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概要と使いどころ
Process-based discrete-event simulation framework in Python. Use this skill when building simulations of systems with processes, queues, resources, and time-based events such as manufacturing systems, service operations, network traffic, logistics, or any system where entities interact with shared resources over time.
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End-to-end Hi-C analysis workflow from FASTQ to compartments, TADs, and loops, with the decision of WHICH features the sequencing depth can support. Covers pairtools read-pair processing and library QC, cooler matrices, ICE balancing and distance-decay expected, A/B compartments, TAD boundaries, loop calling, and the routing of HiChIP/PLAC-seq/Capture Hi-C to protein-directed loop callers. Use when processing Hi-C data end to end, deciding a resolution for a given depth, or choosing between bulk-Hi-C and protein-directed loop calling.
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Machine learning in Python with scikit-learn. Use when working with supervised learning (classification, regression), unsupervised learning (clustering, dimensionality reduction), model evaluation, hyperparameter tuning, preprocessing, or building ML pipelines. Provides comprehensive reference documentation for algorithms, preprocessing techniques, pipelines, and best practices.
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Programmatic untargeted LC-MS feature extraction in R with the modern xcms 4.x MsExperiment/XcmsExperiment API, taking raw mzML to a feature table via CentWave peak detection, retention-time alignment, peak-density correspondence, gap-filling, CAMERA redundancy collapse, and built-in QC feature filtering. Use when converting centroided LC-MS runs into a features-by-samples matrix and deciding centWave/grouping/alignment parameters. For drift correction and QC/CV filtering execution see metabolomics/normalization-qc; for metabolite identification see metabolomics/metabolite-annotation; for the MS-DIAL GUI alternative with MS2Dec deconvolution see metabolomics/msdial-preprocessing; for downstream statistics see metabolomics/statistical-analysis.
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Assigns honest lipid annotation levels, designs class-based internal-standard quantification, and runs lipid-aware differential and enrichment analysis with lipidr, guarding against in-source-fragment phantoms, sn-position over-claims, and invalid cross-class quantification. Use when naming or canonicalizing lipid species (shorthand separators, Goslin), deciding shotgun vs RP vs HILIC LC-MS, picking internal standards (SPLASH/EquiSPLASH), interpreting MS-DIAL/LipidSearch output, or comparing lipid classes. For general feature detection see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for non-lipid annotation confidence see metabolomics/metabolite-annotation; for normalization/QC see metabolomics/normalization-qc; for multivariate stats see metabolomics/statistical-analysis.
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Turns untargeted LC-MS/MS features (m/z, RT, MS/MS) into confidence-stratified metabolite annotations using spectral-library matching (matchms), in-silico tools (SIRIUS/CSI:FingerID, MetFrag) and molecular networking, and assigns a defensible MSI/Schymanski confidence level to each. Use when naming detected features, scoring MS/MS against a reference library, running SIRIUS, or deciding what confidence level an evidence set actually supports. For upstream feature extraction see metabolomics/xcms-preprocessing and metabolomics/msdial-preprocessing; for downstream enrichment that must respect these levels see metabolomics/pathway-mapping; for lipid-specific structural annotation see metabolomics/lipidomics.
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。
Video and audio processing capabilities for Flutter applications
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Audit existing business processes to identify AI automation opportunities. TRIGGERS - Use when user wants to find AI opportunities, audit processes for automation, or assess AI readiness.
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Guide for adult content payment processor selection, merchant account application, compliance, webhook integration, and fallback strategies (CCBill, SegPay, AllSecure, Epoch, Telegram Stars, crypto)
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ZMK Firmware (オープンソースキーボードファームウェア) リファレンス。 Getting Started (hardware, user-setup, customization, zmk-cli)、 Features (bluetooth, split keyboards, encoders, displays, lighting, pointing, studio, low-power)、 Keymaps (keycodes, modifiers, combos, conditional layers)、 Behaviors (タップ・ホールド、レイヤー、マクロ、tap-dance, sticky-key)、 Input Processors、Hardware Integration、Troubleshooting (building, flashing, connection, hardware issues)。
NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Operator API, nsight-compute, compute-sanitizer。
Ergogen (エルゴノミック自作キーボード設計ツール) リファレンス。 YAML / JSON 設定で points, outlines, cases, pcbs を宣言し SVG / DXF / JSCAD / KiCad を出力。 Anchors, Zones, Adjustments, Preprocessing, Footprints, Traces。 キーレイアウト、ケース設計、PCB 生成。
Android アプリ開発 (Kotlin) の Media3 / CameraX リファレンス。androidx.media3, androidx.camera。 ExoPlayer, MediaSession, MediaSessionService, MediaLibraryService, DRM, Compose UI (PlayerSurface), ProcessCameraProvider, CameraSelector, ImageCapture, ImageAnalysis, VideoCapture, CameraInfo, CameraX Extensions, Camera2 Interop。
Claude Agent SDK (code.claude.com/docs/en/agent-sdk) の TypeScript / Python リファレンス。 query(), ClaudeAgentOptions / Options, custom tools (in-process MCP), createSdkMcpServer, hooks, permissions (canUseTool), subagents, sessions (resume / fork), streaming, structured outputs, slash commands, tool search, hosting / observability。
Work out the order and the loose ends before turning down an offer or withdrawing from a selection process: whether the offer being kept is actually settled in writing, whether each decline is mid-selection, post-offer, or after acceptance, who besides the employer has to be told when the application came through an agent, a school recommendation, or a referral, which reply deadlines have passed, and what documents, expenses, or borrowed items are still outstanding. Use when a user holds more than one offer and needs to decline the rest, wants to withdraw from a process they are no longer pursuing, or has been asked to turn down other employers as a condition; do not use to decide whether to decline, to rule on what declining after acceptance means legally, or to send any message.
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Load and preprocess imaging mass cytometry (IMC) and MIBI data from raw MCD/TXT through hot-pixel removal, spillover compensation, and variance-stabilizing transformation, covering readimc/steinbock ingestion, NNLS spillover compensation (CATALYST), IMC-Denoise, and the IMC arcsinh-cofactor question. Use when starting analysis from raw MCD files, building per-channel TIFF stacks, compensating channel spillover, choosing an arcsinh cofactor, or preparing single-cell intensities for phenotyping.
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Processes eDNA metabarcoding from raw paired-end reads to species tables, navigating ASV (DADA2, UNOISE3) vs OTU (swarm v2) decision (Callahan 2017 vs Schloss multi-copy-16S critique), marker/primer choice (Leray COI, MiFish 12S, 515F/806R 16S, ITS2) with primer-specific bias, OBITools3 v3 command-name break (obi stats plural; .tar.gz taxonomy), tag-jumping with dual-indexing (Schnell 2015; NovaSeq 10x MiSeq), decontam as screening-not-classifier (Davis 2018), read-counts-not-abundance critique (Lamb 2019), site-occupancy modeling (Ficetola 2015), Naive-Bayes calibration limits (Bokulich 2018), and eDNA decay (Strickler 2015). Use when going from raw eDNA FASTQ to species tables, picking marker + denoising pipeline, deciding whether read counts represent abundance, applying occupancy modeling, configuring OBITools3 v3, or interpreting decontam output. Not for clinical 16S microbiome (see microbiome/amplicon-processing).
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Align preprocessed CLIP-seq reads (eCLIP, iCLIP, iCLIP2, PAR-CLIP) to genome with STAR or bowtie2 using crosslink-preserving parameters, choosing between unique-mapper-only and multi-mapper-aware alignment for repeat-binding RBPs, deciding STAR vs HISAT2 memory trade-offs, and applying ENCODE-compatible filters. Use when turning preprocessed CLIP FASTQ into a deduplicated, MAPQ-filtered BAM ready for peak calling or crosslink-site detection.
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Preprocess CLIP-seq reads (eCLIP, iCLIP, iCLIP2, iCLIP3, irCLIP, PAR-CLIP, FLASH) with protocol-specific UMI extraction, adapter trimming, length filtering, and post-alignment PCR-duplicate collapse. Use when raw CLIP FASTQ must be turned into deduplicated, crosslink-preserving BAM input for peak calling; choosing between two-pass and single-pass adapter trimming; deciding minimum read length; or mapping UMI patterns to specific eCLIP/iCLIP/iCLIP2/iCLIP3 library preps.
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Ingest content into the vault as structured research notes. Accepts a URL, a local file path, `--from-brief [date]` (pick stories from an n8n Daily Intel news brief), or no arguments (batch-processes _Inbox/). Use when user shares a URL, says "clip this", "ingest", "ingest from the brief", or "process inbox".
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。
Process and extract information from PDF documents. Use this skill when the user asks to read, analyze, or extract data from PDF files.
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。
Administração de Ubuntu, Debian e AlmaLinux — systemd, cron, SSH, Fail2Ban, supervisor, disco, memória, rede e processos. Use quando o usuário disser "servidor", "linux", "systemd", "serviço não sobe", "disco cheio", "load alto", "SSH", "cron não roda", "processo travado".
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。
Process, parse, create, and manipulate Excel spreadsheets (.xlsx, .xls) using libraries like xlsx, exceljs, or SheetJS for data import/export and spreadsheet automation. Use when reading Excel files for data import, generating Excel reports, exporting data to spreadsheets, parsing bulk uploads, creating financial reports, manipulating workbooks and worksheets, or building data export features.
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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.
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。