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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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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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Runs the MS-DIAL preprocessing workflow (peak picking, MS2Dec spectral deconvolution, alignment, gap-filling) and imports the alignment-result table into R or Python with honest filtering. Use when preprocessing LC-MS DDA/DIA (SWATH) raw data with MS-DIAL, deciding MS-DIAL vs XCMS, configuring the MsdialConsoleApp console run, or parsing an MS-DIAL export into a clean feature matrix. For programmatic R peak detection and the feature-table-as-artifact framing see metabolomics/xcms-preprocessing; for lipid annotation mode see metabolomics/lipidomics; for MSI-level confidence honesty see metabolomics/metabolite-annotation; for drift correction and QC see metabolomics/normalization-qc.

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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), ImageMagick (image manipulation, format conversion, batch processing, effects, composition), and RMBG (AI-powered background removal). Use when converting media formats, encoding videos with specific codecs (H.264, H.265, VP9), resizing/cropping images, removing backgrounds from 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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Build or review Java Spring Boot backends using examples for web APIs, separate application layers, and database access. Covers input validation, error handling, caching, background processing, and logging to guide maintainable implementations.

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• Designing web APIs and separating application layers
• Reviewing database access and transactions
• Adding input validation and centralized error handling

通过具体实现示例设计或审查Java的Quarkus服务，涵盖REST API分层、Panache数据访问、Camel消息路由、RabbitMQ集成及异步处理。

• 划分REST API的职责
• 集成Camel与RabbitMQ
• 审查Panache数据保存逻辑

为PDF解析、文本提取和图像分析设计基于文件内容的缓存。移动或重命名文件后仍可复用结果，内容改变时重新处理，并将损坏的缓存视为未保存。

• 复用PDF文本提取结果
• 移动文件后继续复用结果
• 为现有处理函数添加缓存

Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.

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• Submitting AI requests from a JSONL file
• Monitoring batch jobs and waiting for completion
• Saving batch results and error details

Replace a face in a video using a source photo and the HeyGen API. The skill covers submitting image and video URLs, checking processing status, and retrieving the finished video, including workflows that personalize generated avatar videos.

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• Replace a face in a recorded video
• Personalize an avatar video with a face photo
• Track processing and retrieve the finished video

Manage Stripe customers, products, invoices, and recurring subscriptions through its official MCP server, a connection for agent tools. Handle billing changes, payment links, duplicate-charge refunds, disputes, balances, payouts, and processing-fee reconciliation using reviewed operations.

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• Manage recurring subscriptions and billing-plan changes
• Handle invoices and payment links
• Refund duplicate charges and address payment disputes

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.

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

Use PathML for local pathology image research, from loading whole-slide images and splitting them into tiles to preprocessing and quality checks. Plan multiplex measurements, analyze spatial relationships between cells, and run model inference in bounded batches.

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• Planning tile extraction from whole-slide images
• Reviewing preprocessing and image quality
• Validating multiplex measurements and spatial graphs

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.

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

Build Python workflows for filtering, grouping, joining, and reshaping table data with Polars. Get guidance on migrating pandas code and using lazy evaluation, which optimizes a processing plan before execution, for large datasets.

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• Filtering and summarizing large CSV files
• Migrating pandas processing code to Polars
• Joining and reshaping multiple tables

Helps build and review NeuroKit2 research workflows for physiological recordings such as ECG, respiration, and skin conductance. Covers preprocessing, time alignment, event analysis, heart rate variability, and complexity measures, while recording methods and checking data quality.

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• Inspect physiological recordings before preprocessing
• Analyze ECG and heart rate variability for research
• Plan event windows and baseline correction

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.

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• 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

Create automated workflows that add threat intelligence to suspicious IP addresses, domains, URLs, and file hashes. Combine findings from multiple services into consistent alert context and confidence scores to support analysts reviewing security alerts.

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• Adding threat context to security alerts
• Processing indicators from phishing email submissions
• Standardizing bulk indicator lookups from threat feeds

Guides background processing in Node.js and TypeScript with BullMQ and Redis. Covers job queues, which hold tasks for later execution, along with scheduling, priorities, rate limits, worker concurrency, and dependencies between processing steps.

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• Design a queue for background tasks
• Schedule jobs for later or repeated execution
• Organize dependent steps into a job flow

Turn audio and video into text with Whisper, including Japanese and other supported languages. This skill covers transcription, English translation, timestamped subtitles, model selection, and batch processing, with guidance on hardware needs and recognition limitations.

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• Transcribing recorded meetings
• Creating subtitles for videos
• Translating multilingual recordings into English text

Extend the amount of text a language model can process at once. Compares RoPE, YaRN, ALiBi, and position interpolation, with implementation examples, additional training guidance, and memory optimization approaches for long document processing.

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• Extend an existing model to handle longer inputs
• Compare methods for representing token positions
• Plan additional training with long documents

Build custom cognee processing by chaining Python tasks, defining data types, and saving results to graph and vector databases. Use it to enrich existing data, inspect run status, and troubleshoot how tasks pass data.

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• Building custom extraction and processing steps
• Saving custom data types to graph and vector databases
• Adding information to an existing graph

Export data from Google Sheets as CSV for local backups or further processing. The recipe covers checking spreadsheet details, exporting through Google Drive, and reading a specified sheet range directly in CSV format.

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• Keeping a local CSV backup of spreadsheet data
• Exporting spreadsheet data for further processing
• Reading a selected sheet range as CSV