「understanding」の検索結果

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".

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Understand anti-reversing, obfuscation, and protection techniques encountered during software analysis. Use when analyzing protected binaries, bypassing anti-debugging for authorized analysis, or understanding software protection mechanisms.

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Process and generate multimedia content using Google Gemini API. Capabilities include analyze audio files (transcription with timestamps, summarization, speech understanding, music/sound analysis up to 9.5 hours), understand images (captioning, object detection, OCR, visual Q&A, segmentation), pr...

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This skill outlines methodologies for performing authorized penetration testing against AWS, Azure, and GCP cloud environments. It covers understanding the shared responsibility model for testing scope, leveraging cloud-specific attack tools like Pacu and ScoutSuite, exploiting IAM misconfigurations, testing for SSRF to cloud metadata services, and reporting findings aligned to MITRE ATT&CK Cloud matrix.

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Comprehensive data science workflow skill for analyzing tabular and time series data. Use when conducting exploratory data analysis, building predictive models, performing feature engineering, or generating data-driven insights. Supports end-to-end analysis from data understanding through model deployment, following industry best practices for regression, classification, time series forecasting, and statistical analysis.

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Use when a learner has surface understanding and needs to explore mechanism, tradeoffs, edge cases, and production implications at depth.

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 verifying whether the learner truly understands a concept through reasoning and application, not recall trivia.

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Systematically explore an unfamiliar codebase and build a reusable architecture summary. Use for repo onboarding, understanding legacy or inherited code, mapping architecture before a migration or refactor, tracing a bug or feature path, or answering "what does this project do?". Documentation-first, then data flow — ends in a concise technical architecture summary with file:line evidence, not a raw file dump.

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Compile deep repository evidence into artifact-bound correctness doctrine — authority/law/proof maps, owned invariants, failure archaeology, and the strongest durable destination for each piece of knowledge, plus an optional minimal repository-specific skill portfolio. Use when the user wants both deep codebase understanding AND durable doctrine, knowledge routing, or repo-specific skill recommendations. Research discoverable facts before asking. Read-only. Not for quick onboarding, one isolated invariant, ordinary implementation, generic review, or direct skill creation.

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Interview the user relentlessly about a plan or design until reaching shared understanding. Use when fleshing out an idea, stress-testing a design, or when the user says "grill me".

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Crystallize shared understanding into a reviewable spec, or update an existing one. Use when the problem needs a written specification, when assumptions or decisions have changed, or when the user says 'write a spec'.

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Builds a DRAFT quiz from the training content the user supplies (slide deck, manual, e-learning script, session transcript, procedure): numbered questions with the correct answer, distractors drawn from the content itself, an explanation quoting the source passage with its location, a difficulty tag (Recall, Apply, Analyse) with its basis, and a coverage table showing which sections and objectives each question tests and which are left uncovered. Use when the user asks to "build a quiz from this deck", "write knowledge check questions for this module", "create an assessment from this training", "test understanding of this procedure" or "which sections does this quiz cover". Do not use for a reader-facing question and answer page built from documents, use faq-builder instead; for writing the procedure itself from notes or a walkthrough, use sop-drafter. Drafts for human review; never approves, authorises or signs off.

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Navigate the user through reading code stop by stop to pay down understanding debt left by AI tooling. Explicit invocation only.

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。

Quiz the user on a plan, implementation, or codebase to pay down understanding debt left by AI tooling. Explicit invocation only.

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。

Tutor the user step by step on a plan, implementation, codebase, or topic to pay down understanding debt left by AI tooling. Explicit invocation only.

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Build full-paper Chinese-English side-by-side, figure/table-aware, source-grounded Markdown readers for journal or conference papers from PDF, DOI, arXiv, publisher HTML, or pasted text. Use whenever the user asks to translate or read a paper, make 中英文对照/原文对照/全文翻译解读, extract figures or tables into the right positions, preserve figure/table placement near relevant prose, or keep exact source anchors for every block. This skill must not degrade into a summary-only output unless the user explicitly asks for a summary. Also trigger on general paper-reading and translation requests even without the word "Nature", such as reading/translating an academic paper, literature reading, understanding a paper, and Chinese phrasings like 读论文、精读论文、论文翻译、文献翻译、文献阅读、学术阅读、帮我读这篇文章、翻译这篇paper.

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Use when building custom agent backends, implementing the AG-UI protocol, debugging streaming issues, or understanding how agents communicate with frontends. Covers event types, SSE transport, AbstractAgent/HttpAgent patterns, state synchronization, tool calls, and human-in-the-loop flows.

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Teach a micro-task in the style of the Feynman Technique. Start with a small concrete observation or action, explain the underlying concept from what the learner sees, then walk the learner step-by-step through the task. Before marking the micro-task done, require the learner to explain the underlying concept back in their own words; correct any misunderstanding before advancing.

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Master binary analysis patterns including disassembly, decompilation, control flow analysis, and code pattern recognition. Use when analyzing executables, understanding compiled code, or performing...

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Browser automation powers web testing, scraping, and AI agent interactions. The difference between a flaky script and a reliable system comes down to understanding selectors, waiting strategies, an...

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Azure AI Vision Image Analysis SDK for captions, tags, objects, OCR, people detection, and smart cropping. Use for computer vision and image understanding tasks.

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。

Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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。

Shaping the Safety Boundaries: Understanding and … — Vor einem Tag · Abstract Jailbreaking in Large

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Artificial intelligence for modeling and understanding extreme weather ... — 24. Feb. 2025 · This re

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。