geo

name	geo
description	GEO-first SEO toolkit for Codex CLI. Use for website audits, AI search visibility, Generative Engine Optimization, citability scoring, AI crawler access, llms.txt analysis or generation, brand/entity signals, platform readiness for ChatGPT/Claude/Perplexity/Gemini/Google AI Overviews, Schema.org recommendations, technical SEO, content E-E-A-T, client reports, proposals, and prospect workflows. Invoke explicitly as $geo.
metadata	{"short-description":"GEO and SEO audits for AI search visibility"}

GEO-SEO Codex Skill

Use this skill when the user asks for GEO, SEO, AI search visibility, website audit, citability, AI crawler access, llms.txt, schema, content E-E-A-T, platform readiness, or client-ready GEO reports.

Invocation

• Native Codex form: $geo <command> <url-or-domain>.
• If the user gives only a URL and asks for an audit, default to $geo audit <url>.

Runtime Layout

Installed layout:

~/.codex/skills/geo/
+-- SKILL.md
+-- scripts/
|   +-- geo_cli.py
|   +-- fetch_page.py
|   +-- citability_scorer.py
|   +-- llmstxt_generator.py
|   +-- brand_scanner.py
|   +-- generate_pdf_report.py
+-- schema/
+-- references/
+-- .venv/

Prefer the installed virtual environment when running scripts:

• Windows PowerShell: ~/.codex/skills/geo/.venv/Scripts/python.exe ~/.codex/skills/geo/scripts/geo_cli.py quick https://example.com
• macOS/Linux: ~/.codex/skills/geo/.venv/bin/python ~/.codex/skills/geo/scripts/geo_cli.py quick https://example.com

If the installed path is missing, fall back to the current repository's scripts/ directory and the active Python interpreter.

Commands

Command	Result
$geo doctor	Local installation health check
$geo self-test	End-to-end smoke test
$geo quick <url>	Fast deterministic snapshot, inline by default
$geo audit <url>	Full GEO + SEO audit report
$geo audit-json <url>	Standardized GEO-AUDIT.json artifact
$geo citability <url>	GEO-CITABILITY-SCORE.md
$geo rewrite <url>	Citation-ready rewrite starters
$geo crawlers <url>	GEO-CRAWLER-ACCESS.md
$geo llmstxt <url>	Analyze /llms.txt
$geo llmstxt <url> --generate	Write deployable llms.txt
$geo compare-domain <url> <competitor>	Competitor GEO comparison
$geo brands <url-or-brand>	Brand/entity signal assessment
$geo platforms <url>	ChatGPT, Google AIO, Perplexity, Gemini, Copilot readiness
$geo schema <url>	Schema.org detection and JSON-LD recommendations
$geo schema <url> --generate organization	Generate JSON-LD file
$geo technical <url>	Technical SEO and AI-crawler foundation audit
$geo content <url>	E-E-A-T and AI-citable content audit
$geo report <url>	Client-ready Markdown report
$geo report-pdf <url-or-json>	PDF report using generate_pdf_report.py
$geo prospect <cmd>	CRM-lite prospect pipeline workflow
$geo proposal <domain>	Client proposal from audit findings
$geo compare <domain>	Monthly before/after delta report

For command-specific workflow details, read references/commands.md only for the command being executed. For score weights, read references/scoring.md.

Deterministic Helpers

Use scripts/geo_cli.py for checks it supports before doing LLM synthesis:

python scripts/geo_cli.py quick https://example.com
python scripts/geo_cli.py doctor
python scripts/geo_cli.py self-test
python scripts/geo_cli.py audit-json https://example.com --out GEO-AUDIT.json
python scripts/geo_cli.py citability https://example.com/page --out GEO-CITABILITY-SCORE.md
python scripts/geo_cli.py rewrite https://example.com/page --out GEO-REWRITE-SUGGESTIONS.md
python scripts/geo_cli.py crawlers https://example.com --out GEO-CRAWLER-ACCESS.md
python scripts/geo_cli.py llmstxt https://example.com
python scripts/geo_cli.py llmstxt https://example.com --generate --out llms.txt
python scripts/geo_cli.py schema https://example.com --generate organization --out schema-organization.jsonld
python scripts/geo_cli.py compare-domain https://example.com https://competitor.com
python scripts/geo_cli.py fetch https://example.com --out audit-data.json

The helper output is evidence, not the final consulting answer. Use it to ground findings, then add prioritization, business context, and implementation advice.

Full Audit Workflow

For $geo audit <url>:

1. Normalize the URL to https://domain unless the user supplied http://.
2. Run deterministic discovery:
   • geo_cli.py quick <url> --out GEO-QUICK-SNAPSHOT.md
   • geo_cli.py fetch <url> --out audit-data.json
   • geo_cli.py crawlers <url> --json --out crawler-data.json
   • geo_cli.py citability <url> --json --out citability-data.json
   • geo_cli.py audit-json <url> --out GEO-AUDIT.json
3. Inspect the homepage, sitemap, schema, robots.txt, llms.txt, and top pages if available.
4. Evaluate the six categories:
   • AI Citability and Visibility: 25%
   • Brand Authority Signals: 20%
   • Content Quality and E-E-A-T: 20%
   • Technical Foundations: 15%
   • Structured Data: 10%
   • Platform Optimization: 10%
5. Write GEO-AUDIT-REPORT.md with:
   • executive summary
   • score breakdown
   • critical/high/medium/low issues
   • quick wins
   • 30-day action plan
   • ready-to-paste robots.txt, llms.txt, or JSON-LD snippets when relevant

If Codex subagent tools are available and the user explicitly asked for parallel agents, split the analysis by category. Otherwise perform the audit inline.

Report Standards

• Be concrete: cite URLs, observed tags, scores, crawler statuses, and missing schema types.
• Separate deterministic evidence from judgment calls.
• Do not claim live AI search rankings unless they were actually checked.
• Treat llms.txt as an emerging convention, not a guaranteed ranking factor.
• Keep generated client reports usable without extra editing.

Data Storage

CRM/prospect workflows store local runtime data under ~/.geo-prospects/:

~/.geo-prospects/
+-- prospects.json
+-- audits/
+-- proposals/
+-- reports/