AG Kit is a toolkit that gives Google Antigravity coding agents a structured workspace with rules, skills, specialist agents, workflows, persistent memory, orchestration, MCP guidance, and a safety hook. It is for building and operating agent workflows in Antigravity, and its catalogue entries provide parts of that workspace contract.
Borrowing it
Nothing to install: this file belongs to vudovn/ag-kit. Take a copy, put it at the same path in your own repository, and replace the rules that are about this project with yours.
curl -O https://raw.githubusercontent.com/vudovn/ag-kit/main/.agents/skills/geo-fundamentals/SKILL.mdgit clone --depth 1 https://github.com/vudovn/ag-kitWrote 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/skills/vudovn/ag-kit/geo-fundamentals)<a href="https://agentmods.dev/skills/vudovn/ag-kit/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/geo-fundamentals/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/vudovn/ag-kit/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/vudovn/ag-kit/geo-fundamentals.svg" alt="Reviewed on agentmods" width="80" 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 5.1 | $0.00024 | $0.00894 |
| Opus 5 | $0.00012 | $0.00447 |
| Sonnet 5 | $0.00005 | $0.00179 |
| Haiku 4.5 | $0.00002 | $0.00089 |
Grade A, and why
geo-fundamentals 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 10d 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.
This is a copy
94% identical to geo-fundamentals — 2 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 159 lines — stays where its author put it; the contents beside it link to each section on GitHub.
GEO Fundamentals
Optimization for AI-powered search engines.
1. What is GEO?
GEO = Generative Engine Optimization
| Goal | Platform |
|---|---|
| Be cited in AI responses | ChatGPT, Claude, Perplexity, Gemini |
SEO vs GEO
| Aspect | SEO | GEO |
|---|---|---|
| Goal | #1 ranking | AI citations |
| Platform | AI engines | |
| Metrics | Rankings, CTR | Citation rate |
| Focus | Keywords | Entities, data |
2. AI Engine Landscape
| Engine | Citation Style | Opportunity |
|---|---|---|
| Perplexity | Numbered [1][2] | Highest citation rate |
| ChatGPT | Inline/footnotes | Custom GPTs |
| Claude | Contextual | Long-form content |
| Gemini | Sources section | SEO crossover |
3. RAG Retrieval Factors
How AI engines select content to cite:
| Factor | Weight |
|---|---|
| Semantic relevance | ~40% |
| Keyword match | ~20% |
| Authority signals | ~15% |
| Freshness | ~10% |
| Source diversity | ~15% |
4. Content That Gets Cited
| Element | Why It Works |
|---|---|
| Original statistics | Unique, citable data |
| Expert quotes | Authority transfer |
| Clear definitions | Easy to extract |
| Step-by-step guides | Actionable value |
| Comparison tables | Structured info |
| FAQ sections | Direct answers |
5. GEO Content Checklist
Content Elements
- Question-based titles
- Summary/TL;DR at top
- Original data with sources
- Expert quotes (name, title)
- FAQ section (3-5 Q&A)
- Clear definitions
- "Last updated" timestamp
- Author with credentials
Technical Elements
- Article schema with dates
- Person schema for author
- FAQPage schema
- Fast loading (< 2.5s)
- Clean HTML structure
6. Entity Building
| Action | Purpose |
|---|---|
| Google Knowledge Panel | Entity recognition |
| Wikipedia (if notable) | Authority source |
| Consistent info across web | Entity consolidation |
| Industry mentions | Authority signals |
What ships with it
1 file beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 10d ago First seen · 159 lines · 24 tokens per session scan A d4ca6f9c8894
geo-fundamentals is a skill published in the GitHub repository vudovn/ag-kit (8,172 stars, last pushed 3d ago), licensed MIT. It adds 24 tokens to every session and 894 once invoked, about $0.0001 per session on Opus 5. A static security scan graded it A with 0 findings. It is 94% identical to geo-fundamentals, differing in 2 lines, and is treated as a copy.
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