| name | geo-audit |
| description | Run a comprehensive GEO visibility audit on any website. Analyzes AI citability, structured data, AI crawler access, llms.txt, and content quality. Produces a 0-30 citability score and prioritized action plan. |
| user-invocable | true |
| allowed-tools | Bash, Read, Grep, Glob, WebSearch, WebFetch |
| context | fork |
GEO Audit Skill
You are running a GEO (Generative Engine Optimization) audit. This skill analyzes a URL for AI search visibility across platforms like ChatGPT, Perplexity, Google AI Overviews, and Claude.
Input
The user may provide:
- A specific URL to audit (e.g.,
example.com/blog/some-post)
- A keyword to check visibility for (e.g., "best project management tools")
- "full" for a complete site audit
- A competitor URL for comparative analysis
If no URL is provided, ask the user for one.
Preparation
If a brand.md file exists in the project root, read it for brand context. Otherwise, infer context from the target site's content.
Audit Steps
Step 1: Run Python Scripts
Run deterministic checks first — do not skip this step.
python3 scripts/check-site.py <url>
python3 scripts/check-page.py <url>
python3 scripts/check-schema.py <url>
python3 scripts/check-geo.py <url> --verbose
Parse the JSON output from each script. Note any llm_review_required: true fields — those require semantic judgment in Step 3.
Step 2: AI Visibility Check
Derive 5-8 queries from the target page's topic and keywords. Search these queries to check if the target site appears in AI-generated answers.
For each query:
- Use WebSearch to identify top results and AI snippet sources
- Note which competitors appear instead
- Observe what content format and structure cited sources use
Step 3: Semantic Analysis
Review script output fields marked llm_review_required: true and apply judgment:
- Partial keyword matches in title/H1 — natural variant or mismatch?
- Cross-domain canonical — intentional syndication or error?
- Schema completeness — are required fields semantically correct?
- First-sentence quality — does it function as a standalone extractable answer?
Step 4: Citability Scoring
Use check-geo.py output as the base score. Supplement with your semantic analysis to adjust where the script flagged llm_review_required.
| Dimension | Abbrev | Score |
|---|
| Direct Answer | DA | /5 |
| Data Density | DD | /5 |
| Authority Signals | AS | /5 |
| Structure Quality | SQ | /5 |
| Freshness | FR | /5 |
| Uniqueness | UQ | /5 |
| Total | | /30 |
Grades: A (27–30) · B (22–26) · C (17–21) · D (12–16) · F (<12)
Step 5: Competitor Benchmark
Identify top 3 competitors appearing in AI responses and compare:
| Factor | Target | Competitor 1 | Competitor 2 | Competitor 3 |
|---|
| AI Citations | | | | |
| Schema Types | | | | |
| Content Depth | | | | |
| Data Density | | | | |
| FAQ Sections | | | | |
Step 6: Recommendations
Produce a prioritized action list:
## Priority Actions
### P0 — Critical (fix immediately)
1. [Action] — [Expected impact]
### P1 — High Impact (this week)
1. [Action] — [Expected impact]
### P2 — Medium Impact (this month)
1. [Action] — [Expected impact]
Output
Save the audit report to audits/geo-audit-[domain]-YYYY-MM-DD.md and display a summary with the citability score and top 3 priority actions.
