Results for “data processing”

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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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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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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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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Governs biometric data processing for employee timekeeping and access control under Art. 9 GDPR special category rules. Covers fingerprint, facial recognition, iris scanning, and voice recognition. Applies necessity tests, evaluates less intrusive alternatives, and implements employee objection procedures. Keywords: biometric data, Art. 9, fingerprint, facial recognition, access control, timekeeping, special category.

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Conducts Data Protection Impact Assessments for AI and ML systems per EDPB Guidelines 04/2025 on AI processing. Covers training data lawfulness evaluation, model risk assessment, automated decision triggers, and AI-specific DPIA methodology. Keywords: AI DPIA, machine learning impact assessment, EDPB AI guidelines, model risk, training data.

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Generates Records of Processing Activities automatically from IT system inventories including Active Directory, cloud service catalogs, API gateway logs, and database schemas. Covers automated field population, data flow discovery, and system-to-RoPA mapping. Activate for automated RoPA, system inventory, data discovery, auto-population, IT-driven records.

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Fix content serving failures caused by metadata (KV store, database) being out of sync with actual storage (GCS, S3, filesystem). Use when: (1) Content exists in storage but API returns "processing" or "not ready", (2) Webhook callbacks that update metadata failed silently, (3) Backfill scripts generated content but didn't update status records, (4) HLS/transcoded content exists but transcode_status is not "Complete", (5) Handler checks metadata status BEFORE checking if content actually exists in storage.

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Computes a numeric or categorical answer to a quantitative data-science question by cleaning and analyzing local data files (CSV, Excel, TSV, and scientific formats .npz/.fits/.h5) with pandas, numpy, and scipy. Use whenever a task ships its own dataset (in whatever local directory it provides) and asks you to derive a value via statistical, geospatial, temporal, ratio, percentage, growth-rate, correlation, signal-processing, orbital, or forecasting analysis. Spans domains including archeology (demographics, paleoclimate proxies, radiocarbon dating, historical conflicts); biomedical/bioinformatics (tumor histology, genes, proteins, peptides, genomic variants); environmental (water quality, bacterial/Enterococcus exceedance, rainfall correlations, environmental justice); legal/consumer (fraud, identity theft, consumer complaints by state/metro); wildfire (incidents, causes, suppression costs, acres burned, fatalities, geospatial intersections); and astronomy/heliophysics/space weather (satellite telemetry TLE/Swarm/SP3, indices OMNI/GOES, solar activity, atmospheric density, orbital mechanics, forecasting).

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Process, analyze, and visualize geospatial data at scale. Handles drone imagery, GPS tracks, GeoJSON optimization, coordinate transformations, and tile generation. Use for mapping apps, drone data processing, location-based services. Activate on "geospatial", "GIS", "PostGIS", "GeoJSON", "map tiles", "coordinate systems". NOT for simple address validation, basic distance calculations, or static map embeds.

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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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Manages MongoDB Atlas Stream Processing (ASP) workflows. Handles workspace provisioning, data source/sink connections, processor lifecycle operations, debugging diagnostics, and tier sizing. Supports Kafka, Atlas clusters, S3, HTTPS, and Lambda integrations for streaming data workloads and event processing. NOT for general MongoDB queries or Atlas cluster management. Requires MongoDB MCP Server with Atlas API credentials.

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The General Data Protection Regulation (EU) 2016/679 (GDPR) is the EU's comprehensive data protection law governing the collection, processing, storage, and transfer of personal data. This skill cover

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Credit risk data cleaning and variable screening pipeline for pre-loan modeling. Use when working with raw credit data that needs quality assessment, missing value analysis, or variable selection before modeling. it covers data loading and formatting, abnormal period filtering, missing rate calculation, high-missing variable removal,low-IV variable filtering, high-PSI variable removal, Null Importance denoising, high-correlation variable removal, and cleaning report generation. Applicable scenarios arecredit risk data cleaning, variable screening, pre-loan modeling preprocessing.

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DuckDB CLI specialist for SQL analysis, data processing and file conversion. Use for SQL queries, CSV/Parquet/JSON analysis, database queries, or data conversion. Triggers on "duckdb", "sql", "query", "data analysis", "parquet", "convert data".

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Manage files in Box, organize folders, share content, and search documents or metadata. Use Box AI to summarize files, answer questions, and extract information, with guidance for bulk processing and building applications connected to Box.

• Organizing and sharing team files in Box
• Summarizing documents and answering questions about them
• Extracting and saving metadata from documents

Check untrusted text before passing it to a model or executing it. Using AIDefence, it provides safety checks, detailed threat classifications, confidence scores, and scanning for attempts to override instructions or extract data.

• Checking user submissions before model processing
• Scanning API payloads and webhook data
• Inspecting tool outputs for hidden instructions

Build and evaluate time-to-event models with scikit-survival, including records whose events remain unobserved when follow-up ends. Keep preprocessing separate from evaluation data, compare models, predict survival probabilities, and summarize censoring-aware metrics.

• Validate event indicators and follow-up times in local CSV data
• Train survival models without leaking evaluation data
• Evaluate survival probabilities with censoring-aware metrics

Validate particle data and run RELION refinement for single-particle cryo-electron microscopy. Check independently refined half maps, calculate diagnostic map correlations, and perform soft-mask postprocessing while preserving execution logs and restart information.

• Check particle metadata and image-stack consistency
• Run refinement on a selected particle population
• Inspect agreement between independent half maps

Build Python machine learning workflows for classification, numerical prediction, and grouping similar data. Combine preprocessing with training, compare algorithms, tune model settings, and evaluate performance using data splits suited to the task.

• Building classification or numerical prediction models
• Grouping similar records and visualizing the results
• Combining missing-value handling with model training

Optimize scientific Python workloads for NVIDIA GPUs by choosing suitable libraries and reducing bottlenecks. Compare outputs with the CPU version and measure complete processing time, including data transfers, to determine whether the GPU implementation provides a useful improvement.

• Assess whether slow Python processing would benefit from a GPU
• Move NumPy or pandas workloads to suitable GPU libraries
• Validate GPU results against CPU outputs

Help secure payment systems using PCI DSS, the payment card data security standard. Covers encryption, log masking, access controls, and tokenization, which replaces card details with substitute values to reduce direct handling of sensitive data.

• Designing secure payment flows
• Replacing card details with payment tokens
• Reviewing sensitive data in logs and storage

Measure Python execution time and memory use to locate slow or resource-heavy code. Use profiling results to guide improvements in data structures, caching, input and output operations, algorithms, and parallel processing.

• Finding slow functions in Python applications
• Reducing memory use and investigating leaks
• Improving data processing and database queries

Route data analysis, large-file processing, and web retrieval through context-mode tools, returning only relevant results to the conversation. This helps preserve the context window, the limited amount of information an AI can work with at once.

• Counting or filtering large datasets
• Retrieving relevant information from web pages
• Collecting and searching results from multiple commands