Results for “computer”

Think and work like an expert Computer Hardware Engineer. Use when a task calls for Computer Hardware Engineer judgment. Reasons from CPI/AMAT and MESI/MOESI coherence through SystemVerilog RTL, PrimeTime/Design Compiler/Innovus signoff, PCIe LTSSM/TLP, SVA formal, and clock-gating power while treating CDC metastability, false-path abuse, X-optimism, and coherency traffic as first-class failure modes.

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Think and work like an expert Brain–Computer Interface Engineer. Use when a task calls for Brain–Computer Interface Engineer judgment. Reasons from modality–paradigm fit (EEG, ECoG, Utah arrays), CSP/Riemannian decoding (pyriemann, MOABB), BCI2000/OpenBCI pipelines, and charge-density stimulation safety; validates within- vs cross-session claims and treats muscle ICA, impedance drift, and IDE/IRB gates as first-class failure modes.

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Systematically QA test any application — web apps, native macOS apps, Electron apps, CLI tools, interactive REPLs, or anything on screen. Three modes: browser (chromux/CDP, fast, DOM-level), computer (MCP computer-use, screenshot + pixel clicks, any app), and cli (tmux, send-keys + capture-pane for interactive terminals). Auto-selects mode or accepts --browser / --computer / --cli override. Use when asked to "qa", "QA", "test this site", "test this app", "find bugs", "test and fix", "fix what's broken", "dogfood", "exploratory test", "bug hunt", "QA this app", "사이트 테스트", "앱 테스트", "브라우저 QA", "화면 보고 테스트해줘", "네이티브 앱 테스트", "screen test". Three tiers: Quick (critical/high only), Standard (+ medium), Exhaustive (+ cosmetic). Produces before/after health scores, fix evidence, and a ship-readiness summary.

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Claude computer use is an Anthropic API tool that lets Claude see a desktop through screenshots and operate it with mouse and keyboard. Use when automating GUI workflows, browser automation without CSS selectors, desktop app testing, or any task that requires seeing and interacting with a graphical interface.

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Execute programs on a compiled transformer stack machine where every instruction fetch and memory read is a parabolic attention head. Demonstrates that transformer attention + FF layers can implement a working computer. Use when user mentions "llm-as-computer", "lac", "stack machine", "compiled transformer", "percepta", "parabolic attention", "execute program", or asks to run/trace programs on the transformer executor.

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

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

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

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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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Set up recurring work in Claude Code by combining persistent memory, scheduled prompts, task queues, and optional computer control. The skill explains how to connect these components, including external scheduling for work that continues after a session closes.

• Reviewing pull requests at regular intervals
• Collecting and summarizing research findings daily
• Preparing meeting notes from saved context

Design agents that operate desktop or browser software through screenshots, mouse actions, and keyboard input. Compare ways to locate interface elements, diagnose coordinate errors, handle changing screens, and evaluate task completion, operating speed, and safety boundaries.

• Designing screen-based automation for software without APIs
• Comparing methods for locating interface elements
• Investigating misclicks and coordinate scaling errors