Systematic AI-assisted open source contribution framework.
The open source contribution space has a massive gap:
- Beginner tutorials teach you how to fork and make your first PR
- Autonomous AI agents try to replace human contributors entirely
- Nothing in between — no systematic, human-led, AI-assisted methodology for making meaningful contributions to any repo, in any language, at any complexity level
WondrAIWork fills that gap.
A complete, repeatable workflow for finding high-impact open source issues and delivering quality fixes — fast. It's language-agnostic, complexity-independent, and designed for contributors who want to solve real problems, not just collect "good first issue" badges.
The framework pairs a human lead with AI pair programming (Claude Code) across 7 phases:
# Clone this repo
git clone https://github.com/Kanevry/wondraiwork.git
cd wondraiwork
pnpm install
# Find high-impact issues
pnpm discover
# Evaluate a specific issue
pnpm evaluate <owner/repo> <issue-number>
# Set up a target repo for contribution
pnpm setup-target <owner/repo>The full methodology is documented in methodology/:
| Phase | Doc | Time | What |
|---|---|---|---|
| 01 | Discover | 30-60 min | Find issues that matter — scoring matrix, search strategies |
| 02 | Evaluate | 15-30 min | Assess repo health, maintainer activity, competition |
| 03 | Understand | 1-3 hours | Systematically explore unfamiliar codebases |
| 04 | Implement | 2-8 hours | Fix it right, following the target repo's conventions |
| 05 | Submit | 30-60 min | PRs that maintainers want to merge |
| 06 | Respond | Ongoing | Handle reviews professionally, iterate fast |
| 07 | Track | Continuous | Document learnings, automate lifecycle |
See the full process overview for how the phases connect.
- Discovery scripts — Automated search across GitHub for high-impact issues using 5 strategies (reactions, comments, help-wanted, good-first-issue, bugs)
- Evaluation scoring — Quantitative assessment: Impact (40%) x Feasibility (35%) x Visibility (25%)
- Codebase mapping — Templates for systematic exploration of unfamiliar projects
- PR quality gates — Checklists and validation before every submission
- Contribution journal — Structured documentation of learnings and outcomes
- Target tracking — Scored and validated contribution opportunities
Active targets are tracked in targets/, scored by tier:
| Tier | Focus | Count |
|---|---|---|
| Tier 1 — Quick Wins | Low complexity, high merge probability | 3 |
| Tier 2 — High Impact | Meaningful fixes in major projects | 7 |
| Tier 3 — Emerging | Smaller projects, broader impact potential | 8 |
Each target file documents the issue, repo health, scoring, approach, and current status.
Completed contributions are documented in contributions/. Each entry records
what was done, what was learned, and the outcome.
- Human judgment, AI speed — The human decides what to work on and validates the approach. AI handles the tedious parts: searching, reading, drafting.
- Repo-native standards — We follow the target repo's conventions, not our own. Their linter, their commit format, their test framework.
- Quality over quantity — One well-crafted PR beats ten sloppy ones. Every contribution should be something the maintainer is glad to receive.
- Learn in public — The methodology, the targets, the journal — it's all here. Copy it, improve it, make it yours.
WondrAIWork uses AI tools (primarily Claude Code) as a pair programming partner. We believe in full transparency about AI usage in open source contributions.
See our AI Attribution Policy for details on how we handle AI disclosure.
Contributions are welcome — methodology improvements, case studies, script enhancements, new targets, documentation fixes. See CONTRIBUTING.md for guidelines.
MIT — use it however you want.
Built by Bernhard Goetzendorfer with Claude Code.