'data processing' 검색 결과

Process spreadsheets with Python after checking source files, required sheets and columns, and external data access. The workflow includes backup source checks and clear failure reports when the required data cannot be reached.

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• Check required files and columns before processing Excel
• Verify external data access before updating a spreadsheet
• Check backup data when the primary source is unavailable

Validate spreadsheet sources before running Python scripts to transform data, update multiple sheets, or apply formatting. Check required data first, try backup sources when needed, and report errors clearly if processing cannot proceed.

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• Checking required columns before processing Excel files
• Transforming data and saving a new workbook
• Updating formatting across multiple sheets

Retrieve public eQTL Catalogue association data, linking genes and genetic variants, along with study metadata. Filter requests to specific identifiers and receive concise Markdown summaries or raw JSON when machine-readable output is requested.

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• Look up association data for a specific gene
• Filter association results by a genetic variant
• Check study metadata in the public catalogue

Test chatbots and AI agents for injected instructions, data leaks, and unauthorized access across accounts. It emphasizes reproducible evidence, verified external requests, and real data access rather than trusting the model's claims.

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• Test document-based prompt injection
• Verify data leaks through tools or rendered images
• Check data isolation between accounts

Write MATLAB or GNU Octave scripts for matrix calculations, data analysis, signal processing, and scientific plots. The skill also helps explain syntax and functions, convert code between MATLAB and Python, and run scripts when an interpreter is installed.

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• Analyzing experimental CSV data and saving plots
• Solving matrix equations and differential equations
• Analyzing signal frequencies and applying filters

Develop and check research code in MATLAB/Octave or Python for signal processing, image analysis, statistics, and simulations. This skill supports reproducible workflows, checks units and assumptions, and prepares figures for papers and presentations.

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• Debugging MATLAB signal-processing scripts
• Analyzing sensor time series in Python
• Checking units and assumptions in optical simulations

Build Next.js 14+ applications with App Router, covering pages, server-side processing, data fetching, and caching. The skill also guides performance improvements, search engine metadata, and production deployment to Vercel or a self-hosted environment.

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• Building pages and layouts with App Router
• Implementing form updates with Server Actions
• Configuring data fetching and cache revalidation

Process mass spectrometry data in Python with PyOpenMS. Read common file formats, smooth and normalize signals, detect peaks, align samples, and process peptide identification results for protein and metabolite quantification workflows.

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• Reading mzML files and inspecting spectra
• Smoothing and normalizing mass spectrometry signals
• Aligning and comparing metabolite peaks across samples

Build image and video AI systems for object detection and segmentation, which identifies separate image regions. The skill covers custom model training, dataset pipelines, inference optimization, real-time processing, and deployment and monitoring in production.

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• Developing object detection and image segmentation systems
• Training custom vision models with project data
• Optimizing inference for real-time video processing

Build and evaluate machine learning models in Python with scikit-learn. Covers classification, numerical prediction, grouping similar data, preprocessing, model comparison, and pipelines that combine data preparation and prediction into a repeatable workflow.

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• Build classification or numerical prediction models
• Group customers with similar characteristics
• Compare models using cross-validation

Prepare pathology image datasets by detecting tissue in whole slide images and extracting smaller image tiles. Use random, grid, or score-based selection, preview extraction locations, and apply preprocessing filters to review and prepare the resulting tiles.

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• Sampling tissue regions for an initial review
• Extracting tiles across tissue in a grid
• Selecting tiles by nuclei density scores

Process datasets that exceed available memory and run Python computations across multiple cores or machines. The skill guides large table and array operations, multi-file cleaning and aggregation, custom task dependencies, and execution settings for parallel workflows.

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• Aggregate multiple CSV or Parquet files
• Process numeric arrays larger than available memory
• Clean JSON records and logs for analysis

Look up biological evidence about genes, variants, diseases, pathways, and proteins across databases. Map identifiers between sources and organize findings into comparison tables that preserve source names, query terms, access dates, and access limitations.

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• Map gene identifiers across databases
• Gather evidence on variant clinical significance
• Compare target–disease association evidence

Comprehensive guide for Go database access — parameterized queries, struct scanning, NULLable columns, transactions, isolation levels, SELECT FOR UPDATE, connection pool, batch processing, context propagation, and migration tooling. Use when writing, reviewing, or debugging Golang code that interacts with PostgreSQL, MariaDB, MySQL, or SQLite; for database testing; or for questions about database/sql, sqlx, or pgx. Does NOT generate database schemas or migration SQL.

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An overview of the core data API of ObsPy, a Python framework for processing seismological data. It is useful for parsing common seismological file formats, or manipulating custom data into standard objects for downstream use cases such as ObsPy's signal processing routines or SeisBench's modeling API.

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You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.

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Load this first for any task involving images, pictures, photos, scans, frames, volumes, or visual data — including reading, writing, filtering, enhancing, denoising, sharpening, deblurring, thresholding, segmenting, masking, detecting, finding or counting objects, removing backgrounds, measuring, comparing, registering, stitching, aligning, cropping, resizing, transforming, or analyzing. Use for image I/O (TIFF, PNG, JPEG, RAW, HDR), histograms, edge detection, color processing, morphology, geometric transforms, deep learning for images, quality assessment, and 3-D volume processing. Also triggers when users describe image problems (dark, noisy, blurry, low contrast, uneven lighting, hazy, color cast) or data exploration tasks (folder of images, characterize data, understand format). Handles microscopy (cells, particles) and general scientific imaging. Routes to sibling skills for medical imaging (DICOM, NIfTI), hyperspectral data, and visual inspection.

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Assess obligations under Regulation (EU) 2023/2854 for connected products, related services, statutory B2B data sharing, unfair data-contract terms, exceptional-need B2G requests, data-processing-service switching, international governmental access, interoperability, smart contracts, enforcement, and non-EU representatives. Use for Data Act scope analysis, gap assessments, product and service design, request handling, contract review, cloud exit planning, or cross-regulation mapping.

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Comprehensive toolkit for developing with the CocoIndex library. Use when users need to create data transformation pipelines (flows), write custom functions, or operate flows via CLI or API. Covers building ETL workflows for AI data processing, including embedding documents into vector databases, building knowledge graphs, creating search indexes, or processing data streams with incremental updates.

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Use this skill when working with Hugging Face Datasets: loading local or Hub datasets, defining Features schemas, processing/streaming datasets, converting formats, sharing to the Hub, managing cache/offline behavior, or using datasets-cli.

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SaaS vendor data processing inventory management. Covers shadow IT discovery, API-based data flow detection, processing purpose mapping, contract status tracking, and continuous inventory reconciliation for cloud service providers.

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Handles GDPR Article 21 right to object to processing, including compelling legitimate grounds assessment, ceasing processing obligations, documentation requirements, and the relationship with erasure under Article 17(1)(c). Activate for right to object, Art. 21, objection to processing, legitimate interest queries.

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Conducts Privacy Impact Assessment for vendor and third-party data processing arrangements. Covers processor due diligence, Data Processing Agreement (DPA) requirements under GDPR Article 28, sub-processor management, cross-border vendor transfers, cloud service provider assessments, and ongoing vendor monitoring. Keywords: vendor PIA, processor assessment, DPA, Article 28, sub-processor, cloud privacy, third-party risk.

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Assesses the GDPR Article 30(5) exemption for organisations under 250 employees. Covers the three exception conditions that negate the exemption: non-occasional processing, risk to data subject rights, and special category data processing. Activate for Art. 30(5), 250 employee exemption, small business RoPA, SME exemption, occasional processing.

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