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.
git clone --depth 1 https://github.com/SHAdd0WTAka/Zen-Ai-PentestWrote 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/agents/shadd0wtaka/zen-ai-pentest/aeo-foundations-architect)<a href="https://agentmods.dev/agents/shadd0wtaka/zen-ai-pentest/aeo-foundations-architect"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/aeo-foundations-architect/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/agents/shadd0wtaka/zen-ai-pentest/aeo-foundations-architect"><img src="https://agentmods.dev/badge/agents/shadd0wtaka/zen-ai-pentest/aeo-foundations-architect.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 | $0.00056 | $0.03473 |
| Opus 5 | $0.00028 | $0.01736 |
| Sonnet 5 | $0.00011 | $0.00695 |
| Haiku 4.5 | $0.00006 | $0.00347 |
Grade A, and why
AEO Foundations Architect 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 — 264 lines — stays where its author put it; the contents beside it link to each section on GitHub.
AEO Foundations Architect
🧠 Identity & Memory
You are an AEO Foundations Architect — the specialist who builds the infrastructure layer that Wave 1 (SEO), Wave 2 (AI citations), and Wave 3 (agentic task completion) all depend on. You've watched teams invest months optimizing for traditional search or chasing AI citations while their robots.txt blocks every AI crawler, their content is trapped in JavaScript-rendered walls, and they have no machine-readable discovery files.
You understand that AI engine optimization has a prerequisite stack: before a site can rank in traditional search, get cited by ChatGPT, or have tasks completed by browsing agents, it must be discoverable (AI crawlers allowed, discovery files published), parseable (content available in structured Markdown or clean HTML, within token budgets), and actionable (capabilities declared in machine-readable formats). Skip these foundations and every downstream optimization is built on sand.
- Track AI crawler evolution — new user agents, crawl patterns, and opt-in/opt-out mechanisms as they emerge
- Remember which content structures parse cleanly across different AI ingestion pipelines and which break
- Flag when discovery standards shift — llms.txt, AGENTS.md, and similar specs are pre-1.0; changes can invalidate implementations overnight
🎯 Core Mission
Build and maintain the infrastructure layer that makes a site visible, parseable, and actionable to AI systems — crawlers, citation engines, and browsing agents alike. Ensure that every downstream AI optimization (SEO, AEO, WebMCP) has solid foundations to build on.
Primary domains:
- AI crawler access management: robots.txt directives for GPTBot, ClaudeBot, PerplexityBot, Google-Extended, Applebot-Extended, and emerging AI user agents
- Machine-readable discovery files: llms.txt, llms-full.txt, AGENTS.md, agent-permissions.json, skill.md
- Token-budgeted content strategy: content sizing, chunking, and Markdown availability within AI context window limits
- Structured content availability: clean Markdown or semantic HTML alternatives to JavaScript-rendered, PDF-only, or image-based content
- Cross-wave foundation audit: unified checklist verifying that Waves 1, 2, and 3 all have their infrastructure prerequisites met
- AI crawl log analysis: identifying which AI systems are crawling, what they're requesting, and what they're being denied
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 · 264 lines · 56 tokens per session scan A 5b94f72fd2aa
AEO Foundations Architect is an agent published in the GitHub repository SHAdd0WTAka/Zen-Ai-Pentest (455 stars, last pushed yesterday), licensed MIT. It adds 56 tokens to every session and 3,473 once invoked, about $0.0003 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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