---
name: "International AEO strategist"
repo: https://github.com/retieedra-profound/skills-marketing-library
source: https://github.com/retieedra-profound/skills-marketing-library/blob/HEAD/profound/international-aeo-strategist/SKILL.md
license: "MIT"
install: "npx skills add retieedra-profound/skills-marketing-library --skill international-aeo-strategist"
page: https://100xlab.com/resources/agent-skills/retieedra-profound-skills-marketing-library--international-aeo-strategist--7r4y53
---


# International AEO Strategist

## Research basis

This skill operationalizes Profound's research, [How query language reshapes AI citations](https://www.tryprofound.com/blog/how-query-language-reshapes-ai-citations), published April 21, 2026.

Key takeaways from the study:

- Profound analyzed 3.25 billion citations across seven models and 14 countries using native-language prompts.
- Query language materially changed citation composition and social-source rates.
- Spanish-language markets showed similar patterns across countries, suggesting language can matter more than geography for some source behaviors.
- Platform preferences diverged: Google AI Overviews used a varied local social mix, while ChatGPT's social citations remained heavily concentrated on Reddit.

These are aggregate observational findings. Validate the target category, engine, locale, and current period before choosing channels.

## Goal

Build a market-specific AEO plan grounded in local-language demand, local sources, engine behavior, and the brand's actual operating footprint.

## Intake

Collect:

- target countries, languages, and language variants
- priority engines and surfaces
- local products, prices, availability, regulations, and entities
- local domains, profiles, publishers, institutions, and communities
- local competitors
- native-language prompt or customer research
- localization owners and review process

## Workflow

1. Separate language from geography in the measurement design.
2. Build prompts from native-language demand; do not translate an English keyword list mechanically.
3. Preserve local phrasing, category names, currencies, units, locations, and decision criteria.
4. Run prompts on each target engine and classify cited sources.
5. Compare owned, earned, institutional, social, marketplace, and competitor citation shares.
6. Identify which local platforms and authorities recur in each engine.
7. Audit brand facts for local accuracy: products, prices, availability, policies, legal claims, and support.
8. Distinguish translation gaps from information gaps and entity inconsistencies.
9. Recommend channels only when the local citation market supports them.
10. Assign native-language review and freshness ownership.
11. Measure each language, country, and engine separately before rolling up.

Do not assume Reddit, YouTube, LinkedIn, TikTok, or a global publisher matters equally in every language.

## Output

Return:

1. Market prioritization with business value and evidence confidence.
2. Native-language prompt portfolio.
3. Engine-by-market citation map.
4. Local source and citation-neighbor shortlist.
5. Fact and entity consistency ledger.
6. Localized content and distribution plan with owners.
7. Measurement framework preserving language, geography, engine, and date.
8. Unknowns requiring native-speaker or legal review.