Answer Engine Optimization (AEO) skill — optimize content to be cited by AI language models (ChatGPT, Perplexity, Claude, Gemini, Mistral) as authoritative sources. Distinct from SEO — AEO optimizes for citation in LLM-generated responses, not search rankings.
SkillScore91Maintained
Automatic
◆Marketing & Content pack
Last verified 8/4/2026
What it changes
Passed 20 of 22 test cases with the skill, and 10 of 22 without it.
Without skill45%
With skill91%
Checks it passed that the model alone missed
specifies the --output json flag when invoking the audit script
uses the healthcare industry parameter when executing the audit tool
uses python3 scripts/citation_tracker.py with the export action flag
and 10 more in the full report
10 fixed10 passed either way2 still failing0 broke
Pass-rate lift
+45 pp
Tokens
+91%
Agent turns
0%
Response time
-45%
Model gemini-3.6-flashMeasured 8/4/2026v120 of 22 cases passed with skill
Tokens, turns and time describe resource use, not a dollar price.
See it in action
One of 10 test cases the skill fixed. Same model, same prompt, with and without the skill.
1 of 10
Prompt
We want to parse the audit results programmatically inside an automated CI/CD publishing pipeline. Which command line option formats the audit script output as structured JSON?
What was checked, without and with the skill
Checked
Without skill
With skill
specifies the --output json flag when invoking the audit script
Missed
Passed
case-13 · gemini-3.6-flash · 8/4/2026 · answers are capped and redacted
What's inside
The instructions your model reads when the skill loads.
SKILL.md221 linessingle file
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:
Turn any domain folder of skills into a bounded agentic loop: compile a goal into a verifiable task plan, execute tasks with the domain's own tools, verify every task with machine-run checks, retry with caps, escalate to a human when budgets exhaust, and refuse to close until everything is verified or explicitly waived. Use when you want an agent or subagent to pick up a goal and drive it to a verified close across one of this repo's 18 domains ('run this goal through the engineering harness', '