Learn AI agents from scratch.
C-Rank Grade: D (Risky) - 22/100
External PR Merge Rate: -
Response Time: 13d
First Timer Success: -
hardness1020/learn-agent-architecture 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 hardness1020/learn-agent-architecture respond to incoming external pull requests in approximately 306.7 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 22.3 places hardness1020/learn-agent-architecture in the D tier.
The external contributor pull request merge rate for hardness1020/learn-agent-architecture is 0.0%, based on public PR activity from non-core contributors.
hardness1020/learn-agent-architecture does not currently have active "good first issue" tags indexed, but accepts external contributions through standard GitHub issue tracking.
Collaborative maintainer environment in hardness1020/learn-agent-architecture. Reviews community pull requests with focus on project quality.
Discussions in hardness1020/learn-agent-architecture 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/hardness1020/learn-agent-architecture?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...
Calculating local time...
Detected: UTC
Maintainers are most responsive around 04:00 UTC (04:00 UTC in your timezone). Recommended submission window is Wednesday - Friday from 04:00 to 08:00 UTC (04:00 - 08:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~199 hours (81% approval). Large PRs (>500 lines) take ~1472 hours (29% approval). Keep your diff compact for rapid review.
No cached good first issues currently tracked for hardness1020/learn-agent-architecture.
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When evaluating whether to contribute to hardness1020/learn-agent-architecture, 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 hardness1020/learn-agent-architecture acknowledge new external contributions in approximately 12 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.
hardness1020/learn-agent-architecture 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 ~12 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 22.3 places hardness1020/learn-agent-architecture 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.
hardness1020/learn-agent-architecture 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 hardness1020/learn-agent-architecture 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.