GTM Engineer Skills: A Complete Workflow for Website AEO/GEO Optimization in the AI Era

This article introduces the open-source project gtm-engineer-skills, a Claude Code-based skill set for AI engine optimization. It covers the full GTM engineering workflow from brand research, keyword mining, GEO content planning to code-level optimization, aiming to help websites improve visibility in generative AI engines like ChatGPT and Perplexity. The project uses a modular design, supporting flexible combination of skill components, and is suitable for content, technical, and growth teams to build website optimization capabilities in the AI era.

Traditional SEO has long been the core method for website traffic, but the rise of AI conversational engines like ChatGPT has changed how users access information—more and more people ask AI directly instead of using search boxes. This gave birth to AEO (Answer Engine Optimization) and GEO (Generative Engine Optimization): unlike SEO which focuses on search rankings, AEO/GEO focus on whether website content can be understood, cited, and recommended by AI. If a website cannot be effectively parsed by AI, even with good SEO, it may miss out on AI-native traffic. The gtm-engineer-skills project is an end-to-end solution designed to address this challenge.

The core concept of gtm-engineer-skills is the 'skill pipeline'—each skill handles a specific component and outputs standardized files for downstream use, supporting flexible combinations (run the full process or execute individual components). The entire workflow includes 10 core skills divided into 6 phases:

1. Brand Foundation Research: Generate brand_dna.md;
2. Multi-dimensional Research (parallel): Keyword mining, Reddit opportunity analysis, GEO prompt research;
3. Content Architecture Planning: Integrate multi-source data to output content architecture;
4. Content Production (parallel): Write dual-optimized long-form articles, generate AI-parsable charts;
5. Content Audit: Verify authenticity and link validity;
6. Implementation (parallel): Build resource pages, code-level AEO/GEO optimization.

The most technically valuable module in the project is improve-aeo-geo, which directly operates the codebase to implement optimizations. The strategies include:

1. Structured Data Markup: Add/improve JSON-LD markup (Schema.org);
2. Semantic HTML Refactoring: Use tags like article/section to ensure DOM structure matches visual presentation;
3. Content Extractability Optimization: Optimize body structure, remove interfering elements;
4. Technical Performance Tuning: Code splitting, critical CSS inlining, image lazy loading;
5. Multimodal Content Adaptation: Generate text descriptions and data tables for charts;
6. Citation-Friendly Design: Optimize URLs, add canonical links, clarify text anchors.

The practical value of the project is reflected in:

• Content teams: Through geo-content-research and write-seo-geo-content, generate 'AI-native' content based on real AI user questions;
• Technical teams: improve-aeo-geo and build-resource-pages convert optimizations into code changes (submit PRs), lowering implementation barriers;
• Growth teams: Provide full GTM engineering capabilities from research to release, enabling the establishment of internal AI optimization capabilities without relying on external agencies. Each component has clear inputs and outputs, making it transparent, controllable, and auditable.

When using the project, note the following:

1. Model Differences: Codex has built-in Reddit access capability, while Claude usually does not—this affects the output of the reddit-opportunity-research skill;
2. Baseline Assessment: Use aeo-audit.sh to evaluate the website's AI visibility baseline, making it easier to measure optimization effects;
3. Content Audit: The audit-content skill can capture some issues, but manual review is still necessary to avoid incorrect information being spread by AI (high recovery costs).

AI is reshaping how information is accessed. AEO/GEO are not replacements for SEO, but their evolutionary form. gtm-engineer-skills provides an engineering methodology to help teams systematically build website visibility in the AI era. The project uses the MIT license, supporting free modification and expansion. The standardized SKILL.md format can be integrated into existing AI-assisted development workflows, making it a worthwhile project to adopt for maintaining competitiveness in the AI search era.