gfi-scraper 0.1.1

Discover, rank, and get personalized recommendations for 'good first issue' contributions

🎯 Good First Issue Finder

Discover, rank, and get personalized recommendations for "good first issue" contributions in Canonical's GitHub repositories.

Features

• Scrape all open "good first issues" across 76+ Canonical repos (206 issues)
• Rank using a weighted heuristic (freshness, competition, availability, popularity, activity, linked PRs)
• Match issues to your developer profile using GPT-5.5 (single API call, ~$0.002)
• Cache & diff between runs — see what's new since last time
• Auto-refresh with --watch mode or cron scheduling
• Beautiful TUI — browse, filter, and match interactively in the terminal

Quick Start

Prerequisites

• Python 3.12+
• gh CLI authenticated (gh auth login)
• OpenAI API key (for LLM matching only)

Install

pip install .

# Or for development (editable install with test dependencies)
pip install -e ".[dev]"

Run

# 1. Scrape and rank all issues
gfi-scrape

# 2. Interactive TUI (browse, filter, match)
export OPENAI_API_KEY='sk-...'
gfi-tui

# 3. Or use the headless matcher
gfi-match

Project Structure

├── pyproject.toml
├── README.md
├── docs/
│   ├── architecture.md        # System design & data flow
│   └── scoring.md             # Ranking heuristic explained
├── src/gfi_scraper/
│   ├── __init__.py
│   ├── scrape_good_first_issues.py   # Scraper + ranker + cache
│   ├── match_issues.py               # LLM-powered matcher
│   └── tui.py                        # Interactive terminal UI
├── tests/
│   └── test_all.py            # 96 unit tests
├── .cache/                    # Run-to-run diff cache (gitignored)
└── good_first_issues.csv      # Latest scraped results

Usage

Scraper

# Basic run
gfi-scrape

# Custom org
gfi-scrape --org ubuntu

# Auto-refresh every 4 hours
gfi-scrape --watch --interval 4

# Generate crontab entry
gfi-scrape --cron

TUI

gfi-tui

Key	Action
b	Browse all issues (paginated)
n	What's new (since last run)
f	Filter by keyword
d	Detail view of a specific issue
m	Match to your profile (LLM)
s	Stats overview
q	Quit

Matcher (headless)

gfi-match --top 15

How Scoring Works

Each issue is scored 0–100 using a weighted composite:

Signal	Weight	Logic
Freshness	25%	Exponential decay (half-life: 180 days)
Competition	25%	Fewer comments = higher score (cap: 10)
Availability	20%	No assignees = 100, decays per assignee
Popularity	15%	Repo stars, log-scaled
Activity	10%	Staleness gate (updated within 1 year?)
PR Status	5%	Open PR = competition penalty

Testing

python3 -m pytest tests/ -v

96 tests covering: scoring functions, body extraction, CSV round-trips, caching/diffing, GraphQL parsing, LLM prompt building, TUI helpers, integration, and edge cases.

Cost

• Scraping: Free (uses gh CLI with your GitHub token)
• LLM matching: ~$0.002 per run (single GPT-5.5 call, ~10k tokens)

License

MIT