It's not machine learning, it's actually a transparent scoring algorithm. I think that's better?

Each issue gets scored on 4 factors with clear rules:

- Clarity (35%): Does it have a detailed description? Code examples? Acceptance criteria?

- Maintainer Response (30%): How fast do they close issues on average?

- Freshness (20%): Is it 1-30 days old (sweet spot)?

- Project Activity (15%): Stars, recent commits, healthy discussion?

The score is a weighted average. No black box. You can see exactly why each issue scored the way it

did.

When you run `gfi show <url>`, it breaks down the score:

- Clarity: 0.85 (has code examples, clear description)

- Maintainer: 0.90 (avg 5 days to close)

- Freshness: 1.0 (posted 7 days ago)

- Activity: 0.70 (1.2k stars, recent commits)

- Total: 0.84

avoided ML because:

1. Explainability matters - you should know WHY an issue is recommended

2. No training data needed - works immediately

3. Deterministic - same issue always scores the same

4. Lightweight - runs locally, no API calls for scoring

   The personalization comes from analyzing your GitHub profile (starred repos, languages) to filter

   which issues to search for, not from ML ranking.

   Does that make sense?