The Problem with Resumes as Data
Resumes are usually flat files (PDFs/Word). But skills aren't flat—they are a network. If you know React, you likely know JavaScript, JSX, and Virtual DOM. If you're missing Redux, the "gap" between you and a Senior Frontend role isn't a missing word on a page; it's a missing node in your professional graph.
I built SkillNode to treat career paths as what they actually are: Knowledge Graphs.
The Architecture: Neo4j + Gemini AI
1. Why Neo4j?
Traditional SQL joins are a nightmare for "pathfinding" (e.g., Find the shortest learning path from Node.js to Cloud Architect).
In Neo4j, I modeled this using:
- Nodes: Skills, Job Roles, and Learning Resources.
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Relationships:
REQUIRES,IS_PREREQUISITE_FOR, andHAS_SKILL.
This allows for lightning-fast traversal to identify exactly where a user's current "cluster" of skills ends and the target role begins.
2. The Logic Layer: Gemini AI
I use Gemini AI to handle the unstructured heavy lifting.
- Skill Extraction: It parses the user's project descriptions to identify "implied" skills that an ATS might miss.
- Path Generation: Once Neo4j identifies the gap, Gemini generates a personalized learning roadmap that explains why these specific bridges are needed based on the user's existing tech stack.
Key Takeaway
Don't let an ATS loop define your worth. If you believe your projects speak louder than your resume, you need to see your skills as a network, not a list.
Check out SkillNode here: SkillNode
I'd love to hear your thoughts: Have you ever used Graph databases for non-social network apps? Let’s chat in the comments!
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