I recently worked on the “Build the Ultimate Web Crawler Agent with Firecrawl” (March n8n challenge) and ended up building something pretty useful for dev workflows.

💡 The problem

If you’ve ever evaluated an npm package, you know the drill:

• Check npm downloads
• Open GitHub → stars, issues, commits
• Look for activity / maintenance
• Compare alternatives

Takes like 15–30 minutes per package

🚀 What I built

I created an AI-powered package evaluator that answers:

👉 “Should I use this package or not?”

You just input a package name, and it gives you a full breakdown.

⚙️ How it works

• 🔥 Firecrawl → finds npm + GitHub URLs dynamically
• GitHub API → stars, issues, last commit
• npm API → weekly downloads
• 🤖 AI agent → converts raw data into insights + recommendation

📊 Output (this is the interesting part)

Instead of just numbers, it gives:

• Risk score → Low / Medium / High
• Adoption level → Very popular / Niche
• Issue health
• Alternatives (with trade-offs)
• Final recommendation → Use / Consider / Avoid

Also separates:

• Observed facts (data)
• Inferred insights (AI reasoning)

😅 Challenges I hit

• Scraping npm/GitHub pages didn’t work well (JS-rendered data missing)
• AI-only approach was slow and inconsistent
• Mapping correct GitHub repo dynamically was tricky
• Handling invalid packages + edge cases took more effort than expected

🔑 Biggest takeaway

The best combo ended up being:

👉 Firecrawl (discovery) + APIs (reliable data) + AI (reasoning)

🤔 Curious

Would you actually use something like this before choosing a library?

Or do you prefer manual evaluation?

Happy to share more details if anyone’s interested 👍

Check out the workflow here : https://n8n.io/workflows/14911