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 skills/phuonghx/aim-cli/geo-fundamentalsnpx skills add phuonghx/aim-cli --skill geo-fundamentalsgit clone --depth 1 https://github.com/phuonghx/aim-cliWrote 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/phuonghx/aim-cli/geo-fundamentals)<a href="https://agentmods.dev/skills/phuonghx/aim-cli/geo-fundamentals"><img src="https://agentmods.dev/badge/skills/phuonghx/aim-cli/geo-fundamentals.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 | $0.00084 | $0.01268 |
| Opus 5 | $0.00042 | $0.00634 |
| Sonnet 5 | $0.00017 | $0.00254 |
| Haiku 4.5 | $0.00008 | $0.00127 |
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 4d 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 — 143 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Generative Engine Optimization (GEO)
The goal here is narrow: get a page quoted inside an AI-generated answer, not just ranked on a results page. This file explains the levers that move that needle.
How GEO differs from classic SEO
Search optimization tries to win a position in a ranked list a human scrolls through. GEO tries to become the passage a language model pulls into its synthesized reply. The overlap is real, but the targets diverge:
- Outcome that matters -- a citation or named mention, not a click-through rank.
- Where it happens -- inside answer engines (Perplexity, ChatGPT browsing, Gemini, Copilot), not the ten blue links.
- What you tune for -- extractable facts, named entities, and trust markers rather than keyword density alone.
- How you measure -- how often the brand is referenced in answers, versus rank tracking and CTR.
The answer-engine field
Each assistant cites a little differently, which changes where the easy wins are.
- Perplexity -- attaches inline numbered references; tends to cite generously, so it is the friendliest starting target.
- ChatGPT -- mixes inline mentions and footnote-style links when browsing; custom GPTs open extra surface area.
- Gemini -- leans on a sources block and inherits a lot from Google's index, so SEO work carries over.
- Reasoning-first engines -- favor thorough, well-structured long-form material when reasoning over a topic.
What the retrieval layer rewards
Answer engines run a retrieval-augmented pipeline: they pull candidate passages, rank them, then write over the top results. Rough influence of each signal:
| Signal | Approximate pull |
|---|---|
| Meaning match to the query (embeddings) | strongest |
| Source authority and reputation | strong |
| Topical / keyword overlap | moderate |
| Spread across independent sources | moderate |
| Recency of the page | smaller but real |
Takeaway: write the passage that most precisely answers the likely question, from a source the model already trusts.
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.
- 4d ago First seen · 143 lines · 84 tokens per session scan A 9d091eea5035
geo-fundamentals is a skill published in the GitHub repository phuonghx/aim-cli (1 stars, last pushed 2mo ago), licensed MIT. It adds 84 tokens to every session and 1,268 once invoked, about $0.0004 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 2026-08-31.
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