deepmodeling/deepmd-kit - Open Source PR Review Scorecard

A deep learning package for many-body potential energy representation and molecular dynamics

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

External PR Merge Rate: 80%

Response Time: 4d

First Timer Success: 36%

Frequently Asked Questions

Is deepmodeling/deepmd-kit welcoming to first-time open-source contributors?

deepmodeling/deepmd-kit has a recorded first-timer success rate of 36.4%. 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 deepmodeling/deepmd-kit respond to incoming external pull requests in approximately 86.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 59.5 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 59.5 places deepmodeling/deepmd-kit in the A tier.

What is the external contributor pull request merge rate for deepmodeling/deepmd-kit?

The external contributor pull request merge rate for deepmodeling/deepmd-kit is 80.1%, based on public PR activity from non-core contributors.

Are there Good First Issues available in deepmodeling/deepmd-kit?

deepmodeling/deepmd-kit does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

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deepmodeling/deepmd-kit

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Active (30d)A•Welcoming(60/100)Python

A deep learning package for many-body potential energy representation and molecular dynamics

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AI Maintainer Review Guidelines

Review Persona

High-Friction Gatekeeper

Warmth Score
5.0/10
Patience Score
6.0/10
Nitpick Rate
65%

Rigorous architecture standards (100% critical review signals). Ensure PRs strictly follow guidelines before requesting review in deepmodeling/deepmd-kit.

Top PR Submission Do's

  • •Add unit tests with >80% coverage for modified code paths
  • •Use conventional commit messages and clean branch names
  • •Link relevant GitHub issue ID in PR description pre-flight checklist

Top PR Friction Pitfalls (Don'ts)

  • •Do not submit unlinked PRs without referencing an existing issue
  • •Do not mix refactoring and feature logic into a single commit
  • •Do not ping maintainers repeatedly within 24 hours of opening
Response Velocity
3 days+
Standard maintainer review cycle

Average Response Latency

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

Merge Efficiency
80.1%
High acceptance rate for external PRs

External Acceptance Rate

Percentage of community pull requests successfully merged into main.

First-Timer Success
36.4%
Accepts new contributor PRs

First PR Conversion

Rate at which developers submitting their first repository PR succeed.

Active Maintainers
13 core
Highly collaborative maintainer core
Diagnostic Health HUD
80.1%
Merge Gauge
36.4%
1st-Timer
Community Vibe43/100

Embed C-Rank Badge

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

GetMerged C-Rank badge for deepmodeling/deepmd-kit
[![GetMerged C-Rank](https://getmerged.abhishekco.de/api/badge/deepmodeling/deepmd-kit)](https://getmerged.abhishekco.de/deepmodeling/deepmd-kit?utm_source=github&utm_medium=badge)

Active Good First Issues (0)

View on GitHub

No cached good first issues currently tracked for deepmodeling/deepmd-kit.

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Contributor Community Vibe Feedback

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Rate your first-hand PR experience (review speed, maintainer responsiveness, and onboarding ease) to help other contributors.

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Maintainer helpfulness
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Contributor Compatibility & Review Speed Analysis for deepmodeling/deepmd-kit

When evaluating whether to contribute to deepmodeling/deepmd-kit, 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 deepmodeling/deepmd-kit acknowledge new external contributions in approximately 3 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 80.1% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to deepmodeling/deepmd-kit

01

Is deepmodeling/deepmd-kit welcoming to first-time open-source contributors?

deepmodeling/deepmd-kit has a recorded first-timer success rate of 36.4%. 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 ~3 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 59.5 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 59.5 places deepmodeling/deepmd-kit in the Welcoming tier.

04

What is the external contributor pull request merge rate for deepmodeling/deepmd-kit?

The external pull request merge rate is 80.1%. 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 deepmodeling/deepmd-kit?

deepmodeling/deepmd-kit 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 deepmodeling/deepmd-kit 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.