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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Computes and interprets sequencing read depth and coverage over a genome, windows, or target regions with mosdepth (windowed depth, cumulative distribution, --quantize callable BEDs), bedtools genomecov/coverage (bedGraph tracks, per-target stats), samtools depth/coverage (per-base depth, per-contig depth+breadth). Covers the breadth-vs-mean distinction, the cumulative-coverage curve, evenness (CV/Fano/fold-80/Gini), what each tool silently counts (duplicates, secondary/supplementary, MAPQ, read span vs fragment, mate-overlap), the samtools-depth 8000-cap version trap, and the bedtools coverage -a/-b orientation flip. Use when assessing sequencing adequacy, building coverage tracks, computing breadth at a depth threshold, defining callable regions, or QCing target-capture uniformity.

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Calculates statistical power for high-dimensional genomics experiments (bulk RNA-seq, scRNA-seq, ATAC-seq, ChIP-seq, methylation, proteomics) under negative-binomial count models using RNASeqPower, PROPER, and simulation via powsimR, distinguishing per-gene from marginal (transcriptome-wide) power, the role of mean expression and dispersion, and the sequencing-depth-versus-replicate tradeoff. Covers simulation as the honest default for overdispersed counts, FDR-aware average power versus single-test power, observed/post-hoc power as an anti-pattern, and the winner's-curse / Type-S / Type-M consequences of underpowering. Use when planning replicate number for a sequencing experiment, deciding whether to add depth or samples, choosing closed-form versus simulation power, estimating power from pilot dispersions, or justifying replication in a grant. For clinical-trial power see clinical-biostatistics/power-and-sample-size; for the inverse sample-size question see experimental-design/sample-size.

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Estimates the minimum biological replicates (or cells/events) for a target power at a target FDR in genomics experiments using ssizeRNA, PROPER, powsimR for scRNA-seq, and pilot-data dispersion estimation from DESeq2/edgeR. Covers the biological-versus-technical replication distinction (technical replicates do not add degrees of freedom for biological inference), replicate-number-versus-sequencing-depth budgeting, scRNA-seq sample-versus-cell allocation under a pseudobulk model, and the critique that "n=3" is a publication convention rather than a power calculation. Use when budgeting a sequencing experiment, writing the sample-size justification in a grant, estimating replicates from pilot data, allocating a fixed budget between samples and depth, or planning scRNA-seq cohort size. For clinical-trial sample size see clinical-biostatistics/power-and-sample-size; for the power-given-n direction see experimental-design/power-analysis.

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根据工厂设备、人员、材料和交期安排生产顺序，协调换产与工序负荷，分析瓶颈，并在设备故障或缺料时协助调整计划。

• 比较设备有限时的生产顺序
• 设备故障后重新安排计划
• 比较批量大小与换产成本

在React或Next.js中实现拖动、滑动手势、文字和SVG动画，涵盖多阶段动作、滚动与光标跟随及加载状态，并帮助按用途选择API。

• 实现拖动关闭面板和可排序列表
• 逐词显示文字并实现数字动画
• 绘制SVG线条和圆形进度

通过 Claude DevFleet 将开发工作分配给多个 Claude Code 智能体，在独立工作目录中并行执行，并管理任务依赖、进度以及变更、测试和错误报告。

• 将开发计划拆分为带依赖关系的任务
• 在独立工作目录中并行开发
• 检查多个开发任务的进度

用于AI音乐专辑制作，涵盖概念构思、歌词初稿、曲目排序和导出，适合独立专辑实验或品牌音乐企划；完整制作流程需另行安装上游资源包。

• 策划独立制作的专辑
• 围绕专辑主题起草歌词
• 安排专辑曲目顺序

Process paired-end 16S sequencing reads in QIIME 2 to produce ASVs, distinct sequence variants, and taxonomic assignments. Check input files, primer settings, and read retention while preserving artifacts that record the processing history.

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• Validate paired FASTQ files and sample metadata
• Remove primers and denoise reads with DADA2
• Assign taxonomy using a compatible classifier

Work with genomic regions in Polars tables to find overlaps, nearby regions, covered bases, and gaps. Read and write bioinformatics files, query data with SQL, and check coordinate conventions when joining datasets or migrating from bioframe.

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• Finding variants that overlap genomic regions
• Merging regions and measuring covered bases
• Summarizing sequencing read depth

Prepare bulk RNA-seq sequencing files or quantification outputs for gene-level comparisons. Check sample information and data quality, configure processing workflows, and assemble validated count tables and metadata for downstream differential expression analysis with PyDESeq2.

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• Preparing gene count tables from FASTQ files
• Configuring an nf-core/rnaseq workflow
• Combining Salmon, STAR, or featureCounts outputs

Plan a product or feature launch from preparation through launch day and 30 days of follow-up. Create channel plans, assigned tasks, checklists, and Product Hunt briefs, with a readiness score to identify preparation gaps.

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• Planning a product or major feature launch
• Sequencing a waitlist and early-access rollout
• Preparing a Product Hunt launch

Prepare employee communications for organizational changes, tool rollouts, and policy updates. Create announcements, audience-specific FAQs, manager talking points, and a communication calendar, while checking tone, channel choices, and sequencing against the change's impact.

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• Preparing an internal reorganization announcement
• Planning communications for a new tool rollout
• Writing audience-specific FAQs for policy changes

Plan customer retention and customer success staffing for a startup. Break down renewal and churn patterns, group customers by value and fit, and compare service models to guide resource allocation and hiring.

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• Reviewing retention metrics and reasons for churn
• Allocating support investment across customer tiers
• Estimating customer success staffing needs

Helps startup leaders make strategic data decisions: assess permission to train AI on customer data, choose data architecture, evaluate customer data as a business asset, and sequence hiring around the decisions the business needs to make.

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• Assessing customer data for AI training
• Choosing data architecture and what to build or buy
• Evaluating data assets before fundraising or M&A

Support startup AI strategy by comparing API use, fine-tuning—additional training of an existing model—and in-house development. Assess three-year costs, regulatory risk, self-hosting breakeven points, and hiring priorities to inform leadership decisions.

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• Compare API use, fine-tuning, and in-house development
• Assess regulatory risks for an AI use case
• Calculate when self-hosting becomes cost-effective

Rewrite a feature-based roadmap around the changes customers and the business should experience. The result pairs each initiative with an outcome statement, success metrics, sequencing notes, and context connecting the plan to company strategy.

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• Turning a feature list into measurable outcomes
• Connecting roadmap initiatives to company strategy
• Defining success metrics for planned work

Extract text from PDFs by checking each tool’s output and switching methods when needed. Guides agents from file reading to pdftotext and PyMuPDF, then checks readability, missing sections, and records extraction failures.

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• Extract text from local or downloaded PDFs
• Switch tools when file reading returns binary data
• Check extracted text for readability and missing sections

Split large feature requests or user stories, descriptions of what users need, into smaller deliverable pieces. Eight patterns guide the breakdown, with acceptance criteria and checks that each piece provides value and can be developed and tested independently.

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• Breaking down work that will not fit in one sprint
• Clarifying backlog items that are too large to estimate
• Separating stories with multiple acceptance scenarios

Turn business goals and customer problems into a product roadmap. Define initiatives, rank priorities, check dependencies and team capacity, and organize releases with clear reasons stakeholders can review and discuss.

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• Planning a quarterly or six-month product roadmap
• Prioritizing competing initiatives across teams
• Explaining roadmap choices to executives

Compare business growth options across existing and new products and markets. The skill connects each option to documented demand signals, rates its risk, and recommends an order of action with the assumption that could change it.

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• Compare growth options for next year's business plan
• Check the evidence behind a diversification proposal
• Sequence growth initiatives by risk and funding needs

Analyze marker-gene sequencing data to study microbial communities. Supports FASTQ quality checks and, with primers, metadata, and taxonomy resources, workflows that produce sequence variant tables, taxonomic assignments, diversity measurements, and plots.

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• Check amplicon FASTQ quality and analysis readiness
• Generate sequence variant and taxonomy tables
• Visualize microbial diversity and taxon composition

Plan or run ATAC-seq analysis to identify accessible DNA regions. Check sequencing quality, align reads, detect peaks, combine evidence across replicate samples, and prepare comparisons between conditions with quality summaries and visualization tracks.

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• Check the quality of ATAC-seq samples
• Detect accessible DNA regions from FASTQ or BAM files
• Build consensus peaks across replicate samples

Plan or run bulk RNA-seq processing from sequencing reads to count matrices, tables of expression counts across samples. Check sample metadata, reference consistency, read mapping, and quantification quality, and produce summaries for downstream analysis.

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• Generate count matrices from FASTQ files
• Check reference files and sample metadata
• Review mapping and quantification quality