TIGER-AI-Lab/ClawBench - Open Source PR Review Scorecard

Open-source benchmark for browser AI agents on daily tasks.

C-Rank Grade: A (Welcoming) - 60/100

External PR Merge Rate: 80%

Response Time: 11d

First Timer Success: 33%

Frequently Asked Questions

Is TIGER-AI-Lab/ClawBench welcoming to first-time open-source contributors?

TIGER-AI-Lab/ClawBench has a recorded first-timer success rate of 33.3%. 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 TIGER-AI-Lab/ClawBench respond to incoming external pull requests in approximately 254.5 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 59.7 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 59.7 places TIGER-AI-Lab/ClawBench in the A tier.

What is the external contributor pull request merge rate for TIGER-AI-Lab/ClawBench?

The external contributor pull request merge rate for TIGER-AI-Lab/ClawBench is 80.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in TIGER-AI-Lab/ClawBench?

TIGER-AI-Lab/ClawBench does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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TIGER-AI-Lab/ClawBench

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Open-source benchmark for browser AI agents on daily tasks.

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AI Maintainer Review Guidelines

Review Persona

Strict Architecture Gatekeeper

Warmth Score
7.6/10
Patience Score
8.0/10
Nitpick Rate
45%

Strict review standards in TIGER-AI-Lab/ClawBench. Ensure PR scope matches issue requirements closely before requesting review.

Top PR Submission Do's

  • •Ensure code complies with the project coding style
  • •Keep PRs scoped to a single concern
  • •Include context and link to the related issue

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit PRs without linking an issue
  • •Do not break existing tests without fixing them
  • •Do not mix unrelated refactors in a single PR
Response Velocity
10 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
80.0%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
33.3%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
7 core
Highly collaborative maintainer core
Diagnostic Health HUD
80.0%
Merge Gauge
33.3%
1st-Timer
Community Vibe42/100

Embed C-Rank Badge

Show contributors that your repository actively reviews and merges external pull requests.

GetMerged C-Rank badge for TIGER-AI-Lab/ClawBench
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/TIGER-AI-Lab/ClawBench)](https://getmerged.abhishekco.de/TIGER-AI-Lab/ClawBench?utm_source=github&utm_medium=badge)

Active Good First Issues (4)

View on GitHub

Problem The Harbor compatibility test is skipped when Harbor is not installed, and CI does not execute a real harbor run. The Harbor runtime Dockerfile is also outside the current harness image build scan. This leaves adapter, image, runtime readiness, verifier, and cleanup regressions undetected. Required scope Pin Harbor 0.22.x and Python 3.12 in a Linux Docker job. Convert one real V2 task and validate it with Harbor's loader. Run a single Harbor task smoke covering setup, runtime readiness, verifier, artifact collection, and cleanup. Include src/clawbench/runtime/harbor/Dockerfile in the image build matrix. Upload task/runtime/verifier logs on failure and record Harbor/schema/image versions. Acceptance criteria Adapter/runtime changes require the Harbor smoke job. The job exercises successful interception, no interception, judge-inconclusive, and cleanup failure paths. Harbor artifacts are available for debugging without exposing credentials. This is HAR-001 in docs/codebase-ev

📅 Opened Sep 10, 2026💬 0 comments
Quality: 90/100Contribute

Problem .env is tracked by Git and pyproject.toml force-includes it in wheel/sdist artifacts. The file contains a non-placeholder PurelyMail API key and must be treated as exposed. Required scope Revoke and rotate the current PurelyMail credential. Remove .env from Git history and package inclusion; add a safe .env.example. Load credentials only from CI/runtime secrets. Add secret scanning and a release check that wheel/sdist artifacts contain no credentials. Update Harbor/native documentation so credentials are injected, never packaged. Acceptance criteria git ls-files .env is empty. Built wheel and sdist contain no .env or credential values. CI fails on newly introduced secrets. Historical release artifacts are audited and the rotation is documented. This is tracked as SEC-001 in docs/codebase-evaluation-2026-09.md. @Perry2004 please review ownership and the rotation plan.

📅 Opened Sep 10, 2026💬 0 comments
Quality: 90/100Contribute

Problem Generated Harbor tasks use network_mode = "public" and run stock agents with shell access. The instruction says to use only the browser, but an agent can still call a site API with curl, Python, Node, or another direct client. That can invalidate benchmark results. Required scope Define the browser-only policy and an explicit egress allowlist for sites, model endpoints, judge, PurelyMail, and browser providers. Enforce the policy at the container/tool boundary, not only in instruction.md. Detect and preserve evidence of blocked direct requests or disallowed tools. Emit failure_category = "policy_violation" without conflating it with an agent mismatch. Acceptance criteria Direct API/HTTP submission attempts are blocked or recorded as policy violations. Legitimate CDP/browser flows continue to work. Harbor and native result summaries can separate policy violations from agent failures. This is HAR-002 in docs/codebase-evaluation-2026-09.md and is related to #331. @Perry2004 pl

📅 Opened Sep 10, 2026💬 0 comments
Quality: 90/100Contribute

Problem The runtime server writes intercepted request headers to requests.jsonl without a default redaction policy. Authorization, Cookie, Set-Cookie, CSRF tokens, and provider credentials can therefore leak into shareable benchmark artifacts. Required scope Add a default header allowlist or sensitive-header redaction for persisted requests. Redact token-like fields in headers and bounded request bodies where needed. Add an explicit retention/configuration contract and fixture tests. Keep enough metadata to explain that a value was redacted. Acceptance criteria Secrets in test fixtures never appear in requests.jsonl, summaries, or uploaded artifacts. Redaction behavior is deterministic and covered by tests. Harbor and native paths use the same artifact policy. This is SEC-002 in docs/codebase-evaluation-2026-09.md. @Perry2004 please review the privacy/retention policy.

📅 Opened Sep 10, 2026💬 0 comments
Quality: 90/100Contribute
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Contributor Compatibility & Review Speed Analysis for TIGER-AI-Lab/ClawBench

When evaluating whether to contribute to TIGER-AI-Lab/ClawBench, 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 TIGER-AI-Lab/ClawBench acknowledge new external contributions in approximately 10 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 80.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to TIGER-AI-Lab/ClawBench

01

Is TIGER-AI-Lab/ClawBench welcoming to first-time open-source contributors?

TIGER-AI-Lab/ClawBench has a recorded first-timer success rate of 33.3%. 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 ~10 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 59.7 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 59.7 places TIGER-AI-Lab/ClawBench in the Welcoming tier.

04

What is the external contributor pull request merge rate for TIGER-AI-Lab/ClawBench?

The external pull request merge rate is 80.0%. 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 TIGER-AI-Lab/ClawBench?

TIGER-AI-Lab/ClawBench does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.

GetMerged C-Rank™ Indexing Standard

All metrics displayed for TIGER-AI-Lab/ClawBench 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.