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Complete mass spectrometry analysis platform. Use for proteomics workflows feature detection, peptide identification, protein quantification, and complex LC-MS/MS pipelines. Supports extensive file formats and algorithms. Best for proteomics, comprehensive MS data processing. For simple spectral comparison and metabolite ID use matchms.
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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
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Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
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Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
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Download Copernicus CDS ERA5 single-level point time-series climate forcing for non-US or non-NLDAS EcoSIM sites from longitude/latitude coordinates. Use when a paper-derived site is outside the NLDAS domain, lacks AmeriFlux/FLUXNET curated forcing, or needs global ERA5 variables such as 2m temperature, dewpoint temperature, surface pressure, solar radiation, precipitation, and 10m wind components for EcoSIM preprocessing.
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Extract typical annual GPP, annual evapotranspiration, mid-season LAI, mid-season root biomass, mid-season shoot biomass, median canopy height, maximum primary-root depth, Vcmax at 25oC, and Jmax at 25oC from EcoSIM h0 NetCDF output files. Use when asked to derive these EcoSIM output targets from a selected .ecosim.h0.*.nc file for model-output processing, trait checks, or benchmark summaries.
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Process and extract information from PDF documents. Use this skill when the user asks to read, analyze, or extract data from PDF files.
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Process, parse, create, and manipulate Microsoft Word (.docx) documents programmatically using libraries like docx or mammoth.js for document generation and data extraction. Use when generating Word documents from templates, extracting text and formatting from .docx files, creating reports and invoices, parsing resumes and forms, converting Word to HTML, creating mail merge documents, or automating document workflows.
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Organize, categorize, track, and manage invoices systematically with automated extraction of invoice data, payment tracking, and financial organization. Use when processing invoice uploads, extracting invoice details (date, amount, vendor), categorizing expenses, tracking payment status, organizing receipts, generating financial reports, or building accounting and bookkeeping systems.
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Process, parse, create, and manipulate PDF documents using libraries like pdf-lib, PDFKit, or pdf-parse for document generation, data extraction, and PDF manipulation. Use when generating PDFs from HTML, extracting text from PDFs, merging or splitting PDFs, adding watermarks, filling PDF forms, creating invoices and reports, parsing legal documents, or building document management systems.
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Designs genomics experiments so technical nuisance variation (batch, lane, plate, flow cell, operator, reagent lot, processing day) is balanced against the biological variable of interest and therefore estimable rather than confounded, using constrained sample-to-batch assignment (designit, OSAT), the confounder/mediator/collider distinction, and the principle that no post-hoc correction recovers a fully confounded design. Covers detecting hidden batches with surrogate variable analysis, a decision table for downstream correction (ComBat-seq, RUVSeq, SVA) whose execution is deferred to differential-expression/batch-correction, and reproducibility metadata. Use when assigning samples to sequencing batches/lanes/plates, avoiding batch-condition confounding, deciding whether a design is salvageable by correction, choosing a correction method, or estimating the number of hidden batches. For the experimental unit, randomization, and blocking concepts see experimental-design/randomization-blocking.
中文简介正在准备中。当前显示原文说明。
Stores and operates on sparse expression matrices for single-cell and large bulk RNA-seq, covering dgCMatrix/dgRMatrix/dgTMatrix when-each-is-fast, the dgCMatrix (CSC, R) <-> CSR (Python) implicit transpose, AnnData (cells-rows) <-> SingleCellExperiment (cells-cols) orientation flip, HDF5/h5ad vs Zarr cloud-native shift, HDF5SummarizedExperiment + DelayedArray for out-of-memory bulk, scanpy backed mode for large h5ad, the ~10-15% density crossover where dense beats sparse, 10X format proliferation (MTX vs CellRanger H5 vs h5ad), the dense-conversion memory blow-up, and Dask + Zarr for consortium-scale matrices. Use when choosing sparse format, working with single-cell-sized matrices, importing/exporting 10X, debugging R/Python interop transposes, processing matrices too large for RAM, or building cloud-native pipelines.
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Spring Boot architecture patterns, REST API design, layered services, data access, caching, async processing, and logging. Use for Java Spring Boot backend work.
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Intelligent pattern selection for Fabric CLI. Automatically selects the right pattern from 242+ specialized prompts based on your intent - threat modeling, analysis, summarization, content creation, extraction, and more. USE WHEN processing content, analyzing data, creating summaries, threat modeling, or transforming text.
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Process and manipulate images using ImageMagick. Supports resizing, format conversion, batch processing, and retrieving image metadata. Use when working with images, creating thumbnails, resizing wallpapers, or performing batch image operations.
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Expert GNU awk (gawk) one-liner generation and transformation. Use this skill whenever the user asks to process text fields, extract columns, compute sums or aggregates, filter structured output, reformat CSV/TSV, parse logs, generate reports, or do any record/field processing task. Trigger on keywords like "awk", "extract column", "sum a field", "count occurrences", "print field N", "filter by column", "reformat output", "aggregate log data", "process CSV", "TSV", or any request that maps to a field-oriented text transformation. When in doubt, use this skill — awk is often the right tool when sed isn't enough.
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Master enterprise-grade Scala development with functional programming, distributed systems, and big data processing. Expert in Apache Pekko, Akka, Spark, ZIO/Cats Effect, and reactive architectures. Use PROACTIVELY for Scala system design, performance optimization, or enterprise integration.
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Draft privacy policies, terms of service, disclaimers, and legal notices. Creates GDPR-compliant texts, cookie policies, and data processing agreements. Use PROACTIVELY for legal documentation, compliance texts, or regulatory requirements.
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Leverages Splunk Enterprise Security and SPL (Search Processing Language) to investigate security incidents through log correlation, timeline reconstruction, and anomaly detection. Covers Windows event logs, firewall logs, proxy logs, and authentication data analysis. Activates for requests involving Splunk investigation, SPL queries, SIEM log analysis, security event correlation, or log-based incident investigation.
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