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 factsgit 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/facts)<a href="https://agentmods.dev/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/facts"><img src="https://agentmods.dev/badge/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/facts/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/facts"><img src="https://agentmods.dev/badge/skills/ai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool/facts.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 | $0.00011 | $0.00626 |
| Opus 5.5 | $0.00004 | $0.00250 |
| Sonnet 5.5 | $0.00002 | $0.00125 |
| Haiku 4.5 | $0.00001 | $0.00063 |
Grade A, and why
facts 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.
How it starts
The opening of the file, as written. The whole thing — 50 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Brand facts check
Check the brand's drafts against its approved brand facts (its plans, trial, founding year and the claims it makes), and list every place a draft says something else.
When to use
- "Check our drafts against our brand facts", "Does this page still say the old trial length?", "Are our facts consistent?"
Where the facts come from
GetCito reads the brand facts from the brand's own pricing, About and product pages, and a person approves each one in the brand's settings, on the Brand facts tab. Only approved facts count. Read them with get_brand_facts: the approved facts still on the brand's site, each in its page's own words, with when it was approved and the page it came from. Facts waiting for approval, rejected ones and ones gone from the site aren't in it.
When it returns none, say so, and ask the person to open Brand facts and approve the ones waiting, or to paste the facts they confirm. Never approve a fact yourself, and never use a fact that isn't approved or that the person didn't confirm.
What to read
get_brand_facts: the brand's approved facts.list_drafts: the brand's drafts, latest changed first.get_draftfor each draft you check, at most five in one run: its passages, and where it is live.
Steps
- Read the approved facts with
get_brand_facts. With none, stop and ask for them, as above. Facts the person gives in the chat count too. - Read each draft and find every passage that states one of those facts: a plan, a trial, a founding year, a claim about the brand.
- Compare each with the approved fact: same, different, or no longer true.
- Draft the fix for each mismatch: the passage with the approved fact in it, nothing else changed.
Output
- A table: Draft, Passage, Says, Approved fact, Fix.
- The drafts with no mismatch, in one line.
- Facts a draft states that aren't in the approved list, for the person to check.
When the person wants the fixes kept, save the corrected page as a new draft with create_draft; the app asks them first when it should. In an agent's run nobody is there to ask: always save it with its tool, and the app holds it for approval where the agent's mode needs it.
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 · 50 lines · 11 tokens per session scan A 086054616ed9
facts 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 11 tokens to every session and 626 once invoked, about $0.0000 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.
facts compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| factsai-search-guru/getcito-worlds-first-open-source-aio-aeo-or-geo-tool | Skill | 449 | yesterday | MIT | A | 11 | 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 |