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 skills add ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool --skill questionsgit clone --depth 1 https://github.com/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-toolWrote 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/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/questions)<a href="https://agentmods.dev/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/questions"><img src="https://agentmods.dev/badge/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/questions/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/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/questions"><img src="https://agentmods.dev/badge/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/questions.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 | $0.00016 | $0.00474 |
| Opus 5.5 | $0.00006 | $0.00190 |
| Sonnet 5.5 | $0.00003 | $0.00095 |
| Haiku 4.5 | $0.00002 | $0.00047 |
Grade A, and why
questions 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 yesterday.
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.
Copies of this mod
1 near-identical copy found in the catalogue:
- questions — 100% identical, 0 lines differ
What it actually says
Buyer questions
What buyers ask AI engines in the brand's category: the questions grouped by topic, how often they are searched, and which brand AI recommends for each, with the reason it gives.
When to use
- "What do buyers ask AI about integrations?", "Which buyer questions came up this week?", "Product digest."
- For one competitor's side of things, the Rival brief skill fits better.
What to read
list_topicsandlist_prompts: the brand's topics and the prompts under each, newest first when the person asks what's new.get_fanoutsfor the prompts of the topic asked about: the questions engines search behind them.get_prompt_volumes: how often each prompt is searched, where the volume is known.list_ai_answersfor those prompts: which brand each engine recommends, and the words it gives as the reason.list_prompt_suggestions: questions the brand doesn't track yet.
Steps
- Take the topic the person names; with none, the topics with the most new prompts and fan-outs.
- Group the questions by what the buyer wants to know, not by their wording. Merge near-duplicates.
- For each group: its searches a month where known, the brand AI recommends most, and the reason it gives, quoted from an answer.
- Mark the groups where no brand clearly leads: they are the easiest to win.
- Close with what this means for the roadmap and the content plan, in three to five lines.
Output
- A table: Buyer question, Searches a month, AI recommends, Reason AI gives.
- "What this means": three to five lines.
- The untracked questions worth tracking, as a list the person can add with
add_prompts; the app asks them first.
Don't
- Never make up a volume: write "unknown" where none is known.
- Quote the reason AI gives; don't write your own.
- Don't say which product is better: report what AI says.
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.
- yesterday First seen · 46 lines · 16 tokens per session scan A 77a6da049e17
questions is a skill published in the GitHub repository ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool (449 stars, last pushed yesterday), licensed MIT. It adds 16 tokens to every session and 474 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-11-05.
questions compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| questionsai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool | Skill | 449 | yesterday | MIT | A | 16 | Claude Code, Codex |
| geo-platform-optimizerasale-ai/seo-geo-skill | Skill | 6 | 2mo ago | none | A | 34 | Claude Code |
| ansvisor-aeo-coach-standaloneansvisor/ansvisor | Skill | 232 | yesterday | MIT | A | 148 | Claude Code, Codex |
| geo-loopmverab/eGEOagents | Skill | 217 | yesterday | MIT | A | 62 | Claude Code |
| content-scoringmverab/eGEOagents | Skill | 217 | yesterday | MIT | A | 34 | Claude Code |
| competitive-analysismverab/eGEOagents | Skill | 217 | yesterday | MIT | A | 29 | Claude Code |
| schema-generatormverab/eGEOagents | Skill | 217 | yesterday | MIT | A | 35 | Claude Code |