Results for “data flow”

Use this skill when building, debugging, or optimizing CRM Analytics dataflows — defining node types (sfdcDigest, Append, Augment, computeExpression, computeRelative, Flatten, dim2mea, sfdcRegister), scheduling runs, handling run failures, and tuning performance. NOT for doing the same transformation in a Data Prep recipe — use admin/analytics-recipe-design. NOT for dataset field types, refresh schedules, or row limits — use admin/analytics-dataset-management.

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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Check task processing statistics to see how much data and how many docs are moving. Use when checking throughput, diagnosing a stalled task, verifying data movement, or reporting on task activity. Use when user says "is data flowing", "how much data", "check throughput", "how many documents", "task not processing", "data not arriving", "stats", "bytes processed", "stalled capture", "no data in destination", "data volume", "task report", "usage report", or "how much data did we process".

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Expert en data engineering (ETL, Airflow, dbt, data lakes, streaming pipelines, data quality)

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Authors reproducible Nextflow DSL2 pipelines built on reactive dataflow, where processes communicate only through channels and execution order is not guaranteed. Use when deciding channel/dataflow (Nextflow) vs rule-based (Snakemake) authoring; wiring queue vs value channels and fixing shared-reference exhaustion with .first(); composing DSL2 modules and subworkflows with take/main/emit; selecting container/conda profiles and pinning images by digest for portability across local/SLURM/LSF/AWS Batch/Google Batch/Kubernetes executors; diagnosing why -resume misses the cache (nondeterministic input order, mtime on network filesystems, mutable :latest tags) with cache 'lenient' and -dump-hashes; managing work/ vs publishDir and dynamic retry escalation; and choosing whether to adopt an nf-core community pipeline or author from scratch.

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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

The English summary is being prepared. Showing the original description.

Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

The English summary is being prepared. Showing the original description.

Download genome assemblies, gene records, and ortholog data from NCBI using the modern Datasets v2 CLI (replaces assembly_summary.txt scraping and many EFetch workflows). Use when bulk-pulling genome assemblies, gene metadata across species, ortholog sets, or BLAST databases; when E-utilities are too slow for genome-scale work; or when automatic checksum verification, parallel download, and clean accession-driven retrieval are required. Encodes the JSON-lines output format, dataformat conversion, --dehydrated for cloud workflows, and when Datasets is/isn't the right tool.

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Write, review, and refactor Kotlin code using established conventions. Covers safe handling of missing values, immutable data, coroutines for asynchronous work, and type-safe configuration builders, helping keep application code readable and maintainable.

• Reviewing Kotlin code for unsafe null handling
• Refactoring mutable data and deeply nested code
• Designing asynchronous work with coroutines and Flow

Find bottlenecks in large data transfers, imports, transformations, and historical data catch-up. Benchmark alternative processing methods, turn the fastest correct approach into a repeatable workflow, and verify file counts, row counts, and timestamps after execution.

• Speeding up large data imports and exports
• Reducing the time needed to load historical data
• Comparing batch sizes and worker counts

Build web search, page content retrieval, structured data extraction, and browser interactions into applications with Firecrawl. The skill guides endpoint selection, credentials, SDK or REST integration, and verification with a real request.

• Adding web search to an application
• Extracting structured data from known URLs
• Building website clicks and form filling into a workflow

Analyze Excel and CSV files to summarize statistics, filter records, compare groups, and join data across sheets or files. Export results as CSV, JSON, or Markdown tables for further work or sharing.

• Compare monthly sales and top-selling products
• Combine order records with customer data
• Check missing values and statistical summaries

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Builds a 30/60/90-day cash flow forecast from accounting records, payment services, or CSV data. Uses historical payment timing to estimate uncertainty, flags specific cash risks, and delivers a chat summary, forecast page, and downloadable Excel workbook.

• Check whether cash will cover the next payroll
• Review cash flow across three 30-day windows
• Identify invoices whose late payment could cause a shortfall

Build Python applications that collect live cryptocurrency prices, trades, and order books with Cryptofeed. Covers consistent data formats across exchanges, price comparisons, and writing incoming data to storage systems for monitoring and analysis.

• Monitoring prices across cryptocurrency exchanges
• Analyzing order book depth
• Analyzing executed trade flows

Audit one datafusion-spark function against Apache Spark 4.2.0. Compare source code, accepted types, and edge cases, verify expected results with PySpark, then fix differences and record remaining gaps as issues and disabled tests.

• Auditing a function for Spark compatibility
• Checking NULL, overflow, and ANSI mode behavior
• Finding missing SQL test coverage

Scans code for security vulnerabilities with CodeQL by tracing data across functions. Builds and checks analysis databases, models project-specific APIs, and saves findings in SARIF, a structured results format, with broad or focused scan options.

• Scanning a codebase for security vulnerabilities
• Tracing untrusted input through custom APIs
• Choosing broad or high-precision security scans

Build or review quality control workflows for single-cell and single-nucleus RNA sequencing data. Select filters from observed data, detect doublets, assign cell types, and create UMAP plots that summarize cell relationships, while recording analysis decisions and outputs.

• Choosing quality filters from dataset distributions
• Detecting doublets and checking ambient RNA
• Assigning cell types using references or marker genes

Turn descriptions or code analysis into Mermaid diagrams for workflows, system architecture, and database relationships. Check diagram syntax and export SVG, PNG, or text-based ASCII output, with themes and batch rendering options.

• Visualizing system architecture
• Documenting API request sequences
• Drawing database relationships

Generate ASP.NET Core pages, components, or API endpoints for creating, reading, updating, and deleting database records. Follow the project's existing styling and code conventions, with database migrations and verification of the generated functionality.

• Add database-backed record management pages
• Generate Minimal API endpoints for a model
• Create Blazor forms that match existing styling

Build research workflows that regenerate results from raw data with a single command. Organize project files, configure workflow tools, manage software environments and data versions, and prepare replication packages for journal submission.

• Setting up folders for a new research project
• Automating analysis and figure generation
• Debugging results that differ across computers

Explore scientific data files to understand their structure, statistical patterns, and quality. References cover over 200 formats, with workflows for producing Markdown reports that summarize findings and recommend preprocessing, visualizations, and further analysis.

• Checking missing values and outliers in experimental tables
• Examining sequence lengths and quality scores in FASTQ files
• Inspecting microscopy image dimensions and metadata

Turn research about an existing app into a technical plan for a new implementation. Choose technologies, define database tables and access rules, map API routes, and order development around a small, complete core workflow.

• Choose technologies for an alternative app
• Design database tables, indexes, and access rules
• Map API routes and scheduled background jobs