「data processing」の検索結果

Multi-agent research orchestration: split a research goal into parallel sub-goals, run each via headless `claude -p` subprocesses, aggregate results into a polished report file. Use for systematic web/document research, competitive or industry analysis, batch link/dataset processing, and long-form evidence synthesis. Triggers: "深度调研", "deep research", "wide research", "多 Agent 调研", "系统调研".

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Detect buying signals across TAM companies and watchlist personas. Three-phase architecture: (1) free diff-based signals from existing data (headcount growth, tech stack changes, funding rounds), (2) Apify-powered signals (job postings, LinkedIn content analysis, profile changes), and (3) post-processing with dedup, scoring, and lead status updates. Writes signals to Supabase signals table for downstream activation.

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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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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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Display 3-D image volumes, medical image volumes, surface meshes, and annotations for 3-D image processing. Use when displaying 3-D images or isosurfaces with volshow, creating volume viewers with viewer3d, adding Regions of Interest (ROI) or annotations, overlaying masks or segmentations, streaming volumetric data, or building apps with volume display.

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Patterns for using blockedImage to process large images, harness parallel compute for image processing, and write custom adapters. Use when writing code that creates, processes, or visualizes blockedImage objects, when implementing images.blocked.Adapter subclasses, or when a user needs help with large image data. Always use this skill when working with TIFF,GeoTIFF, .svs, .ndpi, .czi or other WSI, satellite imagery or microscopy volume image formats.

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Collect, download, and verify App Store Connect Analytics reports with asc. Use when users need to discover analytics report requests, select report instances by processing date or granularity, download every segment, or verify downloaded files against Apple's size and MD5 metadata before analysis.

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Expert ISO 27701 Privacy Information Management System (PIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 27701:2025, ISO/IEC 27701:2019, privacy information management, PIMS certification, PII controller or processor obligations, privacy risk assessment, Statement of Applicability for privacy, privacy by design, data subject rights, DPIA, records of processing activities, transitioning from ISO 27701:2019, GDPR alignment with ISO 27701, or any privacy management system topic. Also trigger for questions about Annex A.1 (controller controls), A.2 (processor controls), A.3 (shared security controls), or implementing a standalone PIMS without ISO 27001. When in doubt, use this skill — it covers the full ISO 27701 lifecycle from gap assessment through certification.

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Neuropixels probe electrophysiology data processing and analysis

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Parallel and distributed computing for large-scale data processing

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Geospatial data processing, coordinate transforms, and mapping

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。

Expert ISO 27701 Privacy Information Management System (PIMS) compliance advisor. Use this skill whenever a user asks about ISO/IEC 27701:2025, ISO/IEC 27701:2019, privacy information management, PIMS certification, PII controller or processor obligations, privacy risk assessment, Statement of Applicability for privacy, privacy by design, data subject rights, DPIA, records of processing activities, transitioning from ISO 27701:2019, GDPR alignment with ISO 27701, or any privacy management system topic. Also trigger for questions about Annex A.1 (controller controls), A.2 (processor controls), A.3 (shared security controls), or implementing a standalone PIMS without ISO 27001. When in doubt, use this skill — it covers the full ISO 27701 lifecycle from gap assessment through certification.

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Performs structured compliance review, clause redlining, and drafting suggestions for legal documents (privacy policies, data processing agreements, vendor and SaaS contracts) against India's DPDPA 2023 and the EU GDPR. Flags clauses as compliant, at-risk, or non-compliant with reasoning, and proposes ready-to-use model replacement language.

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Review, draft, or redline a Data Processing Agreement (DPA / Auftragsverarbeitungsvertrag / AVV) under Art. 28 GDPR, or prepare a Joint Controller Arrangement under Art. 26 GDPR. Supports bilingual output (DE/EN), both controller- and processor-side perspectives, and two review depths — quick (Art. 28(3)(a)–(h) coverage) and negotiation-grade (clause-by-clause risk scoring).

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The analysis and visualization of alternative splicing (AS) events from RNA sequencing data remains challenging. SpliceWiz is a user-friendly and performance-optimized R package for AS analysis, by processing alignment BAM files to quantify read counts across splice junctions, IRFinder-based intron retention quantitation, and supports novel splicing event identification. We introduce a novel visualization for AS using normalized coverage, thereby allowing visualization of differential AS across

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The QFeatures infrastructure enables the management and processing of quantitative features for high-throughput mass spectrometry assays. It provides a familiar Bioconductor user experience to manages quantitative data across different assa

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Expert knowledge for Azure IoT Operations development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, integrations & coding patterns, and deployment. Use when configuring MQTT/OPC UA data flows, WASM/ONNX processing, Dapr/Fabric RT endpoints, or Arc Kubernetes deployments, and other Azure IoT Operations related development tasks. Not for Azure IoT (use azure-iot), Azure IoT Hub (use azure-iot-hub), Azure IoT Edge (use azure-iot-edge), Azure IoT Central (use azure-iot-central).

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Write performant Eloquent queries with eager loading, reusable scopes, and strict lazy-loading prevention in Laravel. Use when defining model relationships, creating query scopes, or processing large datasets with chunk/cursor.

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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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Inspects videos, extracts frames, and concatenates video clips stored in Volcengine TOS: read video metadata, capture single snapshots, batch-capture multiple frames by explicit timestamps or interval, concatenate multiple video clips, and save results locally or back to TOS. Use this skill when the user needs to get video duration or codec info, capture a poster image or thumbnail, extract frames at specific timestamps, sample frames at regular intervals, merge or join multiple video clips into one — even if they don't explicitly mention 'video processing' or 'frame extraction'.

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CPU-only motion data processing pipeline for game animation: BVH import, contact detection, root decomposition, motion blending, FABRIK IK. No GPU required.

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Delegate complex, long-running tasks to Manus AI agent for autonomous execution. Use when user says 'use manus', 'delegate to manus', 'send to manus', 'have manus do', 'ask manus', 'check manus sessions', or when tasks require deep web research, market analysis, product comparisons, stock analysis, competitive research, document generation, data analysis, or multi-step workflows that benefit from autonomous agent execution with parallel processing.

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。

Market data preparation including OHLCV resampling, gap handling, anomaly detection, normalization, and multi-source merging

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Implement PCI DSS compliance requirements for secure handling of payment card data and payment systems. Use when securing payment processing, achieving PCI compliance, or implementing payment card security measures.

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。