Lysis

Beginner-friendly GitHub issue ranker. Give it a repo and it ranks the open issues by how likely a first-time contributor is to get a PR merged for them.

Labels like good first issue are often stale or wrong, so Lysis learns from what actually happened: issues that first-time contributors really closed.

How it works

1. Ingest: fetch repos, issues and merged PRs from the GitHub API (ETag-cached).
2. Label: an issue is positive if a first-time contributor's PR closing it was merged.
3. Model: gradient boosting on issue and repo features, evaluated with repo-grouped cross-validation.
4. Rank: score open, unassigned issues, then decay by age so stale issues sink.

Results

5-fold repo-grouped CV on closed issues (base rate 0.10):

Ranker	AP	AUC
random	~0.10	0.50
labels-only baseline	0.245	0.678
gradient boosting	0.278	0.753

This is an early prototype. The model beats the baseline, but the signal is modest.

Quick start

git clone https://github.com/OpenRootdev/Lysis.git
cd Lysis
python -m venv .venv
.venv\Scripts\activate        # Windows (macOS/Linux: source .venv/bin/activate)
pip install -r requirements.txt
cp .env.example .env          # add a read-only GITHUB_TOKEN

Build the dataset (once, takes a while, cached):

python src/lysis/ingestion/fetch.py
python src/lysis/features/labels.py
python src/lysis/models/evaluate.py

Rank issues:

python src/lysis/cli.py psf/requests
streamlit run src/lysis/app.py

Project layout

src/lysis/
  ingestion/   GitHub client and repo fetching
  features/    labels and feature building
  models/      baseline, gradient boosting, evaluation
  cli.py       command line ranking
  app.py       Streamlit UI
tests/

Known limitations

• Repo stats come from fetch time, not issue time (a small leak; removing them costs about 0.025 AP).
• Only Python repos so far.
• Labels depend on PRs linking issues with closes #N.

Contributing

See CONTRIBUTING.md. Look for issues labeled good first issue.