42건 · 관련도순
Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
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Generate elaborative interrogation prompts that deepen encoding through targeted why and how questions. Use when students memorise without understanding or need deeper processing of content.
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Design a checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
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Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension. Use when user requests structural review or editorial review of structure
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Optimize existing CLAUDE.md, SKILL.md, agent definitions, and other AI-facing files for Claude comprehension and economy. Scope: optimization of existing content only — not upstream sync, not read-only auditing. Measures baseline metrics, delegates to the ai-doc-optimizer agent with file-type-specific context, runs independent verification via second agent, measures post-optimization metrics, and presents comprehensive before/after report. Supports iterative mode for large targets. Use when improving prompt effectiveness, reducing token waste, or rewriting instructions for LLM consumption. Invoke with /optimize-claude-md <file-or-directory>.
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Create user-centered, accessible interface copy (microcopy) for digital products including buttons, labels, error messages, notifications, forms, onboarding, empty states, success messages, and help text. Use when writing or editing any text that appears in apps, websites, or software interfaces, designing conversational flows, establishing voice and tone guidelines, auditing product content for consistency and usability, reviewing UI strings, or improving existing interface copy. Applies UX writing best practices based on four quality standards — purposeful, concise, conversational, and clear. Includes accessibility guidelines, research-backed benchmarks (sentence length, comprehension rates, reading levels), expanded error patterns, tone adaptation frameworks, and comprehensive reference materials.
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Use for debugging and system comprehension. Triggers: debug this, root cause, how does X work, reverse engineer, 왜 안 돼, 디버깅, 원인 분석, 로직 파악, 뜯어봐.
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Run Proto UI's dynamic comprehension assessment and derive an unsigned local task-fit result. Use when an Agent needs review calibration or a fresh autonomous task and review ceiling. Do not use a self-result as identity, permission, or acceptance evidence.
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Establish Proto UI repository state, live contributor authority, Agent comprehension scope, task risk, and authorization before work begins. Use at the start of a development session, before autonomous task selection, after a material repository update, or when permission or assessment state may have changed.
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Use when a feature or code change made by Claude is complete and the developer wants to confirm they actually understood it before shipping. Triggers on "handoff", "quiz me", "comprehension check", "did I understand this", "verify I read the code", "test my understanding", or after finishing a feature when the developer wants to take ownership of AI-written code. Also triggers on requests for an interactive or clickable quiz ("interactive mode", "let me answer with the mouse", "mode interactif", "je réponds à la souris"). Quizzes the developer on WHERE the logic lives (file/function) and WHY decisions were made (trade-offs, behavior, debugging), with the number of questions scaled to the size of the change. Multiple-choice questions are clickable by default (asked through `AskUserQuestion`) and fall back to typed answers. Points to file:line on wrong answers, allows one retry, then reveals the answer, and ends with a score plus the weak areas to re-read. Conducts the quiz in the developer's language.
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识别和管理 Loop 系统的认知风险: 自动化程度越高,你真正理解的部分越少。 当用户发现"仓库里有很多代码但我看不懂"、"loop 产出很多但不确定对不对"、或需要向团队警示 AI 使用风险时使用。 不适用于: 纯技术调试、或产出完全可客观验证的场景。 关键 trigger: "AI 生成的代码我看不懂"、"产出太多理解不过来"、"AI 使用风险"。
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"Apply Cognitive Load Theory to optimize instructional design by managing intrinsic, extraneous, and germane load within working memory limits. Use this skill when the user needs to diagnose why learners are overwhelmed, redesign training or documentation for better comprehension, evaluate UI/UX information architecture for cognitive burden, or when they ask 'why is this tutorial confusing', 'how to simplify complex instructions', or 'what causes information overload'.".
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Teacher mode — turn a session recap / article / docs / concept / piece of code / bug into an incremental mini-course with comprehension checks, quizzes, and an auto-saved summary note. Invoke /teach [topic or URL]. Triggers — "explain it like a teacher", "walk me through what we did", "turn this into a lesson", "quiz me", "teach me", "let's learn this together".
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Structural editor that proposes cuts, reorganization, and simplification while preserving comprehension. Use when user requests structural review or editorial review of structure
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Use this skill when a company needs rules for how staff use company systems, data, devices and AI tools, or asks "write our acceptable use policy", "AUP for employees", "can staff use ChatGPT at work", "AI usage policy", "BYOD rules", "remote work security rules" or "a policy people will actually read". Finds how people really work (devices, apps, AI tools, travel), puts the choices to the reader as decisions, drafts a short plain-language policy with concrete do and do not examples, and adds a comprehension check and an acknowledgement text for onboarding.
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Idiomatic programming style, patterns, and conventions for Python development. Trigger when: - Writing, refactoring, reviewing, or debugging Python code. - Files matching the pattern **/*.py (including requirements.txt, pyproject.toml, setup.py) are in the workspace or referenced. - Tasks involve: python, pytest, pip, uv, virtualenv, flake8, black, mypy. - Prompt contains keywords: python, py, pep8, typing, list comprehension, decorator, generator, asyncio, yield, poetry.
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Maxim motion-design + kinetic typography video — engineered for comprehension-to-conversion via AIDA + Hick's Law. Explainer-ad hybrid that makes complex value props land in under 15 seconds and converts via behavioral-architected CTA.
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Generates comprehension questions and language exercises based on the analyzed content to create a complete learning loop.
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