「reproducible」の検索結果

Use when you need framework-agnostic Docker and container image guidance for Java projects - Dockerfile design, multi-stage Maven builds, jlink custom runtimes, micro runtime distributions such as Alpaquita, JVM container ergonomics, non-root execution, image metadata, .dockerignore, reproducible builds, vulnerability scanning, SBOM awareness, and production-safe container defaults. This should trigger for requests such as Review Java Dockerfile; Improve Docker image security; Add jlink runtime to a Java container; Add containerization to a Java project; Optimize Java container image size; Review Docker build reproducibility. Part of Plinth Toolkit

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Use when the user wants to prepare code for open-source release, create reproducible research artifacts, or structure a repository for publication. Triggers on phrases like "publish code", "open source release", "reproducibility", "research repository", "code release", or "prepare for publication".

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Acquire, clean, analyze, verify, visualize, and explain data for journalism. Use for reproducible data reporting, statistical analysis, maps, or public methodology.

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Python-based workflow manager for reproducible, scalable pipelines. Define rules with file-based dependencies; Snakemake resolves execution order and parallelism. Runs local, SLURM, LSF, AWS, GCP via profiles; per-rule conda/Singularity envs. For NGS pipelines, ML training, and multi-step file processing. Use Nextflow for Groovy dataflow or nf-core integration.

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MS spectral matching and metabolite ID with matchms. Import spectra (mzML, MGF, MSP, JSON), filter/normalize peaks, score similarity (cosine, modified cosine, fingerprint), build reproducible pipelines, identify unknowns vs spectral libraries. Use pyopenms for full LC-MS/MS proteomics.

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Use when planning how many patients or cases a study needs before data collection (power analysis, IRB justification). Walks a decision tree to the right test and returns reproducible R/Python code and IRB-ready justification text. Analyzing collected data is /analyze-stats.

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Use when data needs statistical analysis. Runs reproducible Python/R code for Table 1, diagnostic accuracy, agreement, regression, survival, propensity score, survey-weighted and repeated-measures models, with publication tables. Sample size is /calc-sample-size; pooling studies is /meta-analysis.

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Use ClawBio for local-first bioinformatics agent workflows: install and run its skill library, route genomic or omics inputs, create reproducible reports, connect optional MCP or Nextflow integrations, and author or validate skills.

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Use Cookiecutter Data Science to generate, configure, validate, and troubleshoot reproducible data-science project templates and generated project workflows.

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Show Honey's benchmark scoreboard — the committed quality and token results per task tier (code, user-facing, agent-to-agent) from bench/. Reports only the reproducible committed figures, never invents per-repo numbers. Use when asked how much Honey saves, how it compares to Caveman / Ponytail / no-skill baseline, or for the headline numbers.

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Use when the user wants to automate capture of marketing screenshots for a SwiftUI iOS app across multiple locales, devices, or appearances. Covers full-screen shots, isolated element renders (carousel cards, widgets), and reproducible output naming. Triggers on marketing screenshots, locale screenshots, widget renders, App Store assets, fastlane-alternative, simctl screenshots.

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Automatically generates executable tests that reproduce reported bugs from issue reports and code repositories. Use when users need to: (1) Create a test that reproduces a bug described in an issue report, (2) Generate failing tests from bug descriptions, stack traces, or error messages, (3) Validate bug reports by creating reproducible test cases, (4) Convert issue reports into executable regression tests. Takes a repository and issue report as input and produces test code that reliably triggers the reported bug.

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Best practices for designing reproducible experiments

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Automate ML workflows with Airflow, Kubeflow, MLflow. Use for reproducible pipelines, retraining schedules, MLOps, or encountering task failures, dependency errors, experiment tracking issues.

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Run a cumulative daily or retrospective sweep of research papers on a chosen topic, audit their claims, methods, integrity signals, and independent support, then identify overlooked but feasible project or business opportunities in a detailed source-grounded report. Use when asked to monitor papers every day, mine buried research, evaluate whether a paper is credible or reproducible, find unimplemented research ideas, or separate promising work from hype, weak evidence, and retracted or contradicted results.

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Design, implement, and validate reproducible machine-learning pipelines spanning data preparation, training, evaluation, registry, and deployment gates. Use when the user requests an ML pipeline, needs to turn model scripts into an orchestrated workflow, or provides pipeline components that must be connected safely.

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Design reproducible evaluations for AI agents with representative task sets, explicit rubrics, appropriate graders, baselines, regression gates, and failure analysis. Use when defining agent quality, comparing prompts or models, validating a release, measuring tool-use reliability, investigating regressions, or deciding whether an agent is ready for production.

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Populate databases with realistic, reproducible test data for development, testing, and staging environments. Use when the user requests database seeding or provides relevant inputs for this workflow.

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Weekly clinical-trial status diff — the 11-category change report over the stored weekly snapshots (spec 055 FR-B03). Surfaces exactly what changed on the trial registry for your coverage: New / Suspended / Withdrawn / Terminated / Recruiting / Recruitment-Complete / Completed / Ahead / Delayed / Upsized / Downsized, every row carrying previous and new values, with risk flags on Terminated and Downsized and re-timing flags on Ahead and Delayed. The stored-previous-value discipline makes each week's diff reproducible.

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Apply vaex in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses vaex or its strengths fit the task.

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Apply validate data in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses validate data or its strengths fit the task.

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Apply umap-learn in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses umap-learn or its strengths fit the task.

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Apply statistical power in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses statistical power or its strengths fit the task.

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Apply single-cell rna qc in reproducible local data workflows with version-aware APIs, explicit assumptions, and validation. Use when the user chooses single-cell rna qc or its strengths fit the task.

日本語の概要は準備中です。原文の説明を表示しています。