io.github.adityatiwari101104/contribos

ContribOS

Get trusted. Get merged. Come back. ContribOS helps new open-source contributors earn trust. It finds where you're welcome, shows how this repo wants the change done, checks that you truly understand your change, coaches you through review, and turns merged PRs into a record the next maintainer can verify.

In 2026, writing code is not the hard part of contributing. AI made PRs cheap, maintainers answered with PR caps, vouch lists and AI policies, and newcomer merge rates fell. ContribOS makes your PR worth a maintainer's time.

ContribOS never opens PRs, posts comments or claims issues for you, and it never writes your explanation. It points, and you decide.

The journey

Step	Command	What you get
Find	contribos find --lang python --topic web	Open issues sorted into Good bet / Possible / Skip, each with evidence: claimed? open PR? maintainer active? clear? Plus repo welcome: archived, vouch list, AI policy, outside-PR merge rate, how long outside PRs wait for a first human reply, how many stall, a stale bot, and your own open PRs there
Rules	contribos policy owner/repo	The repo's AI policy, disclosure format (Assisted-by: and similar trailers), vouch gate, "no AI on good first issues", issue-first and claim-first norms, tests, changelog, DCO/CLA, style tools and activity, each quoted with file:line. --json gives the same rules in a form agents can read
Propose	contribos claim <issue-url>	A short proposal to post before coding: likely files, a similar-size past PR, where the test goes, and one real question. Warns about taken issues, no maintainer yet, good-first-issue AI rules and issue-first rules. If the repo has a vouch list, it drafts the introduction first
Tone	contribos tone comment.md --kind comment	Flags machine-written tells (delve, buzzwords, assistant openers, filler), length, a missing question and leftover placeholders in anything you're about to post
Agent rules	contribos agent-rules . --ai "Claude Code"	Teaches your coding agent this repo's rules: a SKILL.md for Claude Code, Codex, Copilot and Cursor, a hook that stops the agent opening PRs or posting comments, and a commit-msg hook for sign-off and the AI-disclosure trailer. All local-only, excluded from git
Setup	contribos setup owner/repo	Exact steps taken from the repo's CI and contributing guide: runtime version, install, services, env vars, test and lint commands
Diagnose	contribos setup --diagnose output.txt	What a failure means: missing dependency, wrong version, service down, env var, native build tools, flaky test, or a real test failure (and how to check it isn't yours)
Understand	contribos brief <issue-url>	Files to read with the reasons each was picked, similar past PRs and what reviewers said, related tests, look-alike files to leave alone, house rules, and likely review questions. Add --llm for a plain-words summary that is grounded in the evidence
Prove	contribos check --explain explain.md --verify-test "pytest tests/test_x.py" --issue 123 --ai "Claude Code: drafted the test" --pr-draft pr.md	Pre-submit review: scope versus similar past changes, tests, changelog, sign-off, debug leftovers, AI policy and disclosure trailers, competing PRs for the same issue. --verify-test runs your test with the fix and again with your source files swapped back to the base version, so you can show it fails without your change. It checks that your explanation covers every changed file and states what you're unsure of, then drafts the PR: what and why, how you verified it, what you're unsure about, related past changes, AI assistance
Respond	contribos review <pr-url> --path .	Each reviewer comment classified (blocking, change, question, nit…), the code it points at, the house rule it echoes, a reply draft, what's still unanswered and for how long, and failing CI
Grow	contribos record <github-user> --html me.html	A public, linkable record: merged PRs, change requests, whether you answered every review comment, whether you explained your change and disclosed AI use
Agents	contribos mcp	Nine MCP tools for Claude Code, Cursor, Codex and others (policy, brief, setup, diagnose, check, review, find, claim, tone), taking text as input where an agent has text: claude mcp add contribos -- python -m contribos mcp

Install

pip install contribos     # Python 3.10+, git. Zero external dependencies.
export GITHUB_TOKEN=...   # needed for find, claim/brief from an issue URL, review, record

Everything that can run offline does: policy, brief --title, claim --title, setup, check, bench and review --data need only git. Add --offline to skip the API, and --update to fetch new commits. Clones and indexes are cached in ~/.cache/contribos (CONTRIBOS_CACHE).

Optional AI summaries: CONTRIBOS_LLM=anthropic (with ANTHROPIC_API_KEY) or CONTRIBOS_LLM=openai (with OPENAI_API_KEY), plus optional CONTRIBOS_MODEL. The model only explains evidence ContribOS already gathered. Any sentence citing a file outside that evidence is dropped.

How it works

• repo.py handles cached clones. It reads any revision with git show and git grep, so no checkout is needed.
• precedent.py builds a SQLite index of main-line changes (squash and merge PRs), the files they touched, and the issues they fixed.
• policy.py is the policy radar, built from contribution docs, AI policies, templates, vouch lists, CI config and history.
• brief.py ranks files by rare-term matches in code, path matches, and files touched by similar past PRs. It also finds related tests via co-change history.
• check.py is the pre-submit check, the proof-of-understanding check and the PR draft.
• setup_doctor.py produces setup steps from CI and docs, and diagnoses failures.
• find.py checks issue takeability, repo welcome and responsiveness.
• claim.py drafts the proposal and vouch introduction.
• proof.py runs a test with and without your change (files restored in finally).
• tone.py checks text you're about to post.
• agent_rules.py writes the local agent skill and hooks.
• review.py classifies review comments, drafts replies and tracks follow-through.
• record.py builds the contribution record (markdown plus a self-contained HTML page).
• llm.py is the optional provider layer (Anthropic or OpenAI) with citation grounding.
• mcp_server.py is a dependency-free MCP stdio server.
• github.py is the optional API client. Every caller handles its absence.
• bench.py runs the history benchmark.

Benchmark (2026-09-28, offline, PR titles standing in for issues)

Repo	Brief hit@5	Grep-only hit@5
pallets/flask (30 PRs)	0.77	0.73
pytest-dev/pytest (30 PRs)	0.87	0.87

Matching against past PRs barely improves file finding, which supports the plan's bet that finding files is a commodity. The value is in how past PRs did it and what reviewers asked. The next benchmark needs real issue text and review comments, which requires GITHUB_TOKEN.

Status

• Tested on real repos (Flask, pytest, Ghostty): policy, brief, claim --title, setup, check, bench, mcp.
• Tested against the live GitHub API on 2026-10-01: find (scored issue takeability and maintainer response medians on maximilianfeix/proxy-scraper and brekkylab/backlot), claim <url> (drafted pre-coding proposals with past similar PRs and file targets), and record (verified public merged PR portfolio for @adityatiwari101104).
• Tested on temporary git repos: agent-rules (git status stays clean, the guard blocks gh pr create, the commit-msg hook enforces sign-off and the trailer), check --verify-test, tone, policy radar v2.
• Tests: python -m unittest discover -s tests (40 tests, CI matrix on Linux, macOS, and Windows).

Releasing

To publish a new release to PyPI:

python -m build
twine upload dist/*
git tag v<version>
git push origin v<version>