What if your code editor could do keyword research, audit your SEO, and optimize your content for AI search engines — without leaving VS Code?
I built a set of open-source agent skills that turn GitHub Copilot into a hands-on marketing strategist. Here's what I learned, how they work, and how you can use (or build) your own.
The problem
I run a small website. Every time I wanted to write a blog post, the workflow looked like this:
Open Ahrefs/Semrush → research keywords
Open Google Docs → write the article
Open WordPress → publish and fill in SEO fields
Open ChatGPT → rewrite for social media
Repeat
Five tools. Five context switches. And every time, I found myself giving the same SEO instructions to AI assistants: "Use the keyword in the first paragraph", "Write a meta description under 155 characters", "Structure with H2/H3 headings"...
What if I could encode all that knowledge once, and have my AI assistant apply it automatically?
The solution: Copilot Skills
GitHub Copilot (in agent mode) can read SKILL.md files from your workspace. These files act as knowledge modules — they tell Copilot how to approach a domain, what rules to follow, and what frameworks to apply.
I created four skills that cover the full marketing workflow:
seo-keyword-research → Find what your audience searches for
↓
seo-strategy → Structure your site to rank
↓
geo-optimization → Get cited by AI search engines
↓
content-creator → Write content that ranks AND gets cited
What's inside a skill?
A skill is just a Markdown file with structured knowledge. Here's a simplified look at the GEO (Generative Engine Optimization) skill:
name: geo-optimization
description: "\"Optimize content for AI search engines like ChatGPT, "
Gemini, and Perplexity. Use when the user mentions 'AI visibility',
'GEO', 'AI search', or 'cited by AI'."
GEO — Generative Engine Optimization
What is GEO?
GEO = optimizing content so AI-powered search engines cite your
brand in generated answers.
GEO Scorecard (16 criteria)
| Criterion | Target |
|---|---|
| Authoritative tone | Expert language, no hedging |
| Structured data | Schema markup present |
| ... |
Anti-patterns
| Don't do this | Why |
|---|---|
| Keyword stuffing | AI recognizes and skips it |
| ... |
The description field is key — Copilot uses it to decide when to invoke the skill. The body contains the actual knowledge the AI applies.
How it works in practice
Here's what a real session looks like:
Me: "Do keyword research for a blog about home espresso machines"
Copilot (automatically invokes seo-keyword-research):
Searches the web for related keywords
Classifies intent (informational, commercial, transactional)
Analyzes SERP features
Outputs a structured keyword map with primary/secondary keywords, difficulty estimates, and content type recommendations
Me: "Now write the article and optimize it for AI search"
Copilot (invokes content-creator + geo-optimization):
Writes with proper H2/H3 structure
Includes the keyword in the first paragraph
Adds statistics with citations (GEO requirement)
Uses authoritative tone
Ends with a clear conclusion (AI search engines need this)
Generates meta title and description within character limits
Me: "Publish as draft to WordPress"
Copilot (uses REST API knowledge from seo-strategy):
Creates the post via wp-json/wp/v2/posts
Sets Yoast SEO meta fields
Outputs the draft URL
Total time: ~15 minutes. From zero to published draft with SEO metadata.
The GEO angle: why AI search changes everything
Here's something most developers don't think about: when someone asks ChatGPT "what's the best espresso machine for beginners?", the answer comes from somewhere. That somewhere could be your website — but only if your content is structured in a way AI models can parse and trust.
This is called Generative Engine Optimization (GEO). It's like SEO, but for AI search engines. The research (Aggarwal et al., 2024) shows that GEO techniques can boost visibility in AI-generated answers by up to 40%.
My geo-optimization skill includes:
A 16-criterion scorecard for AI-readiness
Writing guidelines that balance human readability and AI parseability
Anti-patterns (things that make AI skip your content)
E-E-A-T implementation (Experience, Expertise, Authoritativeness, Trust)
What I learned building these skills
- Skills need evidence, not opinions AI assistants hallucinate. If your skill says "meta descriptions should be 155 characters" without a source, Copilot might second-guess it or contradict itself later.
I added a sources file with links to Moz, Ahrefs, Google Search Central, and peer-reviewed papers. This makes the skill more reliable and more trustworthy for users.
- The description field is your trigger Copilot decides which skill to invoke based on the description in the YAML frontmatter. Be specific about trigger words:
description: "Use when the user mentions 'SEO', 'search engine
optimization', 'Google rankings', 'rank higher', 'meta tags'..."
The more trigger phrases you include, the more reliably it activates.
Structure > prose
Tables, bullet lists, and clear headings work better than long paragraphs. The AI parses structured content faster and applies it more consistently.Skills should reference each other
My skills cross-link: the SEO strategy skill says "for keyword research, see seo-keyword-research". This helps Copilot chain skills together in multi-step workflows.
Installation (30 seconds)
git clone https://github.com/helder-ai/marketing-skills.git \
.github/skills/marketing
That's it. Open VS Code with Copilot agent mode, and start asking marketing questions. Copilot auto-discovers the skills.
Or as a Git submodule (stays updatable):
git submodule add https://github.com/helder-ai/marketing-skills.git \
.github/skills/marketing
Build your own skills
The pattern works for any domain knowledge:
Create a SKILL.md in .github/skills/your-skill/
Add YAML frontmatter with name and description (include trigger words)
Write structured knowledge — tables, checklists, decision trees
Add references for any factual claims
Test it — ask Copilot questions in the skill's domain and see if it gets invoked
Domains that would benefit from this approach:
Accessibility (WCAG guidelines as a skill)
Security (OWASP Top 10 as actionable checklists)
API design (REST/GraphQL conventions)
Legal/compliance (GDPR, cookie consent rules)
Brand guidelines (tone of voice, terminology)
The repo
GitHub: helder-ai/marketing-skills
Licensed CC BY-NC 4.0 — free to use, adapt, and learn from.
If you find it useful: ⭐ the repo, or open a PR if you have improvements. I'd especially love contributions from SEO practitioners who want to encode their expertise.
What domain knowledge would you encode as a Copilot skill? Let me know in the comments!
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