「deep」の検索結果

Control local apps through Computer Use (the @oai/sky runtime) inside the Codex app. Use when the session uses a custom (non-OpenAI) model, for example deepseek-v4-flash or mimo-v2.5, and the user asks to control the computer, operate a desktop app's UI, use Safari or Chrome through computer use, click or type in an app, or take a screenshot of an app. Prefer purpose-built connectors, APIs, or CLIs when they exist.

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Expert en détection de contenu IA (watermarking, outils de détection, originalité, intégrité académique)

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Manage GitHub issues, PRs, branches, and repositories via OAuth (3LO). For reading issues or PR details, not for deep code analysis — delegate that to code-agent.

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Autonomously deep-scan entire codebase line-by-line, understand architecture and patterns, then systematically transform it to production-grade, corporate-level professional quality with optimizations

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You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.

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Guide for diagnosing and improving MSBuild project evaluation performance. Only activate in MSBuild/.NET build context. USE FOR: builds slow before any compilation starts, high evaluation time in binlog analysis, expensive glob patterns walking large directories (node_modules, .git, bin/obj), deep import chains (>20 levels), preprocessed output >10K lines indicating heavy evaluation, property functions with file I/O ($([System.IO.File]::ReadAllText(...))), multiple evaluations per project. Covers the 5 MSBuild evaluation phases, glob optimization via DefaultItemExcludes, import chain analysis with /pp preprocessing. DO NOT USE FOR: compilation-time slowness (use build-perf-diagnostics), incremental build issues (use incremental-build), non-MSBuild build systems. INVOKES: binlog MCP server tools (evaluations, evaluation_global_properties, evaluation_properties, imports, properties); falls back to dotnet msbuild -pp:full.xml for preprocessing, /clp:PerformanceSummary.

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You are an expert error analysis specialist with deep expertise in debugging distributed systems, analyzing production incidents, and implementing comprehensive observability solutions.

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。

Deep research with structured reports and charts. ONLY use when the user explicitly requests research/analysis, or needs data visualization with charts, or quantitative/comparative analysis across multiple sources. Do NOT use for simple questions or quick lookups.

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PyTorch deep learning development with transformers, diffusion models, and GPU optimization.

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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

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Generate two complementary onboarding documents that together give any engineer — from newcomer to principal — a complete understanding of a codebase. Use when user asks for onboarding docs or getting-started guides, user runs /deep-wiki, or user wants to help new team members understand a codebase.

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Orchestrator for the full academic research pipeline: research -> write -> integrity check -> review -> revise -> re-review -> re-revise -> final integrity check -> finalize. Coordinates deep-research, academic-paper, and academic-paper-reviewer into a seamless 10-stage workflow with mandatory integrity verification, two-stage peer review, and reproducible quality gates. Triggers on: academic pipeline, research to paper, full paper workflow, paper pipeline, end-to-end paper, research-to-publication, complete paper workflow.

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Comprehensive investigation of complex multi-domain narratives (technology, theology, policy, finance). Systematically decouples factual claims from speculation through iterative verification, multi-source comparison, and contradiction resolution. Use for investigations requiring institutional knowledge, historical context, and handling of conflicting incentives.

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Survey the whole tdmcp project and produce a prioritized list of NEW features it could implement — across artist controls, CLI/DX, AI/LLM integration, and TouchDesigner depth. Use whenever the user wants to brainstorm, discover, survey, list, or audit what features/tools/effects/controls/commands/prompts/capabilities tdmcp *could* add, asks 'what could we build / what's missing / what are the opportunities / ideas for the project', or wants a feature backlog or gap analysis. Also for follow-ups: re-run, refresh, update, re-survey, deepen, or re-prioritize the backlog, or survey just one surface (e.g. 'CLI ideas only'). This is the IDEATION harness — it produces a list to decide from; it does NOT build. When the user has already chosen a feature and wants it implemented/shipped/added, use tdmcp-pipeline instead.

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You are an expert in Prisma ORM with deep knowledge of schema design, migrations, query optimization, relations modeling, and database operations across PostgreSQL, MySQL, and SQLite.

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Run autonomous research tasks that plan, search, read, and synthesize information into comprehensive reports.

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Use when deep scan and ingest project files (.md, .json, agent.md, .openwiki) and past conversation logs into the local memB vector memory engine.

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要件が曖昧なとき、ソクラテス式に1問ずつ質問して要件を掘り下げる。十分に明確になったら discovery-council にハンドオフ。「要件が曖昧」「何を作ればいいか」「掘り下げて」「インタビューして」で発動。※ 完成した plan/design を詰問するなら grill、非対話で一括批判するなら critic。

Implement, review, or improve widgets, Live Activities, and controls using WidgetKit and ActivityKit. Use when building home screen, Lock Screen, or StandBy widgets with timeline providers; when creating interactive widgets with Button/Toggle and AppIntent actions; when adding Live Activities with Dynamic Island layouts (compact, minimal, expanded); when building Control Center widgets with ControlWidgetButton/ControlWidgetToggle; when configuring widget families, refresh budgets, deep links, push-based reloads, or Liquid Glass rendering; or when setting up widget extensions, App Groups, and entitlements.

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Implement SwiftUI navigation patterns including NavigationStack, NavigationSplitView, sheet presentation, tab-based navigation, and deep linking. Use when building push navigation, programmatic routing, multi-column layouts, modal sheets, tab bars, universal links, or custom URL scheme handling.

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When the user wants to monitor competitor apps on an ongoing basis — tracking metadata changes, keyword shifts, screenshot updates, rating trends, or new features. Use when the user mentions "competitor monitoring", "track competitors", "competitor alert", "competitor changed their title", "watch a competitor app", "competitor weekly report", "competitive intelligence", or "what changed in competitor's listing". For a one-time deep competitive analysis, see competitor-analysis. For market-wide chart movements, see market-movers.

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NotebookLM でディープリサーチ（Web 調査）を実行する。「NotebookLM リサーチ」「NotebookLM で調査」「NotebookLM ディープリサーチ」「NotebookLM で調べて」などで起動。notebooklm-mcp を使用。

Transform transcripts, conversations, or raw notes into shareable content — either multiple social posts OR a single long-form article. Learns style from user's published content. Handles transcripts of any length (tested up to 5500+ lines / 90+ min). Use when user: (1) provides transcript/conversation/recording and wants social content, (2) asks to "turn recording/notes into posts", (3) mentions generating content from recordings/notes, (4) needs multiple publishable pieces from single source, (5) asks to write an article from a transcript — e.g. "write an article/long-form", "organize into an article", "write up this session", "turn this talk into a blog post", "deep dive". Social mode outputs 2-15 ready-to-publish pieces. Article mode outputs a structured long-form piece (3000-10000+ chars) written section-by-section.

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Expert in building voice AI applications - from real-time voice agents to voice-enabled apps. Covers OpenAI Realtime API, Vapi for voice agents, Deepgram for transcription, ElevenLabs for synthesis, LiveKit for real-time infrastructure, and WebRTC fundamentals. Knows how to build low-latency, production-ready voice experiences. Use when: voice ai, voice agent, speech to text, text to speech, realtime voice.

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。