An advanced, set-and-forget automation tool for Microsoft Rewards. AutoRewarder performs Bing searches for PC and mobile point collection, collects Daily Sets, and uses mathematically driven, human-like input simulation (W3C Actions, Bezier curves, and smart scrolling). Packaged as an executable app for a seamless, plug-and-play experience
C-Rank Grade: B (Solid) - 64/100
External PR Merge Rate: 63%
Response Time: 1h
First Timer Success: 50%
safarsin/AutoRewarder has a recorded first-timer success rate of 50.0%. Repositories ranked B typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in safarsin/AutoRewarder respond to incoming external pull requests in approximately 1.1 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 64.3 places safarsin/AutoRewarder in the B tier.
The external contributor pull request merge rate for safarsin/AutoRewarder is 62.5%, based on public PR activity from non-core contributors.
safarsin/AutoRewarder currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
An advanced, set-and-forget automation tool for Microsoft Rewards. AutoRewarder performs Bing searches for PC and mobile point collection, collects Daily Sets, and uses mathematically driven, human-like input simulation (W3C Actions, Bezier curves, and smart scrolling). Packaged as an executable app for a seamless, plug-and-play experience
Active maintainer team in safarsin/AutoRewarder welcoming community pull requests and bug fixes.
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/safarsin/AutoRewarder?utm_source=github&utm_medium=badge)
Historical health & responsiveness evolution
External PR acceptance
First PR success rate
50% receive response in
90% receive response in
Maintainer review activity heatmap & prime window prediction
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Detected: UTC
Maintainers are most responsive around 18:00 UTC (18:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 16:00 to 20:00 UTC (16:00 - 20:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (78% approval). Large PRs (>500 lines) take ~5 hours (28% approval). Keep your diff compact for rapid review.
Clean up the main dataset by removing obsolete queries and expanding the pool with new search terms. Remove old or broken queries Add new queries Guidelines for Contributors If you want to pick up this issue, please follow these rules to ensure the dataset remains safe and effective: 1. What to remove: Outdated, seasonal or temporary queries (e.g., removing "winter storm warning" as it's not evergreen). Duplicates or similar variations (e.g., keeping "chatgpt" but removing "chat gpt"). Overly short or gibberish terms. 2. What to add: Natural, human-like searches: Prefer 2-4 word phrases over single words (e.g., "how to bake dark bread" instead of just "bread"). Evergreen topics: Add queries related to history, science, geography, cooking, or general knowledge. Formatting: Ensure all new entries are properly formatted as lowercase strings, without special symbols. 3. How to submit: Open a Pull Request and mention Closes #140 in the PR description. Target file: queries.json No
hey there, every time I try starting it or even booting the app, I always get the same error message(s), I was only able to complete first setup and after that, it never worked, could you help me out there? thanks "[ERROR] Error loading WebDriver: Message: session not created from unknown error: cannot parse internal JSON template: EOF while parsing a value at line 1 column 0; For documentation on this error, please visit: https://www.selenium.dev/documentation/webdriver/troubleshooting/errors#sessionnotcreatedexception Stacktrace: msedgedriver!GetHandleVerifier [0x7ff62e901e55+4ca5] msedgedriver!(No symbol) [0x7ff62e8529d4] msedgedriver!GetHandleVerifier [0x7ff62ec813af+3841ff] msedgedriver!(No symbol) [0x7ff62e6206ad] msedgedriver!(No symbol) [0x7ff62e620b9b] msedgedriver!(No symbol) [0x7ff62e61bb17] msedgedriver!(No symbol) [0x7ff62e619923] msedgedriver!(No symbol) [0x7ff62e65be7b] msedgedriver!(No symbol) [0x7ff62e65b806] msedgedriver!(No symbol) [0x7ff62e62599c] msedgedriver!(No s
In the latest version of the program (4.1), you added a feature to generate search terms with an LLM, but it's limited to a few providers. Would it be possible to add a custom provider using an OpenAI-compatible API? The program would have to ask for the endpoint, and other than that, the request would be similar to the OpenAI one. In fact, i believe that changing the selector to say it's an OpenAI compatible provider, and if the endpoint field is empty to default the endpoint to the official OpenAI one would work well enough for this. This would open up the possibility of using local models or plenty of other providers.
I have been using the schedule search and it seems if we see some kind of completion for each on of them,
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When evaluating whether to contribute to safarsin/AutoRewarder, 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 safarsin/AutoRewarder acknowledge new external contributions in approximately 1 hours. Out of all submitted pull requests from non-core authors in the last 180-day window, 62.5% were successfully merged into the primary branch.
safarsin/AutoRewarder has a recorded first-timer success rate of 50.0%. Repositories ranked Solid typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~1 hours. 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 64.3 places safarsin/AutoRewarder in the Solid tier.
The external pull request merge rate is 62.5%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, safarsin/AutoRewarder currently has 2 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for safarsin/AutoRewarder 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.