SFML is the first fully local AI solar forecast for Home Assistant, powered by a local Attention Transformer. No external AI — such as ChatGPT, Gemini, or Grok — required. Runs entirely on your device for complete privacy.
C-Rank Grade: D (Risky) - 8/100
External PR Merge Rate: -
Response Time: 57d
First Timer Success: -
Zara-Toorox/Solar-Forecast-ML 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.
Maintainers in Zara-Toorox/Solar-Forecast-ML respond to incoming external pull requests in approximately 1372.6 hours on average. Keeping PRs focused on single tasks and ensuring tests pass helps maintainers review faster.
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 8.0 places Zara-Toorox/Solar-Forecast-ML in the D tier.
The external contributor pull request merge rate for Zara-Toorox/Solar-Forecast-ML is 0.0%, based on public PR activity from non-core contributors.
Zara-Toorox/Solar-Forecast-ML does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Highly welcoming maintainers in Zara-Toorox/Solar-Forecast-ML. Prompt code reviews with positive guidance for new contributors.
Discussions in Zara-Toorox/Solar-Forecast-ML focus heavily on practical implementation feedback, code formatting standards, and issue reproduction details.
Unlinked PRs without issue context and changes that fail automated test suites face the highest review friction.
Average Response Latency
Tracks hours until a maintainer leaves a review, comment, or PR response.
External Acceptance Rate
Percentage of community pull requests successfully merged into main.
First PR Conversion
Rate at which developers submitting their first repository PR succeed.
Show contributors that your repository actively reviews and merges external pull requests.
[](https://getmerged.abhishekco.de/Zara-Toorox/Solar-Forecast-ML?utm_source=github&utm_medium=badge)
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
Calculating local time...
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Detected: UTC
Maintainers are most responsive around 14:00 UTC (14:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 14:00 to 18:00 UTC (14:00 - 18:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~892 hours (81% approval). Large PRs (>500 lines) take ~6589 hours (29% approval). Keep your diff compact for rapid review.
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No cached good first issues currently tracked for Zara-Toorox/Solar-Forecast-ML.
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When evaluating whether to contribute to Zara-Toorox/Solar-Forecast-ML, 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 Zara-Toorox/Solar-Forecast-ML acknowledge new external contributions in approximately 57 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.
Zara-Toorox/Solar-Forecast-ML 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.
The initial maintainer response time averages ~57 days+. Keeping PRs scoped to single concerns and ensuring CI checks succeed will optimize review turnaround.
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 8.0 places Zara-Toorox/Solar-Forecast-ML in the Risky tier.
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.
Zara-Toorox/Solar-Forecast-ML does not have open beginner labels indexed currently, but external PRs for bugs and documentation improvements are evaluated via normal issue triage.
All metrics displayed for Zara-Toorox/Solar-Forecast-ML 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.