ship the live types, never the dead ones
  3. GEO pass — the measured citation levers
  4. Technical audit — measure, flag, hand off
  5. The audit checklist (the artifact you emit)

What it can do on your machine

Read from SKILL.md and the folder at commit e3d5b33. It shows what the files ask for, not the result of running them.

  • Tool permissions

    Pre-approves nothing: there is no allowed-tools line, so your agent's usual permission prompts apply.

    From allowed-tools in the SKILL.md frontmatter.

  • Runs code

    Ships 1 file in scripts/ (Shell), which the agent can run.

    From the folder's file list and the shell code blocks in SKILL.md.

  • Network

    No URLs in SKILL.md.

    From URLs in SKILL.md, links to its own repository left out.

  • Credentials

    Names no API keys, tokens, secrets or passwords.

    From names ending in _API_KEY, _TOKEN, _SECRET, _KEY or _PASSWORD in SKILL.md.

Context cost

SEO Geo loads about 2.8k tokens when it runs, and up to ~4.8k if it reads all its reference files. Until then it costs about 91 tokens; SKILL.md has 1,363 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~91
When it runs · the whole SKILL.md, loaded when a task matches
~2.8k
With references · SKILL.md plus every file in references/, read only if the agent opens them
~4.8k

Estimates: characters ÷ 4, the usual rule of thumb; real counts depend on the model's tokenizer. Scripts and assets cost tokens only if the agent reads them.

Safety

Auto-check passed

The automated check found no risky patterns in SKILL.md.

Automated static check — not a guarantee. Review scripts before installing. It scans the text of SKILL.md for risky patterns (piping downloads into a shell, reading credential files, hidden Unicode, destructive commands); the scripts in this folder are not scanned.

SKILL.md

The full file from ericrisco/rsc-harness at commit e3d5b33, republished under its MIT licence (© ericrisco). 1,363 words, ~2,820 tokens.

Download SKILL.mdSave it as .claude/skills/seo-geo/SKILL.md (or your agent's skills folder). This skill also uses 5 other files; get the full folder from GitHub.
name
seo-geo
description
Use when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web Vitals, crawlability, and AI-bot access in robots.txt. NOT writing the article body (that is `article-writing`), NOT keyword research or content strategy (that is `content-engine`).
tags
seo, geo, structured-data, schema-org, core-web-vitals, ai-search, technical-seo
recommends
content-engine, article-writing, landing-copy, performance, nextjs, accessibility, analytics
origin
risco

seo-geo — make one page findable in Google AND in AI answers

One audited page, two surfaces. You take an existing URL and make it rank in Google's blue links / AI Overviews and earn citations in ChatGPT Search, Perplexity, and Gemini — through meta, schema, answer-first structure, and a clean technical audit. You do not write the article body and you do not pick the keywords. That work belongs upstream.

When to use / When NOT to use

Use when the request is about a concrete artifact:

  • "Audit this page for SEO" / "why isn't this ranking?" / "make this page rank."
  • "Get us cited in ChatGPT / Perplexity / AI Overviews" / "optimize for AI search."
  • "Add schema markup" / "write the JSON-LD for this article/product."
  • "Our Core Web Vitals are failing" / crawlability / "should we block GPTBot?"

Do NOT use — redirect instead:

RequestOwner
Write the long-form article/blog bodyarticle-writing
Keyword research, topic clusters, content calendarcontent-engine
Write persuasive value-prop / CTA copylanding-copy
Actually fix a slow LCP/INP (render path, hydration, bundle)performance
Wire Next.js generateMetadata / app-router SEO plumbing../nextjs/SKILL.md
WAI-ARIA / keyboard / contrast auditaccessibility
Measure traffic / set up GA4 / dashboards after the factanalytics

The load-bearing distinction: content-engine decides what to write and which keywords to target (upstream strategy); you optimize the specific page that exists. performance owns why a render is slow; you only measure Core Web Vitals against the p75 thresholds, flag a fail, and hand off the fix.

The one rule both surfaces share

Answer the primary query completely in the first ~40–200 words. Everything below is downstream of this. Why: Google extracts that opening for featured snippets and AI Overviews, and the Princeton GEO study (KDD 2024, 10k queries across 8 domains) found answer-first, source-cited content is what AI engines lift into their answers — not buried prose. If the page makes a reader scroll past an intro to reach the answer, no amount of schema saves it.

There is no separate "AI SEO" toggle for Google. AI Overviews and AI Mode run on the same Googlebot crawl, the same rendering, the same ranking, and the same E-E-A-T / helpful-content signals (folded into core ranking in March 2024). Good page experience and distinguishable main content serve both surfaces at once. Treat them as one job.

1. On-page SEO pass

  • Title ≤ 60 chars, primary query near the front. Past ~60 chars Google truncates it.
  • Meta description ≤ 160 chars that earns the click — it is not a ranking factor, it is the ad copy for the result.
  • Exactly one <h1>, then a logical <h2>/<h3> hierarchy that mirrors search intent (the sub-questions a searcher actually has).
  • Internal links with descriptive anchors ("Core Web Vitals thresholds", not "click here") — anchor text is an entity signal both Google and AI engines read.
html
<!-- Bad: vague, query buried, no length discipline -->
<title>Home | Our Awesome Company - Welcome to the Best Site Ever for You</title>
<meta name="description" content="We are a company. Learn more about us here today.">

<!-- Good: query first, within limits, earns the click -->
<title>Core Web Vitals: LCP, INP &amp; CLS Thresholds (2026)</title>
<meta name="description"
  content="The exact LCP, INP, and CLS pass thresholds Google uses at the p75 field
  percentile — plus how to check yours in 2 minutes.">

2. Schema / JSON-LD — ship the live types, never the dead ones

Pick from the live column. Shipping a deprecated type is dead markup: it renders nothing and signals carelessness. The single highest-value guard in this skill is not emitting a dead @type.

Page typeShip this JSON-LDWhy
Article / blog postArticle or BlogPosting — headline, author, datePublished, dateModified, imageStill eligible; dateModified is a freshness signal
Product pageProduct + Offer — price, priceCurrency, availability, optional aggregateRatingLive rich result
Any pageBreadcrumbList, Organization / WebSiteEntity signals AI engines corroborate against
FAQ sectionNOTHING for rich resultsFAQ rich results restricted to health/gov since late 2023; full deprecation May 7 2026, API removed Aug 2026. Keep Q&A as visible prose.
Course / event-announcement / claim-review / salary / vehicle / learning-videoNOTHINGRetired June 2025 — dead markup

Validate every block with the Rich Results Test and the Schema Markup Validator (validator.schema.org) before shipping. Copy-paste blocks with required-vs-recommended props and the full dead-types list are in references/schema-recipes.md.

3. GEO pass — the measured citation levers

The Princeton GEO study quantified what moves source content into AI answers. Apply these as concrete edits — they are not vibes, they are deltas measured on GEO-bench:

  • Cited sources inline (~+30% visibility) — link to authoritative origins for claims.
  • Statistics (~+32%) — one verifiable number per ~150–200 words, with its source.
  • Quotations (~+41%, the biggest lever) — quote a named expert or primary document.
  • Fluency (~+28%) — clean, readable prose. Keyword stuffing did NOT help in the study; stuffing is pure cost.
  • Freshness — update real content and dateModified. Recency is a genuine AI-source signal; a stale-looking page is corroborated less.
  • Entity corroboration — earn mentions on independent authoritative domains so AI engines can cross-check who you are. This is the off-page half of GEO.

Why it is no longer optional: a citation in an AI Overview can lift CTR 80%+, while AI Overviews are compressing classic organic CTR (Gartner projected ~25% organic-traffic decline by 2026). GEO runs alongside on-page SEO, never instead of it.

Myth callout — llms.txt. Do not sell llms.txt as a ranking or AI-visibility signal. Google (Illyes, Mueller) does not use it and likened it to the long-ignored keywords meta tag; the major AI crawlers overwhelmingly skip /llms.txt and crawl HTML directly. AI access is governed by robots.txt + real on-page authority, not a root text file. Spending effort there is the modern keyword-meta-tag mistake.

Show full SKILL.md (524 more words)Show less

4. Technical audit — measure, flag, hand off

Core Web Vitals (thresholds unchanged since INP replaced FID in March 2024):

MetricGood (p75)
LCP≤ 2.5s
INP≤ 200ms
CLS≤ 0.1

A URL passes only if ≥75% of real visits hit "good" at the p75 of the CrUX field dataset. Lab tools (Lighthouse) estimate; CrUX / field decides. A green Lighthouse score with a failing CrUX p75 is still a fail. Measure and flag here — hand the actual render-path fix to performance (and to ../nextjs/SKILL.md for App Router apps). Do not guess at LCP fixes in this skill.

Crawlability / indexability: correct <link rel="canonical">, a referenced sitemap.xml, no stray noindex/Disallow on the page you want ranked, robots.txt present and intentional.

AI-bot access (per purpose): robots.txt controls AI crawlers separately by purpose, and the choice has visibility consequences.

  • Blocking the search bot (OAI-SearchBot, PerplexityBot, Claude-SearchBot) removes you from that engine's citations. Block only the training bot (GPTBot, ClaudeBot, CCBot) if you want citations but not training-corpus use.
  • Google-Extended opts out of Google AI training — but AI Overviews are served by ordinary Googlebot, so you cannot opt out of AI Overviews without leaving Search.

The full 2026 user-agent table and robots.txt templates are in references/ai-crawler-control.md.

5. The audit checklist (the artifact you emit)

Emit this as a pass/fail list per page. This is what scripts/verify.sh lints.

  • <title> ≤ 60 chars, query near front; meta description ≤ 160.
  • Exactly one <h1>; H2/H3 hierarchy matches intent.
  • Primary query answered in the first ~40–200 words.
  • JSON-LD: valid JSON, has @context + @type, no dead type, validates in Rich Results Test.
  • ≥2 GEO levers present (statistic / quotation / cited source / fluency).
  • CWV stated at p75 field thresholds, with pass/fail + handoff note if failing.
  • canonical + sitemap correct; no accidental noindex.
  • robots.txt does not block a search bot while the page claims AI visibility.

Anti-patterns

Anti-patternWhy it failsDo instead
Shipping FAQPage / Course / Vehicle JSON-LD for rich resultsDeprecated (FAQ dead May 2026; seven types retired June 2025) — dead markupArticle/Product/Breadcrumb; keep Q&A as visible prose
Treating GEO as a separate job from Google SEOAI Overviews use the same Googlebot / ranking / E-E-A-TOne audited page serves both surfaces
Adding llms.txt to "rank in AI"Google ignores it; AI bots skip itFix robots.txt + earn real authority/citations
Burying the answer under intro fluffSnippets & AI extraction need answer-firstAnswer the query in the first ~40–200 words
Keyword stuffing for AI citationPrinceton: stuffing didn't help; quotes/stats/cites didAdd statistics, quotations, cited sources
Blocking OAI-SearchBot/PerplexityBot, then expecting AI citationsSearch bots feed the citationsBlock only training bots if you want citations
Calling CWV "passed" off a green Lighthouse scoreCrUX p75 field decides, not the labRead CrUX / field at p75; lab only estimates
Debugging an LCP regression inside this skillRender-path work belongs to performanceFlag the fail, hand to performance

Handoff

When the audit hits a boundary, name the owner and stop:

  • Keywords, clusters, briefs, "what should we write?" → content-engine.
  • The actual article/body draft → ../article-writing/SKILL.md.
  • Persuasive value-prop / CTA copy → landing-copy.
  • Fixing a CWV fail (render, hydration, bundle) → performance.
  • Wiring generateMetadata / sitemap route / app-router SEO → ../nextjs/SKILL.md.
  • WAI-ARIA / contrast / keyboard a11y → accessibility.
  • Measuring traffic / GA4 / Search Console dashboards after → analytics.

© ericrisco, MIT. Rendered from Markdown: HTML in the file is shown as text, images as links, and headings moved down two levels. Raw file

Files

SKILL.md and 5 other files (scripts, references) in skills/seo-geo of ericrisco/rsc-harness.

  • SKILL.md
  • evals/README.md
  • evals/cases.yaml
  • references/ai-crawler-control.md
  • references/schema-recipes.md
  • scripts/verify.sh

Open the folder on GitHubat commit e3d5b33

Compare with similar skills

SEO Geo next to the 5 skills that share the most tags, products or categories with it. Stars are the repository's; “used in” counts other GitHub owners with a copy.

SEO Geo compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
SEO Geo this skillericrisco/rsc-harness174—~2.8kAutomated safety check: PassMIT
Persian SEOTronIsHere/vibefarsiui210—~1.7kAutomated safety check: WarnNone
SEOgridaco/grida2.7k—~2.1kAutomated safety check: PassApache-2.0
SEOaffaan-m/ECC276k2 repos~1.1kAutomated safety check: PassMIT
SEO AI Optimizerluongnv89/skills131—~2.7kAutomated safety check: PassMIT
SEO Optimizeraiskillstore/marketplace4301 repos~2.9kAutomated safety check: PassNone

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Works with

Categories

Questions about SEO Geo

What does SEO Geo do?

A skill your agent uses when one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO, structured-data JSON-LD, GEO citation levers, Core Web…. SEO Geo is an agent skill from ericrisco/rsc-harness.txt.

When should I use SEO Geo?

SEO Geo fits situations like: one existing page needs to rank in Google AND get cited by AI answer engines — auditing a URL for on-page SEO; structured-data JSON-LD; GEO citation levers; core Web Vitals.

How do I install SEO Geo in Claude Code?

Run `npx skills add ericrisco/rsc-harness --skill seo-geo -a claude-code`. Or copy the skill folder (skills/seo-geo in ericrisco/rsc-harness) into .claude/skills/seo-geo in your project. Claude Code loads it when a task matches its description.

How do I install SEO Geo in Codex?

Run `npx skills add ericrisco/rsc-harness --skill seo-geo -a codex`. Or copy the skill folder (skills/seo-geo in ericrisco/rsc-harness) into .agents/skills/seo-geo in your project. Codex loads it when a task matches its description.

Can I use SEO Geo in Cursor, Gemini CLI or GitHub Copilot?

Cursor, Gemini CLI, GitHub Copilot and OpenCode also load SKILL.md folders. With the skills CLI, run `npx skills add ericrisco/rsc-harness --skill seo-geo -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/seo-geo, .gemini/skills/seo-geo, .github/skills/seo-geo and .opencode/skills/seo-geo in your project.

What does SEO Geo need to run?

Going by SKILL.md and its folder, SEO Geo needs a shell for the scripts in its folder. Our summary lists: A Bash shell.

Does SEO Geo access the network?

SKILL.md contains no URLs. Any network use would come from the scripts or tools the agent runs. This is read from the text; nothing was executed.

Is SEO Geo safe to install?

Our automated static check of SKILL.md found no risky patterns, such as piping downloads into a shell, reading credential files or hidden Unicode. It is not a guarantee. The check reads SKILL.md only: the scripts in the folder are not scanned, so read them before running anything.

What licence does SEO Geo use?

SEO Geo is published under the MIT licence (the repository's licence). It allows redistribution, so the full SKILL.md is shown on this page.

How many tokens does SEO Geo use?

About 2.8k tokens (SKILL.md is roughly 11k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full. Its references folder adds about 2k tokens, read only when the agent opens those files.

What are the alternatives to SEO Geo?

Skills that share tags, products or a category with SEO Geo: Persian SEO (TronIsHere/vibefarsiui, 210 stars), SEO (gridaco/grida, 2.7k stars), SEO (affaan-m/ECC, 276k stars) and SEO AI Optimizer (luongnv89/skills, 131 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains SEO Geo?

ericrisco (a GitHub user) maintains it in ericrisco/rsc-harness, which has 174 GitHub stars. The repository holds 233 skills in this directory. The repository was last updated on October 7, 2026.

Source: ericrisco/rsc-harness on GitHub. Facts on this page come from the repository at the commit we read; the author's words are quoted as theirs.