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Found 59 Skills
Process and generate multimedia content using Google Gemini API for better vision capabilities. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (better image analysis than Claude models, captioning, reasoning, object detection, design extraction, OCR, visual Q&A, segmentation, handle multiple images), process videos (scene detection, Q&A, temporal analysis, YouTube URLs, up to 6 hours), extract from documents (PDF tables, forms, charts, diagrams, multi-page), generate images (text-to-image with Imagen 4, editing, composition, refinement), generate videos (text-to-video with Veo 3, 8-second clips with native audio). Use when working with audio/video files, analyzing images or screenshots (instead of default vision capabilities of Claude, only fallback to Claude's vision capabilities if needed), processing PDF documents, extracting structured data from media, creating images/videos from text prompts, or implementing multimodal AI features. Supports Gemini 3/2.5, Imagen 4, and Veo 3 models with context windows up to 2M tokens.
This skill should be used when analyzing video files. Claude cannot process video directly, so this skill extracts frames hierarchically - starting with a quick overview, then zooming into regions of interest with higher resolution and temporal density. Use when asked to watch, analyze, review, or understand video content.
Investigates hypotheses that MEV activity (bundles, searchers, same-block ordering) temporally overlaps or co-occurs with launch-phase rug signals—using public txs, bundle IDs, and clustering with explicit confidence. Use when the user asks about MEV plus rug coordination, launch sniper bundles, Jito or Flashbots overlap with dev exits, or joint profit-flow case studies—not for alleging collusion without evidence, harassing addresses, or live interference.
Extract frames from video files using ffmpeg for AI/LLM analysis. Use when (1) the user asks to analyze, describe, or summarize a video file, (2) the user wants to extract frames or screenshots from a video, (3) the user provides a video file (.mp4, .mov, .avi, .mkv, .webm, etc.) and asks questions about its visual content, (4) the user wants to identify scenes, objects, or events in a video, (5) the user wants timestamps overlaid on extracted frames for temporal reference. Converts video into JPEG frames that can be attached to LLM prompts as images. Requires ffmpeg on PATH. Supports scene-change detection, model-aware optimization (Claude/OpenAI/Gemini), quality presets (efficient/balanced/detailed/ocr), grayscale and high-contrast OCR mode, and automatic FPS calculation via --max-frames.
Use when writing, fixing, or editing TypeScript modules, classes, file structure, declaration order, vertical formatting, dependency direction, cohesion, coupling, dependency construction, temporal coupling, public exports, wiring, or over-abstraction.
Action recognition from video sequences. Supports RGB, optical flow, and joint (multi-stream) input types for classifying temporal actions in video clips. Use when training, evaluating, exporting, or running inference on a TAO action-recognition model. Trigger phrases include "train action recognition", "video action classification", "RGB + optical flow action model", "TAO ActionRecognition".
Pose classification using ST-GCN (Spatial Temporal Graph Convolutional Network). Classifies skeleton sequences into action categories from pose-keypoint data. Use when training, evaluating, exporting, or running inference for a TAO pose-classification model. Trigger phrases include "train pose classification", "skeleton action recognition", "ST-GCN", "keypoint sequence classifier".
This skill should be used for time series machine learning tasks including classification, regression, clustering, forecasting, anomaly detection, segmentation, and similarity search. Use when working with temporal data, sequential patterns, or time-indexed observations requiring specialized algorithms beyond standard ML approaches. Particularly suited for univariate and multivariate time series analysis with scikit-learn compatible APIs.
Expert in event sourcing, CQRS, and event-driven architecture patterns. Masters event store design, projection building, saga orchestration, and eventual consistency patterns. Use PROACTIVELY for event-sourced systems, audit trails, or temporal queries.
Async communication patterns using message brokers and task queues. Use when building event-driven systems, background job processing, or service decoupling. Covers Kafka (event streaming), RabbitMQ (complex routing), NATS (cloud-native), Redis Streams, Celery (Python), BullMQ (TypeScript), Temporal (workflows), and event sourcing patterns.
ARIMA, SARIMA, Prophet, trend analysis, seasonality detection, anomaly detection, and forecasting methods. Use for time-based predictions, demand forecasting, or temporal pattern analysis.
Gets, checks, and verifies the current UTC date and time for unambiguous temporal reference. Use when starting tasks, verifying temporal context, ensuring date awareness before time-sensitive operations, or when incorrect date assumptions are detected.