303 件 ・ 関連度順
概要と使いどころ
Use when a task needs the judgment of a Postsecondary Computer Science Professor — diagnosing why a course's DFW (D/F/withdraw) rate spiked, redesigning an intro programming course's assessment structure, staffing TAs and grading load for a large course, deciding what to prioritize pre-tenure, running an academic-integrity (MOSS similarity) investigation, or mapping a curriculum change against ABET student outcomes.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a computer programmer implementing to a given specification — translating a design/pseudocode/systems-analyst spec into working code in a specified language, flagging spec ambiguity before writing code, maintaining or modernizing an existing codebase against its original contract, or writing unit tests that prove the implementation matches the spec (not designing the system the spec describes).
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a Computer User Support Specialist — triaging an inbound ticket's severity and SLA clock before troubleshooting it, verifying a caller's identity before any password/MFA/account-state change, walking a live incident through a structured troubleshooting sequence instead of guessing at a fix, deciding whether a request is an incident, a service request, or the underlying problem behind a string of incidents, or judging whether a ticket should resolve at tier 1 or genuinely needs escalation.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a Computer and Information Research Scientist — designing an ablation to attribute an empirical gain, budgeting an NSF CISE proposal against page and content limits, deciding what reproducibility artifacts to release with a paper, screening a draft for mathiness or terminology misuse, or scoping the consent/ethics review needed before running an experiment on real users.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a Computer Systems Analyst — running requirements elicitation and gap analysis against a target system, writing a feasibility study for a build-vs-buy-vs-customize decision, designing a system integration approach (point-to-point vs middleware vs API-led), scoping data-migration risk before a cutover, or choosing between phased and big-bang implementation strategies.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a Computer Hardware Engineer — designing a power delivery network and selecting decoupling capacitors for a digital IC's core rail, budgeting a high-speed signal's rise time against trace impedance and via stub length, closing static timing on a board-level DDR or SerDes interface, sizing thermal resistance for a package's TDP, or reviewing a PCB layout for signal- or power-integrity risk before fab release.
日本語の概要は準備中です。原文の説明を表示しています。
Use when a task needs the judgment of a Computer and Information Systems Manager (IT Manager/Director/CIO-adjacent) — planning IT infrastructure and systems strategy, evaluating a technology vendor or platform decision, allocating an IT budget, or managing IT risk (security, uptime, technical debt) at an organizational level. Broader than the devops-sre or software-engineer roles — this one owns IT as a business function, not a specific engineering discipline.
日本語の概要は準備中です。原文の説明を表示しています。
Give an AI agent its own real browser over MCP tool calls - launch, navigate, click, fill, screenshot, extract text, run JS - with a kernel-level real-device fingerprint and a persistent profile, so the session stays logged in between runs and pages see one coherent device instead of a headless build. No Playwright or SDK code to write. Use when an agent should operate a site itself, when a computer-use / browser-use setup needs a captured real fingerprint rather than a synthetic one, when agent sessions keep losing their login, or when comparing hosted agent-browser services. Also for 'MCP browser', 'browser MCP server', 'let my agent browse the web', 'agent browser control', 'browser-use MCP', 'computer use browser', 'Browserbase alternative', 'Steel browser alternative', 'headless browser detected'. Node (npx) or Python; Windows x64, macOS Intel + Apple Silicon, Linux x64 / arm64. SDK and REST reference is anti-detect-browser; account isolation is multi-account-isolation.
日本語の概要は準備中です。原文の説明を表示しています。
GOMS cognitive modeling methodology for analyzing human-computer interaction task performance
日本語の概要は準備中です。原文の説明を表示しています。
Expert in drone systems, computer vision, and autonomous navigation. Specializes in flight control, SLAM, object detection, sensor fusion, and path planning. Activate on "drone", "UAV", "SLAM", "visual odometry", "PID control", "MAVLink", "Pixhawk", "path planning", "A*", "RRT", "EKF", "sensor fusion", "optical flow", "ByteTrack". NOT for domain-specific inspection tasks like fire detection, roof damage assessment, or thermal analysis (use drone-inspection-specialist), GPU shader optimization (use metal-shader-expert), or general image classification without drone context (use clip-aware-embeddings).
日本語の概要は準備中です。原文の説明を表示しています。
Expert-level computer networks covering OSI model, TCP/IP stack, routing protocols, transport layer, application protocols, network security, and software-defined networking.
日本語の概要は準備中です。原文の説明を表示しています。
Create and integrate the machine-level core of a new EMULATORS emulator, including Computer and Emulator derivatives, memory and port maps, chip ownership and wiring, clock coordination, model configuration, commands, formatters, executables, and Visual Studio projects. Use when scaffolding a machine namespace, adding a model or regional variant, assembling existing components into a computer, or making a new emulator build and start.
日本語の概要は準備中です。原文の説明を表示しています。
Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks. Triggers: "image analysis", "computer vision", "OCR", "object detection", "ImageAnalysisClient", "image caption".
日本語の概要は準備中です。原文の説明を表示しています。
Bindet das model-agnostische Computer-Use-Modul open-compute (Screenshot-Wahrnehmung, Windows-UIA-Elementklicks, sicherheitsgegatete Aktionen) fuer ALLE Agenten des Systems ein -- Claude Code, Codex und agy/Antigravity. Nutzen, wenn ein Dienst eine interaktive GUI- oder Browser-Aktion braucht, die kein reiner Text-/API-Weg loesen kann (Paradebeispiel: Tailscale-Reauth im Browser, sonstige Login-/Consent-Dialoge, Dialogfenster-Klicks). Standard ab 2026-08-02: open-compute statt Aufgeben oder Abwarten nutzen.
日本語の概要は準備中です。原文の説明を表示しています。
SOTA Computer Vision Expert (2026). Specialized in YOLO26, Segment Anything 3 (SAM 3), Vision Language Models, and real-time spatial analysis.
日本語の概要は準備中です。原文の説明を表示しています。
Drive the user's desktop in the background — clicking, typing, scrolling, dragging — without stealing the cursor, keyboard focus, or switching virtual desktops / Spaces. Cross-platform: macOS, Windows, Linux. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
日本語の概要は準備中です。原文の説明を表示しています。
Search arXiv physics, math, and computer science preprints using natural language queries. Powered by Valyu semantic search.
日本語の概要は準備中です。原文の説明を表示しています。
Orientation for custom (non-OpenAI) models running in the Codex app through the codex-router proxy. Explains that the app's native tools arrive as flattened codex_app__ and mcp__ names, that the router restores them so the app executes them, which companion skills to read before threads, browser, or computer-use work, and that a turn with no tool call ends the task. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, when codex_app__ or mcp__ tool names appear in the tool list, when a tool result just arrived and more work remains, or when thread, browser, or computer-use work is requested.
日本語の概要は準備中です。原文の説明を表示しています。
Utilizes native Rust/Node and Python-based computer-use-mcp servers to control macOS, Windows, and Linux desktops.
日本語の概要は準備中です。原文の説明を表示しています。
Drive the macOS desktop in the background — screenshots, mouse, keyboard, scroll, drag — without stealing the user's cursor, keyboard focus, or Space. Works with any tool-capable model. Load this skill whenever the `computer_use` tool is available.
日本語の概要は準備中です。原文の説明を表示しています。
Operate Orca-managed worktrees, folder contexts, terminals, repos, automations, artifacts, skill sharing, worktree comments, and Orca's embedded browser through the `orca` CLI. Use when the user says "$orca-cli", "Orca worktree", "child worktree", "spawn codex/claude in a worktree", "read/wait/send Orca terminal", "handoff" / "handover" / "give this to another agent", "Orca browser", "orca artifacts", or "share skills". Prefer it over raw git worktree, ad hoc PTYs, or Computer Use when Orca state is involved. Use Computer Use only for external windows or desktop UI that needs OS-level control, and Playwright or CDP for external pages.
日本語の概要は準備中です。原文の説明を表示しています。
OpenAI API (developers.openai.com) の Agents SDK (Python / TypeScript) リファレンス。 Agent, Runner, handoffs, guardrails, tracing、 built-in tools（web search, file search, computer use, code interpreter, image generation）、 function tools, vector stores, MCP (Model Context Protocol) connectors, background mode, multi-agent orchestration、 SandboxAgent（隔離実行環境）。
Claude Code (code.claude.com) の各サーフェス（Desktop / IDE / Web / モバイル / ターミナル UX）リファレンス。 Desktop app（launch.json, scheduled tasks, WSL, Linux, iOS simulator）、 VS Code / JetBrains / Chrome 拡張連携、claude.ai/code（Web）と mobile アプリ、 interactive mode, fullscreen, voice dictation, accessibility, computer use, artifacts。
Claude API (platform.claude.com) の tool use / Agent Skills / MCP リファレンス。 tool definition (JSON Schema), tool_use / tool_result, server tools（code execution, computer use, web search / web fetch, memory tool, tool search）, Agent Skills (SKILL.md), Skills API, MCP connector, mcp_servers, MCP tunnels, Tunnels API。