🚀 From Late-Night Pings to AI Automation: How We Built a GitHub Copilot Agent That Automates Open Source Contributions

Ever get that late-night ping from a colleague with “hey I did this thing, what do you think…”

That’s exactly how this story started. Paul Yuknewicz had built an impressive Azure Functions template but was facing the tedious reality of manual submission processes to open source repositories.

His question was simple: “Can we automate this?”

The answer changed everything.

The Problem We Solved 🎯

The awesome-azd repository requires contributors to: ✅ Analyze repository structure and technologies ✅ Map detected technologies to specific tags ✅ Create properly formatted JSON entries ✅ Follow precise PR templates ✅ Ensure compliance with contribution guidelines

Time investment: 30+ minutes per submission ⏰

Our Innovation: AI Building AI 🤖

We didn’t just create a helper tool—we built a true GitHub Copilot agent using VS Code’s experimental prompt files feature.

Here’s the workflow we automated:

User creates issue with repo URL → Assigns @github-copilot → Copilot analyzes repository → Technology detection → Tag mapping → JSON generation → Image detection & download → PR creation → Automated validation

The Meta-Moment 🔄

The most fascinating part? We used GitHub Copilot (powered by Claude Sonnet 4) to build the prompt that powers GitHub Copilot.

By feeding the AI examples of successful submissions, contribution guidelines, and problem context, we iteratively refined a 500+ line prompt through conversational development.

AI-assisted prompt engineering proved essential for capturing nuanced requirements that would be impossible to encode manually.

Technical Innovations That Matter 💡

🔹 Intelligent Technology Detection: Analyzes file structures, dependencies, and infrastructure definitions automatically

🔹 Smart Tag Mapping: Maps 25+ Azure services, 10+ programming languages, and 15+ frameworks to appropriate classification tags

🔹 Live Validation: Actually builds and runs the website locally at localhost:3000 to verify changes

🔹 Guidelines-Based Automation: References official contribution guidelines instead of hardcoding rules

🔹 End-to-End Testing: Downloads images, validates JSON schema, ensures alphabetical ordering

The Transparency Factor 👀

One of the coolest aspects? You can watch Copilot work through the “View Session” feature in GitHub PRs.

We literally observed the AI:

Compile the website locally
Navigate to verify the new template appeared correctly
Validate image display and metadata integration
Ensure the gallery functions properly

This transparency builds massive confidence in AI automation.

Real-World Results 📊

Before our automation: ❌ 30+ minutes of manual analysis ❌ Risk of formatting errors ❌ Complex tag mapping decisions ❌ Manual image handling ❌ PR template compliance challenges

After: ✅ Assign @github-copilot to an issue ✅ Automatic analysis and PR creation ✅ Zero manual formatting ✅ Intelligent tag selection ✅ Built-in compliance validation

The Validation Moment 🎉

Test case: GitHub Issue #594 Result: Auto-generated PR #595 with perfect formatting, appropriate tags, and automatic image detection.

It actually worked. 🔥

Key Lessons for AI Automation 📚

1️⃣ Start with Real Use Cases: Paul’s actual repository provided the perfect test case

2️⃣ Iterate Based on Feedback: Each iteration improved based on actual usage patterns

3️⃣ Reference Official Guidelines: Don’t hardcode—reference living documentation

4️⃣ Use AI to Build Better AI: Conversational prompt development captures nuances impossible to encode manually

5️⃣ Embrace Transparency: Being able to watch AI work builds trust and provides learning opportunities

Looking Forward 🔮

This pattern can be adapted for: 🔹 Documentation generation from code analysis 🔹 Automated testing strategy suggestions 🔹 Security compliance validation 🔹 Dependency management automation

The use of prompt files as a standardized approach means these automations can be shared across repositories, version controlled, and collaboratively improved.

The Meta-Reality ✨

Here’s the kicker: This entire LinkedIn post was also written by GitHub Copilot as a wrap-up of our experience building the solution. Also the image 🤖.

My Prompt - “Can you create a LinkedIN blog post summarizing our journey to attract engagement and also give me a prompt to use for an image creation in Bing Image Creator or ChatGPT for the header image.”

It’s AI all the way down. 🤖

Want to see the magic in action? 🔗 View the agent session: GitHub PR #595 Agent Session 🔗 Full prompt file: Available in the awesome-azd repository at .github/prompts/awesome-azd-pr.md

What automation challenges are you facing in your projects? Drop a comment—I’d love to help brainstorm AI-powered solutions! 💭

Resources

GitHub Issue Example: #594
Generated PR: #595
Awesome AZD Repository: github.com/Azure/awesome-azd
Contribution Guidelines: azure.github.io/awesome-azd/docs/contribute/

#AI #GitHub #Copilot #Automation #OpenSource #Azure #MachineLearning #DevOps #Innovation #TechLeadership

The next time someone pings me late at night with “hey I did this thing,” the response might just be: “Assign Copilot to an issue, and let’s see what happens! 🚀”

Originally published on LinkedIn.