Agent skill

Geo Audit

by vellum-ai in vellum-ai/vellum-assistant

Runs a one-command technical GEO audit on any domain. An agent skill from vellum-ai/vellum-assistant.

MITAuto-check passedMarketing & SEO

Install Geo Audit

skills CLI
$ npx skills add vellum-ai/vellum-assistant --skill geo-audit -a claude-code

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

GitHub CLI
$ gh skill install vellum-ai/vellum-assistant geo-audit --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/vellum-ai/vellum-assistant.git skills-src && mkdir -p .claude/skills && cp -r skills-src/skills/geo-audit .claude/skills/geo-audit && 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
geo-audit
GitHub stars
1.4k
Token cost
~2k tokens
SKILL.md length
959 words
Files
2 (incl. scripts)
Skills in repo
108
Repo updated
First seen
Licence
MIT

At a glance

Runs a one-command technical GEO audit on any domain. An agent skill from vellum-ai/vellum-assistant.

  • Works in 6 steps: AI crawler access via robots.txt (25 pts) → llms.txt presence and shape (15 pts) → Server-side rendering (20 pts) → …
  • Tasks that involve AI search optimization
  • SKILL.md covers RUNNING AN AUDIT, WHEN TO USE THIS SKILL, USAGE and WHAT IT CHECKS, plus 4 more sections
  • Runs Python scripts from its folder; calls python3; reaches vellum.ai and stripe.com

What it does

Geo Audit is an agent skill from vellum-ai/vellum-assistant. Runs a one-command technical GEO audit on any domain. Checks AI crawler access, llms.txt presence, server-side rendering, sitemap, and schema markup. Streams results live and ends with a 0–100 score plus the top 3 prioritized fixes. Built to be both genuinely useful and great to demo on camera.

Its SKILL.md is about 2k tokens, which your agent loads only when the skill is triggered. The skill folder holds 2 other files, including scripts (for example `scripts/audit.py`).

It sits in Marketing & SEO, covering AI search optimization, Web scraping and Schema markup. The repository describes itself as: An AI Assistant that’s easy to setup, does your work 24/7, knows your preferences and gets better over time. The licence is MIT.

When your agent uses it

  • Tasks that involve AI search optimization
  • Tasks that involve Web scraping
  • Tasks that involve Schema markup

Example prompts

  • “/geo-audit”

Requirements

  • Python 3

Workflow steps

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

  1. AI crawler access via robots.txt (25 pts)
  2. llms.txt presence and shape (15 pts)
  3. Server-side rendering (20 pts)
  4. sitemap.xml (10 pts)
  5. Schema markup on homepage (15 pts)
  6. Crawlable internal links (15 pts)

What it can do on your machine

Read from SKILL.md and the folder at commit 33cc983. 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/ (Python), which the agent can run.

    Shell commands in SKILL.md call:

    • python3

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

  • Network

    Hosts in commands or code, which the agent is likely to contact:

    • vellum.ai
    • stripe.com

    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

Geo Audit loads about 2k tokens when it runs. Until then it costs about 76 tokens; SKILL.md has 959 words of instructions outside code blocks.

Always · name and description, kept in context so the agent knows when to use it
~76
When it runs · the whole SKILL.md, loaded when a task matches
~2k

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 vellum-ai/vellum-assistant at commit 33cc983, republished under its MIT licence (© vellum-ai). 959 words, ~2,013 tokens.

Download SKILL.mdSave it as .claude/skills/geo-audit/SKILL.md (or your agent's skills folder). This skill also uses 1 other file; get the full folder from GitHub.
name
geo-audit
description
Runs a one-command technical GEO audit on any domain. Checks AI crawler access, llms.txt presence, server-side rendering, sitemap, and schema markup. Streams results live and ends with a 0–100 score plus the top 3 prioritized fixes. Built to be both genuinely useful and great to demo on camera.
metadata.emoji
🔎

GEO Audit

A fast, real technical audit of how AI-ready a website is. Type a domain, get a streaming scorecard back in under 30 seconds, ending with the three things most worth fixing.

This is the operator-side companion to writing content. It tells you whether your site is even legible to AI agents before you spend a quarter producing for them.


RUNNING AN AUDIT

Extract the domain from the user's message and run the script without asking clarifying questions first. Stream the terminal output back as it happens. When the HTML report opens, mention that it just popped up in their browser and summarize the score in one sentence.

WHEN TO USE THIS SKILL

Use this when someone wants to:

  • Quickly understand how AI-friendly a site is
  • Diagnose why a site they've written for isn't getting picked up by ChatGPT, Perplexity, Gemini, or Claude
  • Produce a demo-able audit on any domain on the fly
  • Triage technical GEO issues before kicking off a content program

Do not use this skill for writing articles, comparison pages, or topical hubs. Route those to geo-article-writer.


USAGE

bash
python3 {baseDir}/scripts/audit.py <domain>

Examples:

bash
python3 {baseDir}/scripts/audit.py vellum.ai
python3 {baseDir}/scripts/audit.py https://stripe.com
python3 {baseDir}/scripts/audit.py example.com --json

The script accepts a bare domain (vellum.ai), a full URL (https://vellum.ai), or anything in between. It normalizes.

If the script runs cleanly → stream the output and summarize (default). If the domain is unreachable / DNS fails / times out → report the specific failure plainly, suggest the user double-check the domain or bump --timeout, and do not invent a score. If python3 is unavailable or blocked in this session → say so directly. Do not fabricate a scorecard or paraphrase what the audit "would" find — the numbers only exist if the script ran.

Flags:

  • --json — emit the report as JSON instead of streaming markdown (for piping into other tools)
  • --no-color — strip ANSI color codes (for logs / CI)
  • --no-html — skip the HTML report (default: writes one to a temp file and auto-opens it in your browser)
  • --no-open — write the HTML report but don't auto-open it
  • --timeout N — per-request timeout in seconds (default: 10)

By default the script does two things at once: streams a clean terminal scorecard live as checks complete, and opens a dark-themed HTML report in your browser at the end with a table, prioritized fixes, and a handoff to geo-article-writer. The terminal version is the watchable moment; the HTML is the keepable artifact.


WHAT IT CHECKS

Six checks, each scored. Total: 100 points.

1. AI crawler access via robots.txt (25 pts)

Pulls /robots.txt and verifies each of the major AI agents is either explicitly allowed or not actively blocked:

  • GPTBot, ChatGPT-User, OAI-SearchBot (OpenAI)
  • ClaudeBot, anthropic-ai (Anthropic)
  • PerplexityBot, Perplexity-User (Perplexity)
  • Google-Extended (Gemini / Google AI Overviews — separate from Googlebot)
  • CCBot (Common Crawl, feeds many training sets)

A site that blocks Google-Extended is invisible to Gemini and AI Overviews even if it ranks fine in regular Google. This is the most common silent miss.

2. llms.txt presence and shape (15 pts)

Looks for /llms.txt at the domain root. Scores on:

  • Exists
  • Has a top-level # title
  • Lists at least one curated link
  • Links resolve (no 404s on the first batch)

llms.txt is the emerging convention for handing AI crawlers a curated map. It's still optional, but it's a cheap differentiator.

3. Server-side rendering (20 pts)

Fetches the homepage without executing JS and checks whether the brand name, primary H1, and primary CTA are present in the initial HTML.

This is the single most under-detected GEO failure. A JS-rendered marketing site can look fine to a human and be completely empty to GPTBot, which generally does not execute JavaScript.

Show full SKILL.md (374 more words)Show less
4. sitemap.xml (10 pts)

Confirms a sitemap exists, is referenced from robots.txt, parses as valid XML, and contains a reasonable URL count.

5. Schema markup on homepage (15 pts)

Parses inline JSON-LD on the homepage and scores presence of:

  • Organization (brand identity for AI)
  • WebSite with SearchAction (helps Google understand site search)
  • A primary content schema (Product, SoftwareApplication, or Article — whichever fits)

Schema is one of the few signals models read directly without inference. It punches above its weight.

Fetches the homepage and inspects the first 50 internal <a> tags for:

  • Actual href values (not JS-bound <div onclick> substitutes)
  • Descriptive anchor text (not "click here," "learn more," empty)
  • No-follow ratio under 20%

If your important pages are reachable only through JS-bound elements, they're invisible to most crawlers.


OUTPUT

The script streams a markdown scorecard as checks complete. Each check is one line until done, then resolves to a verdict line. At the end:

GEO Audit — {domain}

✓ AI crawler access ............... 22 / 25
✗ llms.txt ........................  3 / 15
✓ Server-side rendering ........... 20 / 20
✓ Sitemap .........................  9 / 10
~ Schema markup ...................  8 / 15
✓ Crawlable internal links ........ 13 / 15

Score: 75 / 100

Top 3 fixes
  1. Stand up an llms.txt at the domain root (high impact, low effort)
  2. Add Organization + SoftwareApplication JSON-LD to the homepage
  3. Unblock CCBot in robots.txt (cheap win for training-set coverage)

The "top 3 fixes" are not just the lowest-scored checks — they're sorted by (points missing × impact weight) / effort estimate so the user gets a real prioritized list.


INTERPRETING THE SCORE

  • 85–100 — AI-ready. Content investment will compound. Focus on writing.
  • 65–84 — Functional but leaking. Fix the top 2 issues before scaling content.
  • 40–64 — Substantial drag. The audit's top 3 fixes are urgent.
  • 0–39 — The site is effectively invisible to most AI crawlers. Content is wasted spend until infrastructure ships.

DEFAULT DELIVERABLE

When asked to audit a site, run the script and return the streamed report verbatim. Then add one paragraph of plain-language context: what the score means for this specific site, and which of the top 3 fixes is most worth shipping this week.

⚠️ CRITICAL — at the moment you return the report: do not silently rewrite, round, or "clean up" the report's verdicts. The numbers are the product. If the script didn't run, there is no score — say that, never estimate one.

SKILL COMPLETE WHEN

  • audit.py ran against the requested domain and exited without error
  • The streamed scorecard (six checks + total + top 3 fixes) was returned to the user verbatim
  • One paragraph of plain-language context named which fix to ship first
  • If the HTML report was generated, the user was told it opened in their browser

© vellum-ai, 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 1 other file (scripts) in skills/geo-audit of vellum-ai/vellum-assistant.

  • SKILL.md
  • scripts/audit.py

Open the folder on GitHubat commit 33cc983

Compare with similar skills

Geo Audit 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.

Geo Audit compared with similar skills
SkillStarsUsed inTokensAuto-checkLicenceRepo updated
Geo Audit this skillvellum-ai/vellum-assistant1.4k—~2kAutomated safety check: PassMIT
SEOmagnus919/agent-skills115—~1.9kAutomated safety check: PassMIT
SEO Visibility Expertcuriositech/some_claude_skills244—~1.6kAutomated safety check: NotesMIT
GEO-First SEO Audit Toolzubair-trabzada/geo-seo-claude11k—~2.8kAutomated safety check: NotesMIT
SEO and GEO Auditdageno-agents/seo-geo-audit176—~2kAutomated safety check: PassMIT
Universal SEO AnalysisAgriciDaniel/claude-seo19k—~4.9kAutomated safety check: PassMIT

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Categories

Questions about Geo Audit

What does Geo Audit do?

Runs a one-command technical GEO audit on any domain. An agent skill from vellum-ai/vellum-assistant. Geo Audit is an agent skill from vellum-ai/vellum-assistant. Runs a one-command technical GEO audit on any domain.

When should I use Geo Audit?

Geo Audit fits situations like: tasks that involve AI search optimization; tasks that involve Web scraping; tasks that involve Schema markup.

How do I install Geo Audit in Claude Code?

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

How do I install Geo Audit in Codex?

Run `npx skills add vellum-ai/vellum-assistant --skill geo-audit -a codex`. Or copy the skill folder (skills/geo-audit in vellum-ai/vellum-assistant) into .agents/skills/geo-audit in your project. Codex loads it when a task matches its description.

Can I use Geo Audit 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 vellum-ai/vellum-assistant --skill geo-audit -a cursor` (or -a gemini-cli, github-copilot or opencode for the others). To copy it by hand, put the folder in .cursor/skills/geo-audit, .gemini/skills/geo-audit, .github/skills/geo-audit and .opencode/skills/geo-audit in your project.

What does Geo Audit need to run?

Going by SKILL.md and its folder, Geo Audit needs Python for the scripts in its folder and the command-line tools its instructions call (python3). Our summary lists: Python 3.

Does Geo Audit access the network?

SKILL.md names 2 domains. In commands or code: vellum.ai and stripe.com; the agent is likely to contact these when it follows the instructions. This is read from the text; nothing was executed.

Is Geo Audit 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 Geo Audit use?

Geo Audit 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 Geo Audit use?

About 2k tokens (SKILL.md is roughly 8.1k characters). Agents keep only the skill's name and description in context until a task matches; then they load SKILL.md in full.

What are the alternatives to Geo Audit?

Skills that share tags, products or a category with Geo Audit: SEO (magnus919/agent-skills, 115 stars), SEO Visibility Expert (curiositech/some_claude_skills, 244 stars), GEO-First SEO Audit Tool (zubair-trabzada/geo-seo-claude, 11k stars) and SEO and GEO Audit (dageno-agents/seo-geo-audit, 176 stars). The comparison table on this page puts their stars, adoption, token cost, safety result and licence side by side.

Who maintains Geo Audit?

vellum-ai (a GitHub organization) maintains it in vellum-ai/vellum-assistant, which has 1,408 GitHub stars. The repository holds 108 skills in this directory. The repository was last updated on October 9, 2026.

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