Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation
C-Rank Grade: A (Welcoming) - 64/100
External PR Merge Rate: 93%
Response Time: 6d
First Timer Success: 100%
retentioneering/retentioneering-tools has a recorded first-timer success rate of 100.0%. Repositories ranked A typically provide actionable feedback during code reviews and actively nurture new community contributors.
Maintainers in retentioneering/retentioneering-tools respond to incoming external pull requests in approximately 134.8 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 64.1 places retentioneering/retentioneering-tools in the A tier.
The external contributor pull request merge rate for retentioneering/retentioneering-tools is 93.0%, based on public PR activity from non-core contributors.
retentioneering/retentioneering-tools currently has 1 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Python toolkit, MCP server, and agent skills for reproducible, auditable clickstream and event log analytics. Helps AI agents, data scientists and analysts build, validate, and cross-check product analytics, quantitative UX, customer journeys, graph-based user flows, behavioral segmentation, A/B tests, process mining models, Markov chain simulation
Highly welcoming maintainers in retentioneering/retentioneering-tools. Prompt code reviews with positive guidance for new contributors.
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/retentioneering/retentioneering-tools?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 21:00 UTC (21:00 UTC in your timezone). Recommended submission window is Tuesday - Thursday from 19:00 to 23:00 UTC (19:00 - 23:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~88 hours (96% approval). Large PRs (>500 lines) take ~647 hours (42% approval). Keep your diff compact for rapid review.
Hi! I found what looks like a timestamp-ordering bug in the synthetic e-commerce dataset shipped with Retentioneering 5.0.1. The session state machine can emit purchase only from payment_details, and session generation stops after reaching purchase. Therefore, within a generated session_id, payment_details should always precede purchase. However, in the bundled dataset, 205 out of 470 purchase sessions have no payment_details before their first purchase: import retentioneering as rete df = ( rete.datasets.load_ecom(as_dataframe=True) .sort_values(["session_id", "timestamp"]) ) purchase_sessions = 0 missing_payment_before_purchase = 0 for _, session in df.groupby("session_id"): events = session["event"].astype(str).tolist() if "purchase" not in events: continue purchase_sessions += 1 first_purchase = events.index("purchase") if "payment_details" not in events[:first_purchase]: missing_payment_before_purchase += 1 print(purchase_sessions,
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When evaluating whether to contribute to retentioneering/retentioneering-tools, 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 retentioneering/retentioneering-tools acknowledge new external contributions in approximately 5 days+. Out of all submitted pull requests from non-core authors in the last 180-day window, 93.0% were successfully merged into the primary branch.
retentioneering/retentioneering-tools has a recorded first-timer success rate of 100.0%. Repositories ranked Welcoming typically provide actionable feedback during code reviews and actively nurture new community contributors.
The initial maintainer response time averages ~5 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 64.1 places retentioneering/retentioneering-tools in the Welcoming tier.
The external pull request merge rate is 93.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, retentioneering/retentioneering-tools currently has 1 active issue(s) tagged with beginner-friendly labels. You can inspect these directly from the repository issues tab.
All metrics displayed for retentioneering/retentioneering-tools 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.