cloudy-profile-matching/README.md

Profile Matching - Public Code Sample

This is a small, dependency-free portfolio excerpt adapted from Cloudy, an AI-assisted profile and matching service I built for a collaboration platform. The production repository is private, so this project intentionally contains only the self-contained scoring layer and its tests. It includes no user data, credentials, API code, or project-specific infrastructure.

What it demonstrates

• transparent, explainable matching across skills, interests, location, and shared project tags;
• input normalisation and explicit handling of incomplete profile data;
• optional affinity weights that personalise recommendations without changing existing behaviour when no preferences are set;
• pure business logic separated from database and API concerns;
• executable tests for normal cases, edge cases, and scoring invariants.

The actual application combines this score with an asynchronous PostgreSQL repository layer and uses it to rank potential collaboration matches.

Run the tests

The sample uses only the Python standard library:

python -m unittest -v

Why this is good code

The model is deliberately small and deterministic. Each function has one responsibility, is type annotated, and can be tested without a database, LLM, or network access. Missing location data never creates a false positive, and the weighted extension remains backwards-compatible with the original Jaccard similarity model. That keeps the recommendation logic understandable for users and safe to evolve in a larger product.