1.6K skills · sorted by relevance
Expert-level signal processing covering Fourier analysis, sampling theory, digital filters, FFT, spectral estimation, and adaptive filtering.
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Process media files (video, audio, images, documents) using Transloadit. Use when asked to encode video to HLS/MP4, generate thumbnails, resize or watermark images, extract audio, concatenate clips, add subtitles, OCR documents, or run any media processing pipeline. Covers 86+ processing robots for file transformation at scale.
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Reads a project or processing description and returns a DRAFT data protection impact assessment pack: processing description, necessity and proportionality questions, a risk table with blank rating cells, mitigations to consider and open questions. Never rates risk, determines the lawful basis or judges adequacy; the DPO assesses and signs. Works from descriptions, never personal data. Use when the user asks to "start a DPIA for the new monitoring tool", "draft a data protection impact assessment", "scaffold a DPIA from this processing description" or "what would the DPO need for a DPIA on this". Do not use for redacting or de-identifying a document, use document-deidentification-pass instead; do not use for working out what data an incident exposed, use data-incident-impact-brief instead. Drafts for human review; never approves, authorises or signs off.
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End-to-end media pipeline automation — from AI generation through optimization to social publishing. Covers workflow design, WordPress integration, Redis job queuing, batch processing, multi-site pipelines, and orchestration. MCP bridge tools are the primary interface for individual operations; the Design Stack media worker handles batch and advanced processing. Use when you need to automate image generation→optimization→publishing chains, set up cross-site media sync, design batch processing pipelines, or orchestrate multi-step media workflows triggered by post publish or cron.
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Schedule and execute background work on iOS using BGTaskScheduler. Use when registering BGAppRefreshTask for short background fetches, BGProcessingTask for long-running maintenance, BGContinuedProcessingTask (iOS 26+) for foreground-started work that continues in background, background URLSession downloads, or background push notifications. Covers Info.plist configuration, expiration handling, task completion, and debugging with simulated launches.
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This skill should be used at the start of any computationally intensive scientific task to detect and report available system resources (CPU cores, GPUs, memory, disk space). It creates a JSON file with resource information and strategic recommendations that inform computational approach decisions such as whether to use parallel processing (joblib, multiprocessing), out-of-core computing (Dask, Zarr), GPU acceleration (PyTorch, JAX), or memory-efficient strategies. Use this skill before running analyses, training models, processing large datasets, or any task where resource constraints matter.
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Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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Designs QC, corrects signal drift, removes batch effects, filters features, normalizes samples, and imputes missing values for untargeted LC-MS/GC-MS metabolomics, framing each step as a measurement model that can create or erase biological signal. Use when processing a peak/feature table before statistical analysis, choosing a drift-correction or sample-normalization method, deciding QC RSD vs D-ratio filtering, or handling left-censored missing values. The feature table is produced by metabolomics/xcms-preprocessing or metabolomics/msdial-preprocessing; transformation/scaling for modeling defers to metabolomics/statistical-analysis; cross-study design issues link to experimental-design/batch-design.
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Build batch processing systems for AI workloads. TRIGGERS - Use when user needs help with ai-batch-processing related tasks.
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Add background jobs to Django with Celery, moving work such as email delivery and PDF generation outside web requests. Covers scheduled runs, retries, linked or parallel tasks, monitoring, and tests, with Redis or RabbitMQ carrying task messages.
Set up and optimize large language model inference, or response generation, on NVIDIA GPUs. Use quantization, batch processing, and multiple GPUs to improve serving performance, and expose models through a chat completion API.
Run machine learning training and predictions on cloud GPUs, then publish models as APIs that other applications can call. The skill guides Python-based setup, automatic scaling, persistent storage, parallel processing, and scheduled jobs.
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.
Build 3D elements and interactive browser experiences with Three.js. The skill covers scene setup, materials that define surface appearance, user controls, and post-processing, which adds visual effects after rendering. The full workflow requires the upstream skill bundle.
Analyze application and system performance to find bottlenecks, the points that slow processing. Use resource usage and performance data to plan improvements to resource allocation, load balancing, caching, and algorithms, then compare results after changes.
Analyze large matrices before solving linear systems. Check structure and numerical stability, recommend preprocessing and solver settings, estimate selected solution values without a full solve, and assess convergence, memory use, and computational cost.
Inspect how a task ran across agents by collecting recorded execution spans, or timed processing steps. Build a trace tree showing agent activity, duration, errors, and bottlenecks, with a summary of the execution flow.
Generate a spending report from recorded AI usage, with token counts and USD costs grouped by model, agent, and processing tier. Check budget utilization, remaining funds, and alerts for a selected period.
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.
Review TypeScript settings and indexed access to arrays and objects to find values that may be undefined. Enable compiler checks and update unsafe code so potential failures become visible before the code runs.
Add glow, depth-based blur, color correction, and other finishing effects to Three.js scenes. Covers combining effects with EffectComposer, writing custom shaders for screen effects, handling window resizing, and adjusting rendering workload.
Create and edit QGIS map projects from the terminal. Add geographic features and attributes, arrange maps and labels in print layouts, export PDFs or images, and run QGIS algorithms for processing geographic data.
Automate Krita image workflows through commands for project setup, layer management, filters, canvas resizing, and export. Use it for repeatable editing tasks, batch image processing, and structured results that AI agents can read.
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.