# 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:

```bash
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.