I built an AI career skill gap analyzer with Claude Sonnet, FastAPI, and Next.js -- 124 tests, fully open source
Hey IH!
I just launched SkillVector -- an AI-powered career skill gap analysis tool. You paste your resume and a job description, and it gives you:
A deterministic match score (0-100%) using cosine similarity on sentence embeddings
Missing skills ranked by priority (HIGH/MEDIUM/LOW)
A prerequisite-ordered learning path with time estimates
Portfolio project ideas with deliverables (so you can actually prove you learned the skill)
Semantically matched related jobs from a Pinecone vector database
Try it live: https://skillvector-eta.vercel.app
GitHub (open source): https://github.com/RakeshReddy26-bit/skillvector-engine
The tech stack
Backend: Python 3.11, FastAPI, Claude Sonnet (Anthropic) via LangChain, Sentence Transformers (all-MiniLM-L6-v2, 384-dim), Pinecone, Neo4j
Frontend: Next.js 14, TypeScript strict, Tailwind CSS, 9 React components
Infra: Vercel (frontend, free) + Render (backend, free tier)
Tests: 124 pytest tests, all mocked, GitHub Actions CI (lint + test + type-check)
How it works
The core is a 7-step pipeline:
Encode resume + job description into 384-dimensional vectors
Compute cosine similarity for a deterministic match score
Claude Sonnet identifies missing skills through structured prompting
Skills get ordered by prerequisites with time estimates
Evidence engine generates portfolio project ideas
Interview generator creates 5 questions per missing skill
Rubric engine produces evaluation criteria
The score is deterministic (same inputs = same score) because it's based on embedding similarity, not LLM output. The LLM only handles skill identification and project generation.
What I learned building this
Using embeddings for scoring instead of asking an LLM "rate this resume 0-100" gives consistent, reproducible results
Graceful degradation matters -- the app works without Pinecone and Neo4j by falling back to local data
A transform layer between API response and frontend display types saves you from coupling your UI to your API shape
124 tests with full mocking means I can refactor anything without fear
Numbers
55 indexed job descriptions (Junior to Staff level)
32 skills in the prerequisite graph
7 pipeline steps
124 automated tests
$0/month hosting (Vercel free + Render free tier)
The backend sleeps on Render's free tier, so the first request takes ~15-30 seconds to cold start. After that it's fast.
Would love feedback on the analysis quality and the UI. Try the demo mode if you don't want to paste a real resume.
