Nixtla/mlforecast - Open Source PR Review Scorecard

Scalable machine 🤖 learning for time series forecasting.

C-Rank Grade: D (Risky) - 12/100

External PR Merge Rate: -

Response Time: 7d

First Timer Success: -

Frequently Asked Questions

Is Nixtla/mlforecast welcoming to first-time open-source contributors?

Nixtla/mlforecast has a recorded first-timer success rate of 0.0%. Repositories ranked D 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 Nixtla/mlforecast respond to incoming external pull requests in approximately 170.7 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.

What does the 12.1 C-Rank™ score (D 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 12.1 places Nixtla/mlforecast in the D tier.

What is the external contributor pull request merge rate for Nixtla/mlforecast?

The external contributor pull request merge rate for Nixtla/mlforecast is 0.0%, based on public PR activity from non-core contributors.

Are there Good First Issues available in Nixtla/mlforecast?

Nixtla/mlforecast does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.

Nixtla
Nixtla/mlforecastDRisky1.3k
GitHub
Back to Explorer
Nixtla

Nixtla/mlforecast

1,271
DRisky(12/100)Python

Scalable machine 🤖 learning for time series forecasting.

Compare
Jump to:

AI Maintainer Review Guidelines

Review Persona

Empathetic Technical Mentor

Warmth Score
7.6/10
Patience Score
8.2/10
Nitpick Rate
25%

Collaborative maintainer environment in Nixtla/mlforecast. Reviews community pull requests with focus on project quality.

What Contributors Actually Say

Discussions in Nixtla/mlforecast 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
  • Link the issue and include reproduction context in the PR
  • Update docs or README when behavior changes

Top PR Friction Pitfalls (Don'ts)

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

Average Response Latency

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

Merge Efficiency
0.0%
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
1 core
Single maintainer review bottleneck
Diagnostic Health HUD
0.0%
Merge Gauge
0.0%
1st-Timer
Community Vibe0/100

Embed C-Rank Badge

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

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

Active Good First Issues (0)

View on GitHub

No cached good first issues currently tracked for Nixtla/mlforecast.

View all good first issues directly on GitHub

Looking for more Python beginner tasks?Explore Python GFI

Contributor Community Vibe Feedback

Rate what actually matters after opening a pull request here.

Have you contributed to this repo?

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

3 ratings required
Maintainer helpfulness
Review speed
Beginner friendliness

Contributor Compatibility & Review Speed Analysis for Nixtla/mlforecast

When evaluating whether to contribute to Nixtla/mlforecast, 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 Nixtla/mlforecast acknowledge new external contributions in approximately 7 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 0.0% were successfully merged into the primary branch.

Frequently Asked Questions - Contributing to Nixtla/mlforecast

01

Is Nixtla/mlforecast welcoming to first-time open-source contributors?

Nixtla/mlforecast has a recorded first-timer success rate of 0.0%. Repositories ranked Risky 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 ~7 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.

03

What does the 12.1 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 12.1 places Nixtla/mlforecast in the Risky tier.

04

What is the external contributor pull request merge rate for Nixtla/mlforecast?

The external pull request merge rate is 0.0%. 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 Nixtla/mlforecast?

Nixtla/mlforecast 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 Nixtla/mlforecast 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.