no-human-ai/no_human - Open Source PR Review Scorecard

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C-Rank Grade: A (Welcoming) - 58/100

External PR Merge Rate: 3%

Response Time: 2h

First Timer Success: -

Frequently Asked Questions

Is no-human-ai/no_human welcoming to first-time open-source contributors?

no-human-ai/no_human has a recorded first-timer success rate of 0.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.

How fast can I expect code review feedback on my pull request?

Maintainers in no-human-ai/no_human respond to incoming external pull requests in approximately 1.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 58.2 C-Rank™ score (A Tier) represent?

The C-Rank™ system evaluates GitHub projects on a 0–100 scale using real data: PR merge rates, review turnaround time, active maintainer presence, and first-time contributor success. A score of 58.2 places no-human-ai/no_human in the A tier.

What is the external contributor pull request merge rate for no-human-ai/no_human?

The external contributor pull request merge rate for no-human-ai/no_human is 2.8%, based on public PR activity from non-core contributors.

Are there Good First Issues available in no-human-ai/no_human?

no-human-ai/no_human currently has 4 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".

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no-human-ai/no_human

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Active (30d)A•Welcoming(58/100)Python

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AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
8.3/10
Patience Score
8.6/10
Nitpick Rate
20%

Highly welcoming maintainers in no-human-ai/no_human. Prompt code reviews with positive guidance for new contributors.

What Contributors Actually Say

Discussions in no-human-ai/no_human focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.

Hidden Friction Signals

Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.

Unwritten Rules

  • •1) Keep PR scope strictly aligned with the linked issue.
  • •2) Ensure local linters pass before opening a review.
  • •3) Maintain full test coverage for modified logic.

Top PR Submission Do's

  • •Add or update tests for changed behavior before requesting review
  • •Update docs or README when behavior changes
  • •Link the issue and include reproduction context in the PR

Top PR Friction Pitfalls (Don'ts)

  • •Do not leave requested test coverage unresolved
  • •Do not ship user-facing changes without matching docs
  • •Do not open context-free PRs
Response Velocity
1 hours
Standard maintainer review cycle

Average Response Latency

Tracks hours until a maintainer leaves a review, comment, or PR response.

Merge Efficiency
2.8%
Selective PR acceptance rate

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
0.0%
No first-timer merges recorded in window

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
6 core
Highly collaborative maintainer core
Diagnostic Health HUD
2.8%
Merge Gauge
0.0%
1st-Timer
Community Vibe30/100

Embed C-Rank Badge

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GetMerged C-Rank badge for no-human-ai/no_human
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Active Good First Issues (5)

View on GitHub

Problem _DIFF_CAP = 60_000 (review/reviewer.py) applies to the diff in the main review, not only the angle passes. Git's path order means tests/ is usually what gets cut. 2026-09-09 to 2026-09-15 Reviews gate reviews checked 269 diff truncated (median full size 91k chars) 141 a changed file with neither patch nor full text in the prompt 121 of those, no tool call naming that file 43 of those 43, PASSED 26 Proposal Budget the diff per file, so every changed file's patch appears up to its share. List every file whose patch was cut, by path, in the prompt. If a non-excluded changed file was never inspected, route the result through the existing ReviewerUnavailable path (re-run, then escalate). Never PASS, and never spend one of the coder's attempts on a reviewer omission. Exclusions come from a trusted, documented list, not from files the coder can edit. Acceptance criteria With a diff over the cap, every changed file's patch appears up to a per-file budget and cut fi

📅 Opened Sep 15, 2026💬 2 comments
Quality: 90/100Contribute

Found while unblocking PR #308 (an external contributor's PR, red on this). Reproduced on that branch with the tool from main. What happens tests/test_readme_claims.py fails on citation drift and the failure message says: Run uv run python scripts/reanchor_citations.py --apply to re-anchor it; the symbol resolves, so the rewrite is exact Running exactly that leaves the tree worse than before and exits FAIL. A citation is pinned in two places — the prose in docs/*.md and the CITATION_TABLE row in tests/test_readme_claims.py. The tool's own module docstring says it rewrites both ("CITATION_TABLE row to the line the content now lives on"). Measured, it rewrites only the doc: before --apply after --apply docs/eval.md 7736 / 7765 / 7887 7745 / 7774 / 7896 <- correct CITATION_TABLE 7736 / 7765 / 7887 7736 / 7765 / 7887 <- untouched They agreed before and disagree after. Test count went from 3 failures to 4, because test_the_citation_table_co

📅 Opened Sep 13, 2026💬 3 comments
Quality: 75/100Contribute

no_human's review gate runs on machine-checkable signals: tests, held-out tests, the tamper guard, the repro gate, ruff lint evidence, wiring evidence (see docs/verification.md). One signal that every serious agent harness has converged on is missing from that list: type/compile diagnostics. This issue is the plan for adding it, written after a deep dive into what actually works, because "integrate LSP" hides two very different features with very different evidence behind them. Diagnostics as a verification signal is the proven half. SWE-agent's ablation credits its edit-loop linter (rejecting syntax-breaking edits) with roughly +3 points on a 12.47% baseline — the single most-cited agent-computer-interface feature of that paper (NeurIPS 2024). Aider lints after every LLM edit and feeds errors back for a fix round, by default. Claude Code's native LSP support (CLI v2.0.74) documents automatic post-edit diagnostics as the headline benefit — the model notices the error it just introd

📅 Opened Sep 6, 2026💬 3 comments
Quality: 50/100Contribute

The web board (web/src, React + node --test unit tests) works, but it was built by an engineer optimizing for correctness, and it shows. This is an umbrella invitation for UI/UX contributions — frontend and design skills welcome, no Python needed for most of it. Concrete rough edges observed on a fresh install (2026-09-01, v0.1.8 board against demo data — pick one, or report your own): First-run noise — READ THE CONSTRAINT FIRST (amended 2026-09-11). The ! badge on Settings is not noise and must not be hidden, dimmed or deferred while onboarding is incomplete: it is the only persistent signal that the install is not finished, and a board whose AI configuration is half-done is a board that cannot do its job. Proposals that suppress it will be declined, however elegant. What is open: the "Complete AI configuration" popup overlaps the sidebar stats card, and the header can say "nothing needs you" on a screen that plainly needs something. Fix the overlap, or the contradictory header

📅 Opened Sep 1, 2026💬 3 comments
Quality: 65/100Contribute

KI-1 in docs/KNOWN_ISSUES.md records a real product defect: with two tasks running concurrently against one Store (concurrency.enabled: true, max_workers > 1), an attempt could die on sqlite3.OperationalError: cannot commit transaction - SQL statements in progress with the traceback entirely in shipped code (Orchestrator._run_attempt → Store.update_attempt). The only test that drives that configuration, tests/test_scheduler.py::test_two_repos_run_concurrently_in_worktrees, measured a 3/8 serial failure rate on 2026-07-30 and is deselected in CI to keep the badge truthful. Since then, a mitigation landed: every Store write goes through the serialized_write lock in src/no_human/core/db.py. The failure rate has not been re-measured since. KI-1's own status line says: re-measure, then re-enable or close. The task — measurement first, code second (possibly no code at all): Re-measure: run uv run pytest -q tests/test_scheduler.py::test_two_repos_run_concurrently_in_worktrees serially,

📅 Opened Aug 31, 2026💬 0 comments
Quality: 75/100Contribute
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Contributor Compatibility & Review Speed Analysis for no-human-ai/no_human

When evaluating whether to contribute to no-human-ai/no_human, response velocity and maintainer engagement are crucial. GetMerged continuously tracks pull request trajectories, first-comment latency, and code review rounds to help developers avoid submitting pull requests to backlogged repositories.

Currently, maintainers of no-human-ai/no_human acknowledge new external contributions in approximately 1 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 2.8% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to no-human-ai/no_human

01

Is no-human-ai/no_human welcoming to first-time open-source contributors?

no-human-ai/no_human has a recorded first-timer success rate of 0.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.

02

How fast can I expect code review feedback on my pull request?

The initial maintainer response time averages ~1 hours. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 58.2 C-Rank™ score represent?

The C-Rank™ index scores repositories on a 0 to 100 scale using an objective formula: external PR merge rates, initial response speed, active maintainer count, and first-time contributor retention. A score of 58.2 places no-human-ai/no_human in the Welcoming tier.

04

What is the external contributor pull request merge rate for no-human-ai/no_human?

The external pull request merge rate is 2.8%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.

05

Are there beginner Good First Issues open in no-human-ai/no_human?

Yes, no-human-ai/no_human currently has 4 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for no-human-ai/no_human are automatically retrieved via the public GitHub API and recalculated daily. Insider pull requests submitted by repository owners or organization members are excluded from merge rate calculations to preserve objective external contributor statistics.