good-egg

edictions for the contributors who actually need scoring: unknown people with a handful of merged PRs. Merge rate alone outperforms merge rate plus graph at every tier we tested. The new default model is merged / (merged + closed). We also pulled account age out of the score into a separate advisory after DeLong tests showed it adds nothing once you condition on merge rate. The post has the full data, including the tables. Next we're working on content scoring (does this PR fit this repo's conventions?) and cold-start tooling (helping new contributors understand project expectations before they submit). Contributor reputation is one input to review triage. The PR itself carries more signal. Repo: https://github.com/2ndSetAI/good-egg pip install good-egg Or just run it via uvx.

our didn't. The most surprising failure: text similarity between PR descriptions and project READMEs predicted lower merge rates. We published all of it, including the failures. More detail on the Substack post. Repo: https://github.com/2ndSetAI/good-egg (MIT, pip install good-egg). Runs as a CLI, GitHub Action, Python library, and MCP server.

ood behavior -- merged PRs in established repos, sustained contributions over time, work across multiple projects. That track record is a strong signal, and it already exists in the GitHub API. How it works: - Builds a bipartite contribution graph (users ↔ repositories) from merged PRs - Applies personalized graph scoring biased toward your project and language ecosystem - Accounts for recency decay, repository quality (stars, language normalization), and anti-gaming measures (self-contribution penalties, per-repo caps) - Classifies contributors as HIGH / MEDIUM / LOW / UNKNOWN / BOT The methodology doc goes into the full detail: https://github.com/2ndSetAI/good-egg/blob/main/docs/methodol... Runs four ways: - GitHub Action: drop it into any PR workflow and get a comment with the score - CLI: good-egg score <user> --repo <owner/repo> - Python library: await score_pr_author(login, repo_owner, repo_name, token) - MCP server: plug it into Claude or other AI assistants On Vouch and the circle-of-trust approach: Mitchell Hashimoto's Vouch takes a different angle: maintainers manually vouch for contributors they trust, building a web-of-trust. I think that's a valid approach and have seen circles of trust work well (on PyTorch specifically, where contributors came from all over, including major corporate partners). But I've also seen gaps that could easily be filled by a bit of data that already exists. Vouch requires active maintainer participation in a separate system and has a cold-start problem. Good Egg is complementary. It's automated, doesn't ask maintainers to do extra work, and works from day one on any repo. What it doesn't do: Good Egg doesn't send data to any remote service. It reads from the GitHub API, computes locally, and that's it. I'm not building a training set or a contributor database. This is just a tool for the community. Configuration and extensibility: Scoring parameters (thresholds, graph weights, recency decay, language multipliers) are all configurable via YAML or environment variables. More extensibility is planned, particularly around additional data sources (e.g., GitLab) and methodology variations like graph-based project relatedness and incorporating review/issue activity alongside PRs. Code: https://github.com/2ndSetAI/good-egg PyPI: pip install good-egg Docs: https://github.com/2ndSetAI/good-egg/tree/main/docs