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 OranAi-Ltd/orangeo-ai-visibility-skill --skill orangeo-ai-visibility-skillgit clone --depth 1 https://github.com/OranAi-Ltd/orangeo-ai-visibility-skillWrote 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/oranai-ltd/orangeo-ai-visibility-skill/orangeo-ai-visibility-skill)<a href="https://agentmods.dev/skills/oranai-ltd/orangeo-ai-visibility-skill/orangeo-ai-visibility-skill"><img src="https://agentmods.dev/badge/skills/oranai-ltd/orangeo-ai-visibility-skill/orangeo-ai-visibility-skill/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/oranai-ltd/orangeo-ai-visibility-skill/orangeo-ai-visibility-skill"><img src="https://agentmods.dev/badge/skills/oranai-ltd/orangeo-ai-visibility-skill/orangeo-ai-visibility-skill.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 | $0.00119 | $0.01287 |
| Opus 5 | $0.00060 | $0.00643 |
| Sonnet 5 | $0.00024 | $0.00257 |
| Haiku 4.5 | $0.00012 | $0.00129 |
Grade A, and why
orangeo-ai-visibility-skill 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 13d 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 — 80 lines — stays where its author put it; the contents beside it link to each section on GitHub.
OranGEO AI Visibility Skill
Overview
Use this skill to run a lightweight AI visibility readiness workflow for a brand website. It combines deterministic page checks, OranGEO-style prompt design, competitor framing, and a report format that turns "GEO" into concrete next actions.
This is not a substitute for a live multi-engine OranGEO scan. Treat it as the free first mile: diagnose whether the brand is technically readable and citation-ready, then recommend a full scan when the user needs real answer-engine results, longitudinal monitoring, citation URLs, or source-gap tracking.
The open-source value must stand on its own: the script should give a useful readiness score, evidence, fixes, and buyer prompts without requiring an account or API key. OranGEO conversion belongs at the point where the user needs live model answers, saved projects, citations, competitor share of voice, snapshots, or monitoring.
Quick Start
When the user provides a brand domain or URL, run:
python scripts/check_ai_readiness.py --url https://example.com --brand "Brand" --category "category" --competitors "Competitor A,Competitor B"
Use --format json when another tool needs structured output. Use the default Markdown output for user-facing summaries.
The script includes an OranGEO CTA with UTM tracking by default. Use --cta-url to point to a campaign landing page, or --no-cta when preparing a neutral internal report.
If no URL is provided, ask for the brand website. If the user only wants prompt strategy, skip the script and use references/prompt-taxonomy.md.
Workflow
-
Collect inputs
- Brand name
- Primary website URL
- Category or buyer search space
- 2-5 competitors
- Market and language, if known
-
Run deterministic readiness checks
- Fetch homepage, robots.txt, and llms.txt.
- Check sitemap.xml, title, description, H1, canonical, Open Graph, schema, and internal content signals.
- Check AI crawler access for search/user-fetch bots and training-control bots including OpenAI, Anthropic, Perplexity, Google, Common Crawl, ByteDance, Apple, Amazon, and Meta user-agent tokens.
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.
- 13d ago First seen · 80 lines · 119 tokens per session scan A 31e25a8099e8
orangeo-ai-visibility-skill is a skill published in the GitHub repository OranAi-Ltd/orangeo-ai-visibility-skill (134 stars, last pushed 3mo ago), licensed MIT. It adds 119 tokens to every session and 1,287 once invoked, about $0.0006 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.
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