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 bestagentkits/agency-skills --skill aeogit clone --depth 1 https://github.com/bestagentkits/agency-skillsWrote 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/bestagentkits/agency-skills/aeo)<a href="https://agentmods.dev/skills/bestagentkits/agency-skills/aeo"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/aeo/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/bestagentkits/agency-skills/aeo"><img src="https://agentmods.dev/badge/skills/bestagentkits/agency-skills/aeo.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 | $0.00169 | $0.02643 |
| Opus 5 | $0.00084 | $0.01321 |
| Sonnet 5 | $0.00034 | $0.00529 |
| Haiku 4.5 | $0.00017 | $0.00264 |
Grade A, and why
aeo 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 11d 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.
This is a copy
100% identical to aeo — 1 line differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 227 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Answer Engine Optimization (AEO)
Get your content cited by ChatGPT, Perplexity, Claude, Gemini, and Mistral as the authoritative source.
AEO is the practice of optimizing content for citation in LLM-generated responses — distinct from SEO, which optimizes for search rankings. This skill audits, optimizes, and tracks AEO performance.
Distinct From SEO
| SEO | AEO | |
|---|---|---|
| Optimizes for | Click-through rankings | Being cited as authoritative source |
| Audience | Humans browsing search results | LLMs answering questions |
| Success metric | Position 1-10, organic traffic | Citation count across LLMs |
| Key signals | Backlinks, keywords, page speed | E-E-A-T, structured data, factual density |
| Update cadence | Weeks-to-months | Days-to-weeks (LLM training cycles) |
Both can coexist — the same content can rank #1 on Google AND get cited by Perplexity. But the techniques differ: SEO rewards keyword density + backlinks; AEO rewards primary-source signals + structured facts.
When To Use
- Planning a new content piece for an AI-first audience
- Auditing existing content for E-E-A-T gaps before AI Overview rollout
- Tracking which pages get cited by which LLM (citation ledger)
- Researching what queries LLMs cite sources for (vs. what they answer from training)
- Benchmarking against competitors' citation rates
- Building a long-term AEO strategy aligned with traditional SEO
When NOT To Use
- Pure click-through SEO without LLM-citation intent — use
marketing-skill/skills/seo-auditinstead - Brand-voice content with no factual claims — citations require facts to cite
- Content for a topic where LLMs already have strong training signal (e.g., elementary math) — citation upside is minimal
- Time-sensitive content (breaking news) — LLM training lag means citations come months later
Core Capabilities
1. Content audit + E-E-A-T scoring
The auditor (aeo_audit.py) scores content across 4 dimensions:
- Experience: First-person evidence, dated examples, case studies, "We ran X in 2026" claims
- Expertise: Author bio, credentials, citations to peer-reviewed sources, technical depth
- Authoritativeness: External backlinks from authority domains, schema.org markup, structured data
- Trustworthiness: HTTPS, contact info, transparent corrections, factual density (number of verifiable claims per 1000 words)
What ships with it
10 files 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.
- .claude-plugin/plugin.json 1.9 KB
- agents/openai.yaml 174 B
- references/aeo_eeat_canon.md 9.5 KB
- references/aeo_vs_seo.md 8.5 KB
- references/bot_access_and_monitoring.md 6.7 KB
- references/extractable_content_patterns.md 10 KB
- references/llm_citation_patterns.md 8.4 KB
- scripts/aeo_audit.py 17 KB runs code
- scripts/aeo_optimizer.py 11 KB runs code
- scripts/citation_tracker.py 12 KB runs code
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.
- 11d ago First seen · 227 lines · 169 tokens per session scan A c2b1fe51416b
aeo is a skill published in the GitHub repository bestagentkits/agency-skills (11 stars, last pushed 2mo ago), licensed MIT. It adds 169 tokens to every session and 2,643 once invoked, about $0.0008 per session on Opus 5. A static security scan graded it A with 0 findings. It is 100% identical to aeo, differing in 1 line, and is treated as a copy.
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