1.6천건 · 관련도순
Launch the Understand Anything web dashboard to explore an existing codebase knowledge graph, a map of its structure and relationships. Open the current project or a specified directory and receive the local access URL.
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Analyze project code, configuration, and documentation to build a graph for interactive architecture exploration. It maps components and dependencies, supports Japanese descriptions, and can update the analysis based on changes since the previous analyzed commit.
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Ask questions about a codebase using a knowledge graph, a map of its components and relationships. Get explanations tied to specific files, functions, dependencies, and architectural layers, with warnings when the graph may be outdated.
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Analyze Git changes or pull requests against a knowledge graph, a map of code components and relationships. Identify modified files, nearby dependencies, affected architectural layers, and review risks, then save data for a dashboard visualization.
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Extract business areas, workflows, and process steps from a codebase and display them as an interactive horizontal flow graph. Use an existing knowledge graph, a map of project components, or run a lightweight scan of project files.
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Plan an OpenMAIC lesson or course series backward from lasting understanding and evidence of learning. Connect central questions, practical assessment tasks, and learning activities so students can apply concepts in new situations.
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Teach the user to deeply understand a change through interactive tutoring: restating understanding, drilling into why/what/how, and quizzing until mastery. The active counterpart to a one-shot explanation. Use when the user asks to "understand this change", "teach me this change", "help me understand what changed", "walk me through this change", "make sure I understand this", "quiz me on this", or "teach me what we did".
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Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
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Get a detailed explanation of a file, function, or module using an existing knowledge graph and its source code. It explains architectural roles, dependencies, callers, and data flow for readers who may not know the programming language.
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Test your understanding of a phase in AI Engineering from Scratch through eight multiple-choice questions. Receive a score, explanations for incorrect answers, and specific lessons to review before continuing your studies.
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Helps build Python workflows that extract information from documents, images, audio, and video using Azure AI Content Understanding. Covers document text, timed transcripts, video frames, and invoice fields for search and automated processing.
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Help junior developers and AI newcomers understand code through guided questions and progressive hints. Support debugging, concept learning, and critical review of AI-generated code so learners can explain their reasoning and work toward solutions themselves.
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Grounded in first principles, rigorously examine and refine a user's plan, task, decision, goal, strategy, proposal, or idea through structured, progressively deeper questioning, in order to bridge the gap between the User and the Agent. Use when the user explicitly requests grilling, challenge, pressure-testing, cross-examination, red-team review, pre-mortem analysis, or a decision audit. The goal is to uncover unclear objectives, hidden assumptions, contradictions, weak evidence, missing information, overlooked constraints, dependencies, risks, trade-offs, failure modes, and misalignment between intended outcomes and likely real-world results. Begin by establishing a shared understanding of the user's actual intent, goals, constraints, and success criteria. Ask focused, high-leverage questions rather than broad or repetitive ones. Adapt each question based on previous answers, probing deeper where uncertainty, unsupported assumptions, or strategic weaknesses remain. Regularly summarize the current understanding of the user's intent and position to confirm alignment, expose misunderstandings, and refine the problem or goal definition. Distinguish facts, assumptions, hypotheses, and unknowns. Challenge reasoning rigorously while remaining constructive, respectful, and solution-oriented. Continue until the reasoning is internally coherent, evidence-aware, constraint-conscious, risk-assessed, and translated into a clearer, more actionable goal, decision, or plan. Ultimately, achieving the super-alignment.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Azure AI Content Understanding SDK for Python workflow skill. Use this skill when the user needs Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.
한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.
Turn a structured LLM wiki into an interactive knowledge graph. It maps articles, topics, and sources, then adds entities and inferred relationships to help readers explore connections beyond the wiki’s explicit links.
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Turn the current conversation topic into an interactive lesson with spoken explanations and text diagrams in the terminal. Answer one question at each step, receive feedback, and check your understanding with a short quiz and recap.
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Turn AI-assisted coding work into optional, short learning exercises. Predict behavior, compare your approach with the implementation, or explain code in your own words, with feedback that helps clarify how the code works and why design choices were made.
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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".
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Expert knowledge for Azure Content Understanding in Foundry Tools development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when designing Content Understanding analyzers, RAG document flows, agentic workflows, REST/SDK calls, or Foundry deployments, and other Azure Content Understanding in Foundry Tools related development tasks. Not for Azure Speech in Foundry Tools (use azure-speech), Azure AI Search (use azure-cognitive-search), Azure AI Document Intelligence (use azure-document-intelligence), Azure AI Video Indexer (use azure-video-indexer).
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Performs deep AI-powered analysis and understanding of long-form videos (up to 3 hours, 10GB) using Volcengine LAS large language models. Video analysis, video comprehension, and video summarization — generates comprehensive video summaries, video recaps, chapter breakdowns, event timelines, key moments, and structured content indexing. Supports behavior detection, action detection, video annotation, video tagging, and video content recognition. Enables intelligent video question answering — ask questions about video content and get AI answers. Handles long meeting recordings, lecture videos, webinars, surveillance and security footage, movies, tutorials, and any long video that needs detailed understanding. Async processing with submit-poll workflow. Use this skill when the user wants to analyze or understand long videos (up to 3h/10GB) with LLM-based deep comprehension, summarize video content or generate recaps, extract chapters/key moments/timelines from videos, do video Q&A (ask questions about video content), detect actions/behaviors in videos, review meeting recordings/lectures/webinars, or get structured video descriptions.
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PROACTIVELY query the code graph database to understand relationships and impact of changes. Use this skill WHEN READING any file to understand context, when searching for files, when exploring the codebase, or when you need to understand what depends on a component. This is your primary tool for understanding code structure and avoiding breaking changes.
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Socratic questioning partner named Gadfly for deep learning through dialogue. Use this skill when the user wants to learn, understand, or explore a topic through questions rather than explanations. Trigger when the user says "teach me about...", "help me understand...", "I want to learn...", "ask me questions about...", "quiz me", "Socratic method", "Gadfly", or wants to deeply understand something through dialogue rather than passive reading. Also trigger when the user provides a text or document and wants to be questioned about it, or wants to check their own understanding of a concept.
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Teach a topic, video, article, codebase change, spec, or transcript until the user demonstrably understands it, using staged instruction, a running checklist doc, and quizzes. Use whenever the user says "teach me", "explain this in detail", "help me understand X", "/teach-me", "walk me through this", "I want to learn X", "understand this deeply", or pastes a YouTube URL, article, paper, PR, or spec and asks to understand it rather than summarize it. Also use when the user wants study notes or raw material from a source they are learning. Do NOT use for a quick factual lookup, a summary request, or when the user just wants the answer.
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Azure AI Content Understanding SDK for Python. Use for multimodal content extraction from documents, images, audio, and video. Triggers: "azure-ai-contentunderstanding", "ContentUnderstandingClient", "multimodal analysis", "document extraction", "video analysis", "audio transcription".
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