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Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Build data processing pipelines for machine learning, from preprocessing to batch predictions. Uses Ray Data to handle datasets larger than memory, distribute work across CPUs and GPUs, and feed data into training frameworks.

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• Preprocessing datasets larger than memory
• Running batch predictions across multiple GPUs
• Preparing image and tabular data for training

Design n8n automations for incoming events, API connections, database synchronization, AI agents, scheduled tasks, and batch processing. Choose a suitable structure, plan error handling, check connections, test results, and review performance when processing large numbers of items.

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• Sending form submissions to Slack
• Generating and emailing scheduled reports
• Synchronizing records between databases

Handles GDPR Article 18 right to restriction of processing requests, covering the four grounds for restriction (accuracy contest, unlawful processing, erasure opposition, legitimate interest pending), technical flagging mechanisms, and lifting procedures. Activate for restriction request, Art. 18, processing freeze queries.

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Design n8n automations for service integrations, scheduled tasks, databases, and AI agents. Choose from six workflow patterns, plan data flow and error handling, check connections before activation, and improve workflows that process large numbers of items.

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• Send form submissions to Slack
• Email a scheduled analytics report
• Synchronize records between databases

Prepare research papers for RAG, a method that supplies retrieved documents to AI. Download PDFs, extract and split text, and build a local searchable database, with parallel processing for document batches.

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• Build a RAG document collection from selected research papers
• Download and extract text from batches of paper PDFs
• Create a local vector database without paid embedding APIs

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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Video/audio/image processing with FFmpeg and ImageMagick. Tools: FFmpeg (video/audio), ImageMagick (images). Capabilities: format conversion, encoding (H.264/H.265/VP9/AV1), streaming (HLS/DASH), filters, effects, thumbnails, watermarks, batch processing, hardware acceleration (NVENC/QSV). Actions: convert, encode, resize, crop, compress, extract, merge, stream, transcode media. Keywords: FFmpeg, ImageMagick, video encoding, audio extraction, image resize, thumbnail, watermark, HLS, DASH, H.264, H.265, VP9, AV1, codec, bitrate, framerate, resolution, aspect ratio, filter, overlay, concat, trim, fade, batch processing. Use when: converting video/audio formats, encoding with specific codecs, generating thumbnails, creating streaming manifests, extracting audio from video, batch processing images, adding watermarks, optimizing file sizes.

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Use this skill when a company needs its record of processing activities for the first time, or has one that is really a list of tools, and asks "build our RoPA", "records of processing", "data map for GDPR", "Article 30 record", "what personal data do we process and why", "data inventory for privacy" or "our DPO needs a processing register". Drafts the record by processing activity from systems, vendors and team interviews, with purposes, data and subject categories, proposed legal bases, recipients, transfers and concrete retention, plus a separate processor record when the company processes data for customers, and a notes file of open questions per owner.

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Review personal data handling against GDPR requirements, covering processing records, legal grounds, individual rights, breach procedures, and international transfers. Produce findings by article and a prioritized improvement roadmap with responsibilities, estimated costs, and timelines.

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• Preparing evidence for a regulatory audit
• Reviewing privacy requirements for a new system
• Assessing GDPR gaps during acquisition due diligence

Plan data ingestion and transformation workflows with Apache Airflow, Spark, and dbt. Get guidance on processing changed records, validating data quality, and rerunning failed or historical jobs without creating duplicate results.

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• Designing data ingestion and transformation workflows
• Processing only new or changed records
• Adding data quality checks between processing stages

Review a data processing agreement against internal review standards, using the appropriate criteria for your company's role. Receive a clause-by-clause memo with risks, proposed edits, fallback positions, and inconsistencies with privacy policy commitments.

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• Reviewing a customer's data processing agreement
• Checking a vendor's data protection terms
• Finding conflicts with privacy policy commitments

Troubleshoot CSV and XLSX processing when execute_code_sandbox fails. The workflow locates input files, saves processing code as a Python script, captures execution errors, checks dependencies and file access, and confirms output exists before reading it.

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• Recovering spreadsheet processing after sandbox errors
• Locating CSV and XLSX input files
• Diagnosing dependency, path, and encoding errors

Coordinate changes to shared task folders with .processing marker files that indicate ongoing work. Managers and Workers check these markers before editing, synchronize files through MinIO, and remove markers after completing their changes.

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• Coordinate Manager and Worker edits in a shared workspace
• Check for ongoing work before delegated Git operations
• Set an expiration time for a processing marker

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

MS-DIAL-based metabolomics preprocessing as alternative to XCMS. Covers peak detection, alignment, annotation, and export for downstream analysis. Use when processing MS-DIAL output files for R/Python analysis or when preferring GUI-based preprocessing.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Creates GDPR Article 30(1) Records of Processing Activities (RoPA) for data controllers with all seven mandatory fields: controller identity and contact details, processing purposes, data subject categories, personal data categories, recipient categories, third country transfers, and retention periods. Includes Python generator for automated RoPA creation. Activate for controller RoPA, Art. 30(1), processing records, data mapping.

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Spark, pandas, polars, DuckDB optimization for batch data processing. Activate on: batch processing, Spark optimization, polars, DuckDB, pandas performance, data frame, shuffle, partition, memory optimization. NOT for: streaming pipelines (use streaming-pipeline-architect), warehouse queries (use data-warehouse-optimizer).

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Process multimedia files with FFmpeg (video/audio encoding, conversion, streaming, filtering, hardware acceleration) and ImageMagick (image manipulation, format conversion, batch processing, effects, composition). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, extracting audio from video, applying filters and effects, optimizing file sizes, creating streaming manifests (HLS/DASH), generating thumbnails, batch processing images, creating composite images, or implementing media processing pipelines. Supports 100+ formats, hardware acceleration (NVENC, QSV), and complex filtergraphs.

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Use this skill when a new product feature, vendor, AI use, monitoring tool or change in processing raises the question "do we need a DPIA", or someone asks "DPIA screening", "is this high risk processing", "data protection impact assessment threshold", "privacy impact screening for this feature", "does our AI feature need a DPIA" or "document why we did not do a DPIA". Describes the processing with numbers, tests it against the GDPR cases, the supervisory authority's list and the EDPB-endorsed criteria, reaches a reasoned outcome (required, recommended, not required) with a re-screen trigger, and keeps a screening log for accountability.

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ImageMagick CLIを使用した画像処理の専門家スキル。フォーマット変換、リサイズ、トリミング、回転、フィルタ適用、アニメーションGIF作成、PDF処理、バッチ処理を支援。Use when processing images via CLI, converting formats, resizing, cropping, applying filters, creating animations, or batch processing images.

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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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