# AEO Human Guide

## What This Is For
Get your content cited by ChatGPT, Perplexity, Claude, Gemini, and Mistral as the authoritative source. It gives the agent a clearer input/output frame for AEO: what context to ask for, what decisions to make, and what usable artifact to return.

Use this as a human-readable version of the AEO agent skill. It is meant for marketers, operators, founders, and other non-coders who want the workflow without reading agent-specific implementation instructions.

## When To Use This
- Use this when you need a repeatable process for AEO.
- Use this when the task needs judgment, examples, constraints, or a clear output format rather than a one-off prompt.
- Use this when you want to hand an AI assistant enough context to produce a usable marketing artifact.

## When Not To Use This
- Do not use this when you only need a quick factual answer.
- Do not use this when the work depends on private data you cannot share with the assistant.
- Do not use this as a replacement for legal, compliance, financial, or medical review.

## What You Need Before Starting
- The goal or business outcome you want.
- The audience, customer segment, or market context.
- Any source material the assistant should respect, such as notes, briefs, examples, URLs, or brand guidance.
- Constraints such as tone, length, channel, deadline, region, or approval requirements.
- A clear definition of what a good final answer should look like.

## Step-By-Step Workflow
1. State the job clearly: "Use the AEO guide to help me with..."
2. Add context: audience, goal, offer, channel, source material, and constraints.
3. Ask the assistant to identify missing inputs before producing the final output.
4. Have the assistant follow the skill-specific guidance below.
5. Review the result against the final checklist and ask for revisions where needed.

## Skill-Specific Guidance
- 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
- Pure click-through SEO without LLM-citation intent — use `marketing-skill/skills/seo-audit` instead
- 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
- **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

## Decision Points And Nuance
The original skill emphasizes: Distinct From SEO, When To Use, When NOT To Use, Core Capabilities, Content audit + E-E-A-T scoring, Content optimization, Citation tracking, References, Workflow, Configuration.

Use these questions to steer the work:
- What is the intended audience or buyer?
- What source material must be preserved?
- What should the assistant optimize for: clarity, persuasion, accuracy, speed, creativity, or conversion?
- What examples represent the desired quality bar?
- What should the assistant avoid?

## Common Mistakes
- **stdlib-only** for all 3 scripts — no `pip install` required

## Copy-And-Paste Prompt
```text
Use the AEO human guide.

My goal:
[Describe the business outcome]

Audience:
[Describe who this is for]

Context and source material:
[Paste notes, examples, links, or existing copy]

Constraints:
[Tone, length, channel, timeline, must-include items, must-avoid items]

Before producing the final output, ask me for any missing information that would materially improve the result.
```

## Final Checklist
- [ ] The output matches the original goal.
- [ ] The audience and context are reflected in the answer.
- [ ] Important constraints and source material were preserved.
- [ ] The assistant made the relevant decisions explicit.
- [ ] The final artifact is ready to use, review, or hand to the next person.

## Source
This guide was generated from the alirezarezvani/claude-skills skill entry for `aeo`.

## Source Skill Notes
These notes preserve the nuance from the original skill. Use them as supporting reference when the workflow above feels too generic.

# 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-audit` instead
- 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)

Composite score 0-100 with per-dimension breakdown. Output: markdown report with specific fix recommendations.

### 2. Content optimization

The optimizer (`aeo_optimizer.py`) generates AEO-improved variants:

- **Structure rewrite** — H2/H3 hierarchy optimized for LLM parsing
- **Citation density boost** — adds `[1]`-style references with sources
- **Schema injection** — generates JSON-LD for FAQ, HowTo, Article schemas
- **Fact-first lede** — moves verifiable claims into the first 200 words

Three modes: `conservative` (touch <10% of words), `balanced` (touch <30%), `aggressive` (rewrite for maximum AEO).

### 3. Citation tracking

The tracker (`citation_tracker.py`) maintains a local ledger of citations:

- Manual entry: paste a citation found in ChatGPT/Perplexity/Claude/Gemini output
- Track which URL, which LLM, which query, what date
- Compute per-page citation count, citation velocity, LLM coverage
- Export to CSV for reporting

Stores in `~/.aeo-data/citations.json` (local, no telemetry).

## References

- `references/aeo_eeat_canon.md` — E-E-A-T methodology, industry thresholds, anti-patterns
- `references/llm_citation_patterns.md` — per-LLM citation selection heuristics (Perplexity, ChatGPT, Claude, Gemini, Mistral)
- `references/aeo_vs_seo.md` — when to invest in AEO vs SEO vs both
- `references/bot_access_and_monitoring.md` — AI crawler robots.txt matrix (the prerequisite check: a blocked bot zeroes that platform), Google Search Console AI Overviews monitoring, manual testing protocols, citation-drop diagnostic (merged from the former `ai-seo` skill)
- `references/extractable_content_patterns.md` — 7 copy-ready block templates (definition, steps, table, FAQ, attributed stat, expert quote, summary box) that answer engines reliably extract (merged from the former `ai-seo` skill)

## Workflow

```