Auditable bioinformatics workflow compiler: Catalog-bound planning, Workflow IR, and validated WDL 1.0.
C-Rank Grade: A (Welcoming) - 72/100
External PR Merge Rate: 100%
Response Time: <1h
First Timer Success: 100%
yuanzhw/AI-bioworkflow 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 yuanzhw/AI-bioworkflow respond to incoming external pull requests in approximately 0.1 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 72.2 places yuanzhw/AI-bioworkflow in the A tier.
The external contributor pull request merge rate for yuanzhw/AI-bioworkflow is 100.0%, based on public PR activity from non-core contributors.
yuanzhw/AI-bioworkflow currently has 2 active issue(s) tagged with beginner-friendly labels like "good first issue", "beginner", or "up-for-grabs".
Strict review standards in yuanzhw/AI-bioworkflow. Ensure PR scope matches issue requirements closely before requesting review.
Discussions in yuanzhw/AI-bioworkflow 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
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External Acceptance Rate
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First PR Conversion
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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 09:00 UTC (09:00 UTC in your timezone). Recommended submission window is Monday - Wednesday from 06:00 to 10:00 UTC (06:00 - 10:00 UTC local).
Maintainer Strategy: Maintainer Sweet Spot: PRs under 150 lines merge in ~4 hours (96% approval). Large PRs (>500 lines) take ~0 hours (45% approval). Keep your diff compact for rapid review.
Context The current Catalog has compile-ready bulk RNA-seq, reference-preparation, and ChIP-seq paths. A bounded single-cell recipe will extend family-level retrieval and compiler evaluation without claiming an end-to-end general scRNA-seq platform. Scope 10x HDF5 → QC → normalization → highly variable genes → PCA/neighbors → Leiden → UMAP → marker genes Acceptance criteria Add a scrnaseq_qc_clustering RecipeSpec for the scoped path. Add a complete scanpy_qc_clustering ToolSpec with explicit inputs, outputs, parameters, command, runtime, and execution-verification status. If a project-maintained wrapper is required, include its Dockerfile, packaged scripts, smoke_test.sh, and minimal execution evidence. Add a structured example plan and cover Catalog resolution, Workflow IR, deterministic WDL rendering, WOMtool 92 validation, and miniwdl compatibility. Convert the existing scRNA-seq negative retrieval query to supported and add bulk-versus-single-cell confusion cases wit
Context The public Catalog page currently renders a maintained static preview even though the API already exposes recipe and tool listing endpoints. Loading the page from those endpoints will make the trust boundary inspectable without duplicating Catalog definitions in the frontend. Acceptance criteria Replace the static recipe and tool arrays with data from /api/recipes and /api/tools. Present recipe inputs, steps and scatter structure, allowed tools, tool schemas, runtime references, and execution-verification status. Add clear loading, empty, and API-error states. Do not infer, repair, or silently replace Catalog fields in the Web layer. Add focused data-mapping tests and pass the existing Web lint, tests, and production build. Contributor note Keep the first pull request focused on read-only presentation. Catalog mutation and administration are outside this issue.
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When evaluating whether to contribute to yuanzhw/AI-bioworkflow, 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 yuanzhw/AI-bioworkflow acknowledge new external contributions in approximately <1 hour. Out of all submitted pull requests from non-core authors in the last 180-day window, 100.0% were successfully merged into the primary branch.
yuanzhw/AI-bioworkflow 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 ~<1 hour. 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 72.2 places yuanzhw/AI-bioworkflow in the Welcoming tier.
The external pull request merge rate is 100.0%. GetMerged isolates non-core community contributions so external developers get an accurate benchmark of PR acceptance probability.
Yes, yuanzhw/AI-bioworkflow 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 yuanzhw/AI-bioworkflow 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.