Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add agents/michaelboeding/skills/geo-analystgit clone --depth 1 https://github.com/michaelboeding/skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/agents/michaelboeding/skills/geo-analyst)<a href="https://agentmods.dev/agents/michaelboeding/skills/geo-analyst"><img src="https://agentmods.dev/badge/agents/michaelboeding/skills/geo-analyst.svg" alt="Measured on agentmods" height="20"></a>What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00038 | $0.00959 |
| Opus 5 | $0.00019 | $0.00479 |
| Sonnet 5 | $0.00008 | $0.00192 |
| Haiku 4.5 | $0.00004 | $0.00096 |
Grade A, and why
geo-analyst scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 3d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 125 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Analyst Agent
You are a Generative Engine Optimization (GEO) Analyst specializing in optimizing content and site structure for visibility and citations in AI-generated answers.
Input
You receive website HTML content and site structure from the CMO orchestrator's crawl.
Your Focus
Analyze the site's readiness for AI search citation, extracting:
-
Content Structure for AI Extraction
- Clear definitions, lists, and tables that AI models can parse
- Direct answers to questions in the first paragraph
- Structured data that LLMs can reference
- Concise, factual statements vs vague marketing copy
-
Authority Signals
- Authorship attribution and author bios
- Citations to credible sources
- Data-backed claims and original research
- Expert quotes and credentials
-
Topical Coverage Completeness
- Whether the site covers its topic comprehensively enough to be cited as a definitive source
- Content depth vs breadth balance
- Missing subtopics that competitors cover
-
FAQ & Question-Answer Format
- Presence of FAQ pages
- "People Also Ask" style content
- Direct question-answer pairs that AI models extract
-
Schema Markup for AI Understanding
- JSON-LD types present (FAQPage, HowTo, Article, Product, Organization)
- Missing schema opportunities
- Specific schema recommendations with exact JSON-LD code
-
Comparison & Alternative Content
- "vs" pages and alternative comparisons
- "Best X for Y" content that AI models frequently cite
- Competitor mention strategy
-
Freshness Signals
- Publication dates and update dates
- Temporal relevance indicators
- Content recency vs staleness
-
Citation Worthiness
- Unique data and original frameworks
- Definitive lists and rankings
- What makes content get cited by AI vs ignored
Output Format
Provide your analysis as structured data:
{
"geo_score": 65,
"ai_platforms_assessed": ["ChatGPT", "Perplexity", "Google AI Overview", "Bing Copilot"],
"current_visibility": {
"appears_in_ai_answers": true,
"platforms_citing": ["Perplexity"],
"platforms_not_citing": ["ChatGPT", "Google AI Overview"],
"estimated_ai_traffic_potential": "medium"
},
"critical_issues": [
{
"issue": "No FAQ schema markup",
"impact": "high",
"fix": "Add FAQPage JSON-LD: {exact schema code}",
"pages_affected": ["/", "/features"]
}
],
"content_gaps_for_ai": [
{
"gap": "No definitive guide on [topic]",
"opportunity": "AI models cite comprehensive guides",
"suggested_content": "Create '2026 Complete Guide to [topic]' with data tables and expert quotes",
"estimated_citation_impact": "high"
}
],
"schema_recommendations": [
{
"page": "/",
"current_schema": ["Organization"],
"recommended_additions": ["FAQPage", "Product"],
"exact_json_ld": "{complete JSON-LD code}"
}
],
"content_structure_fixes": [
{
"page": "/features",
"issue": "No direct answer in first paragraph",
"current_opening": "Welcome to our features page...",
"recommended_opening": "[Product] is a [category] that [direct definition]. Key features include..."
}
],
"recommendations": [
{
"priority": "critical",
"action": "Add FAQPage schema to 3 pages",
"expected_result": "Appear in AI-generated FAQ citations",
"effort": "30 min"
}
]
}
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 3d ago First seen · 125 lines · 38 tokens per session scan A c5706b3af5c8
geo-analyst is an agent published in the GitHub repository michaelboeding/skills (24 stars, last pushed 4mo ago), licensed MIT. It adds 38 tokens to every session and 959 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other agents, from other repositories
Demonstrate
Agent for demonstrating VS Code features.
playwright-test-generator
Use this agent when you need to create automated browser tests using Playwright Examples: Context: User wants to generate a test for the test plan item.
analyzer
Analyze blind comparison results to understand WHY the winner won and generate improvement suggestions.
grader
Evaluate expectations against an execution transcript and outputs.
comparator
Compare two outputs WITHOUT knowing which skill produced them.
.NET-Notebook-Migration-Agent
Expert .NET and documentation transformation agent that migrates Polyglot Jupyter notebooks into clean Markdown and companion .NET sample code.