'compute' 검색 결과

Access ENCODE uniform analysis pipelines, generate user-specific Nextflow/WDL pipelines, manage compute resources, and integrate with cloud platforms. Use when the user wants to understand ENCODE pipelines, run pipelines on their own data, generate custom Nextflow workflows from ENCODE pipeline code, check compute requirements (CPU/GPU/memory), run pipelines in background, or integrate with Google Cloud, AWS, or other cloud platforms. Also use when the user asks about ENCODE pipeline outputs, processing standards, software versions, or wants to replicate ENCODE processing. Covers local execution, HPC, and cloud deployment with resource-aware scheduling. Use this skill for ANY pipeline execution, workflow generation, or compute resource management task involving ENCODE data.

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Use native computer-use safely in Decisions workflows when the active harness exposes a CUA or computer-use runtime.

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Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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Expert-level computer architecture covering CPU design, instruction sets, pipelining, cache hierarchy, memory systems, parallel architectures, and GPU computing.

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Expert-level computer vision for robotics covering camera models, feature detection, object detection, pose estimation, visual SLAM, and deep learning for perception.

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Expert-level computer graphics covering the rendering pipeline, rasterization, ray tracing, shaders, transformations, lighting models, and GPU programming.

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Construa agentes de IA que interagem com computadores como humanos fazem - visualizando telas, movimentando cursores, clicando botões e digitando texto. Cobre Computer Use da Anthropic, Operator/CUA da OpenAI e alternativas de código aberto. Foco crítico em sandboxing, segurança e tratamento dos desafios únicos do controle baseado em visão. Use quando: computer use, automação de desktop agent, controle de tela IA, agente baseado em visão, automação de GUI.

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Let agents run disposable VMs for computer-use and GUI automation instead of controlling the host desktop.

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Computer Use Agent — autonomous browser control via Steel, Browser Use, and Browserbase cloud providers. Supports headful CDP sessions, CAPTCHA solving, proxy rotation, live view, session recording/replay, Playwright/Puppeteer integration, and LLM-driven visual browsing. Use when asked about computer use, browser automation, web scraping, Steel sessions, cloud browsers, headful browsing, CAPTCHA solving, or autonomous web navigation.

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E2B Desktop Sandbox with autonomous computer use — cloud Linux environments for browser automation, visual UI grounding, screenshot analysis, and shell execution. Supports Browser Use cloud sessions, Playwright integration, and LLM-driven desktop control. Use when asked about sandboxes, computer use, browser automation, E2B, desktop control, visual testing, or cloud Linux environments.

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pi-cua-integration is a Pi/Senpi extension that bridges Cua (trycua/cua) for computer-use sandboxes and host control. MUST USE when the user asks to drive a desktop UI, take screenshots, run a "computer use" task, control a sandbox, or invoke any cua_* tool. Triggers - "cua", "computer use", "sandbox", "screenshot the desktop", "click the screen", "take over the browser", "샌드박스 켜줘", "스크린샷 찍어", "내 컴퓨터에서 자동화".

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Background processing and heavy computations using Dart Isolates with Isolate.run(), compute() function, and platform channels. Use this skill when performing CPU-intensive operations without blocking UI (image processing, data parsing, encryption), implementing background tasks that prevent jank or frame drops, handling large file operations (JSON parsing, database migrations), running parallel computations, fixing UI freezes caused by main thread blocking, using compute() for one-off background work, creating long-lived Isolates with ReceivePort/SendPort, implementing multi-threaded algorithms, debugging isolate communication issues, or preventing memory leaks from unclosed ReceivePorts. Covers isolate spawn, message passing, ReceivePort cleanup (CRITICAL for leak prevention), SendPort communication patterns, error handling, serialization rules, and performance optimization for compute-heavy operations.

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Build AI agents that interact with computers like humans do - viewing screens, moving cursors, clicking buttons, and typing text. Covers Anthropic's Computer Use, OpenAI's Operator/CUA, and open-so...

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Computer Vision Specialist IA — Expert en vision par ordinateur (YOLO, OpenCV, object detection, OCR, segmentation)

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Computer Vision v2 IA — Expert en vision par ordinateur (détection d'objets, segmentation, OCR, vidéo, déploiement edge)

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Integrate and optimize Core ML models in iOS apps for on-device machine learning inference. Covers model loading (.mlmodelc, .mlpackage), predictions with auto-generated classes and MLFeatureProvider, compute unit configuration (CPU, GPU, Neural Engine), MLTensor, VNCoreMLRequest, MLComputePlan, multi-model pipelines, and deployment strategies. Use when loading Core ML models, making predictions, configuring compute units, or profiling model performance.

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NVIDIA CUDA GPU 並列コンピューティングリファレンス。 CUDA C++ / CUDA Python, kernel, nvcc, Unified Memory, CUDA Graphs, Cooperative Groups, Green Contexts, Driver API, マルチ GPU。 PTX ISA 命令セット, state space, MMA 命令。 Blackwell チューニング, Streaming Multiprocessor, NVLink, Locality Domains, Compute Fabric Transport。 CUTLASS / CuTe DSL / CuTe C++ GEMM, Tensor Core, tcgen05, Task Scheduling, Operator API, nvMatmulHeuristics, nsight-compute, compute-sanitizer。

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Builds an interactive HTML dashboard visualizing the Agent Skills ecosystem by fetching live data from github.com/agentskills/agentskills. Use this skill whenever the user wants to explore, research, or visualize Skills architecture, the SKILL.md spec, ecosystem adoption, repo stats, or any Skills intelligence dashboard. Trigger when the user mentions "skills dashboard", "skills ecosystem", "skills architecture overview", "agentskills repo", or wants to analyze how different tools implement Agent Skills. Optimized for Perplexity Computer — Computer fetches all data live, no static files required.

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Build production computer vision pipelines for object detection, tracking, and video analysis. Handles drone footage, wildlife monitoring, and real-time detection. Supports YOLO, Detectron2, TensorFlow, PyTorch. Use for archaeological surveys, conservation, security. Activate on "object detection", "video analysis", "YOLO", "tracking", "drone footage". NOT for simple image filters, photo editing, or face recognition APIs.

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Expert in 3D computer vision labeling tools, workflows, and AI-assisted annotation for LiDAR, point clouds, and sensor fusion. Covers SAM4D/Point-SAM, human-in-the-loop architectures, and vertical-specific training strategies. Activate on '3D labeling', 'point cloud annotation', 'LiDAR labeling', 'SAM 3D', 'SAM4D', 'sensor fusion annotation', '3D bounding box', 'semantic segmentation point cloud'. NOT for 2D image labeling (use clip-aware-embeddings), general ML training (use ml-engineer), video annotation without 3D (use computer-vision-pipeline), or VLM prompt engineering (use prompt-engineer).

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Claude Code의 기억, 정기 실행, 작업 대기열을 결합해 지속적인 작업을 설계합니다. 외부 실행 환경과 화면 조작 연동 설정도 안내합니다.

• 풀 리퀘스트를 정기적으로 확인할 때
• 매일 조사 결과를 모아 요약할 때
• 기억한 맥락으로 회의를 준비할 때

개발 중인 Hermes 데스크톱에서 요소 존재 여부, 실제 CSS 값, 우선 적용된 규칙, 콘솔 출력을 읽어 화면 변경의 반영 여부와 원인을 확인합니다.

• 실행 중인 화면에서 UI 변경 확인
• 실제로 적용된 CSS 규칙 조사
• 디자인 토큰의 실제 값 확인

입자, 프랙털, 데이터의 움직임을 단일 HTML로 시각화합니다. 무거운 계산은 Web Worker로 분리하고 초당 약 60프레임을 목표로 화면을 그립니다.

• 입자와 물리 시뮬레이션 시제품 제작
• 프랙털 시각화
• 오디오와 데이터 시각화 화면 제작