Aeo

AEO — AI Engine Optimization

Write content that LLMs cite when a human asks them a question. Different objective than SEO, different rules than craft writing.

When to use this skill

• User wants content that gets quoted by ChatGPT / Claude / Gemini
• User mentions "AEO", "AI engine", "LLM citation", "get cited", "write for AI"
• Content is for a brand, product, person, or category the customer wants to own in LLM answers

Not for:

• Essays or long-form craft writing → use writing skill
• LinkedIn posts / launch posts → use launch or linkedin-content
• Copy-editing existing text → use copy-editor

Architecture

The skill is scaffolding. The product is the backend endpoint:

atris write aeo "<topic>"
        ↓
POST /api/business/{id}/workspaces/{ws}/aeo/draft
        ↓
reads entity graph from /workspace/atris/aeo/{entities,definitions,stats}.md
        ↓
writes draft to /workspace/atris/aeo/drafts/<slug>.md
        ↓
returns draft + self-score + credit cost

Entity graph lives in the customer's EC2 workspace (not agent_files, not DB). The customer's files ARE the brand's machine-readable self.

The 10 rules (enforced by the endpoint + skill self-check)

1. Front-load the claim. LLMs quote sentence 1 of a paragraph. No throat-clearing.
2. Name entities explicitly. "ExampleCo" not "a leading freight platform." Entity density is how LLMs disambiguate and cite.
3. Canonical definition. One sentence owns X is Y that does Z for the category. Make it liftable.
4. Q&A scaffolding. H2s in question form: "What is X?" "How does X work?" — matches the prompts LLMs actually get.
5. Declarative stats with sources. "$2.3B market (McKinsey 2025)" beats "a growing market." Citable atoms.
6. Comparison tables. LLMs love structured data. Build one per category claim.
7. Fresh dates visible. "Updated 2026-04-17" at top. LLMs weight recency.
8. Counter-position. "Unlike X, Y does Z." Sharpens the entity in the model's mind.
9. No hedging. "May/might/could" kills citation rate. Write assertive.
10. Schema-ready. If HTML target, emit FAQPage + Article + HowTo JSON-LD.

Process

READ entity graph → DRAFT → SELF-SCORE → WRITE to workspace → (phase 2: AUDIT)

1. READ — Pull entities.md, definitions.md, stats.md from customer workspace. If missing, create skeleton files first.
2. DRAFT — Generate article applying all 10 rules. Topic + target queries + entity context → article.
3. SELF-SCORE — Append a ## AEO Self-Check section: each rule scored pass/fail with evidence.
4. WRITE — Save to /workspace/atris/aeo/drafts/<slug>.md.
5. AUDIT (phase 2) — Run target queries against 4 LLMs, measure whether article is cited, write results to /workspace/atris/aeo/audits/<slug>-<date>.md.

Entity graph layout (in customer workspace)

/workspace/atris/aeo/
├── entities.md        # named entities (products, people, companies)
├── definitions.md     # canonical "X is Y" sentences
├── stats.md           # citable stats with sources
├── config.yml         # target_queries, competitor_brands, target_url
├── drafts/            # AEO articles (one per slug)
└── audits/            # citation-audit results (phase 2)

Commands

# Generate a draft (credit-metered)
atris write aeo "<topic>" --workspace <name>

# Init the entity graph (writes skeleton files)
atris aeo init --workspace <name>

# View current drafts
atris ls workspace/atris/aeo/drafts --workspace <name>

Credits

Rules (non-negotiable)

• Draft writes to customer workspace, never to agent_file_memory. EC2 is the source of truth.
• Self-score section is mandatory in every draft.
• Never hedge in the body. Hedging kills citation rate.
• Never fabricate stats. If a stat is uncited, flag it in self-check.
• Entity graph skeleton auto-creates if missing — never fail on first run.

Related

• Feature: atris/features/aeo/idea.md
• Backend: POST /api/business/{id}/workspaces/{ws}/aeo/draft
• Sister skill: writing (human-reader craft)
• Sister skill: copy-editor (deslopper)