「mastery」の検索結果

Bloom's Taxonomy of Educational Objectives and mastery learning as practical instructional design tools. Covers the six cognitive levels (remember, understand, apply, analyze, evaluate, create), the revised 2001 taxonomy, the knowledge dimension, mastery learning loops, formative vs. summative assessment, and the writing of learning objectives that are both specific and verifiable. Use when designing lessons, writing objectives, building assessments, or diagnosing why a learner is stuck at one cognitive level.

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Design motivation systems using Autonomy, Mastery, and Purpose (AMP) for products and teams. Use when the user mentions "intrinsic motivation", "gamification isnt working", "rewards arent working", "autonomy", "mastery", "purpose-driven", "my team is disengaged", or "how do I motivate people". Also trigger when designing onboarding progression, fixing broken gamification, or building team structures that sustain high performance. Covers why carrot-and-stick fails and how to build progress systems. For habit-forming product loops, see hooked-ux. For retention behavior design, see improve-retention.

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Use when the user asks to "optimize CLAUDE.md", "create a new skill", "write a custom agent", "configure hooks", "manage context window", "set up MCP servers", "scaffold a skill package", "analyze token budget", "create subagents", "configure permissions", "set up worktrees", or "integrate Claude Code with editors". Covers Claude Code CLI mastery, skill authoring, context engineering, hooks automation, subagent creation, and development workflow optimization.

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Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.

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Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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Expert blueprint for educational games including gamification loops (learn/apply/feedback/adapt), progress tracking (student profiles, mastery %), adaptive difficulty (target 70% success rate), spaced repetition, curriculum trees (prerequisite system), and visual feedback (confetti, XP bars). Use for learning apps, training simulations, or edutainment. Trigger keywords: educational_game, gamification, adaptive_difficulty, spaced_repetition, student_profile, curriculum_tree, mastery_tracking.

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Train strict project ownership from repo-local docs/ai memory and central LLM Wiki project entities. Use when Codex or Claude Code should initialize a learning question bank, quiz or grade the user, run code tracing or failure drills, record misconceptions, schedule spaced repetition, or generate Markdown/JSON mastery dashboards without replacing llm-wiki or ai-project-memory.

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Teach any topic deeply from first principles and build evidence-based paths toward exceptional capability. Use when a user asks to understand, explain, learn, or deep-dive into a topic; asks why or how something works, why it matters, how alternatives compare, or what different perspectives reveal; requests current paradigms, a complete roadmap or curriculum, deliberate practice and feedback, or a top-1%-level mastery plan. Answer explanation questions completely before offering a curriculum; route explicit mastery goals to an adaptive practice and proof-of-capability system.

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Design a mastery experience sequence that systematically builds student confidence in a skill they avoid. Use when students say 'I can't do this', avoid tasks, or show learned helplessness.

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。

Map knowledge components and skill hierarchies for a cognitive tutoring system or adaptive learning platform. Use when designing intelligent tutoring software or skill-based mastery systems.

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。

Interactive training for the GitHub Copilot CLI. Guided lessons, quizzes, scenario challenges, and a full reference covering slash commands, shortcuts, modes, agents, skills, MCP, and configuration. Say "cliexpert" to start.

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$10K/hr级X/Twitter运营导师。基于Nicolas Cole、Dickie Bush、Sahil Bloom、Justin Welsh、 Dan Koe、Alex Hormozi六位顶级创作者的方法论 + X开源算法深度分析 + AI/科技赛道专精策略， 提炼6个核心心智模型、10条决策启发式、完整的选题-写作-增长操作手册。 通用方法论为底座，AI/科技赛道为专精。 当用户提到「X运营」「推特」「Twitter」「怎么写推文」「怎么涨粉」「X策略」「推特选题」「tweet」「thread」「X算法」时使用。 即使用户只是说「这条推文怎么写」「帮我想个X内容」「推特增长」「发推」「write a tweet」「X account」「grow on X」也应触发。

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Board meeting preparation for the adversarial scenario, not the friendly one. Forces numbers-cold mastery, anticipates hard questions, builds a narrative that acknowledges weakness without losing the room. Use when preparing for a board meeting, an investor update, fundraising presentation, or any high-stakes adversarial review where every number must live in your head not just on a slide.

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Next.js 14+ App Router patterns including RSC, ISR, middleware, parallel routes, and data fetching

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Test-driven development workflow with Red-Green-Refactor cycle across languages

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Navigate difficult conversations and deliver constructive feedback using structured frameworks. Covers the Preparation-Delivery-Follow-up model and Situation-Behavior-Impact (SBI) feedback technique. Use when preparing for difficult conversations, giving feedback, or managing conflicts.

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Builds questions and assessments that measure what they claim — matching item type to the thing being assessed, writing distractors that diagnose, and telling a question that measures learning from one that measures reading speed or test-taking. Use this to write or review assessment items, design a unit check or diagnostic, work out why scores do not match classroom performance, or decide how much evidence a claim about mastery actually needs.

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Run /develop to build a feature, UI or backend, from an approved design, a page, component, API, service, or data slice. If something load bearing is undecided and no spec records it, it stops and routes you to /architect; otherwise it reads the spec plus AGENTS.md, builds, and advances the scope.

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Run /document `pr` | `changelog` | `release-note` | `postmortem` (or let it ask) to write the human facing prose about a change. Drafts from the real commits and diff, writing to the right place. Does not write code, tests, or specs.

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Run /test to write a test suite for code you just built or changed, after implementing a feature, route, or fix. Targets uncommitted changes automatically, reads test preferences.json for your framework (asks and saves it if absent), and picks the right strategy per file: happy path, edge cases, error states, accessibility.

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Run /sync as the last step after a change is complete, around merge, to keep durable knowledge current. Updates root and nested AGENTS.md, reconciles the scope from repo evidence, and flags specs the change made stale. Surgical edits only: it adds lines, and rewrites single lines it owns. Never a whole section, never curated prose.

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Run /scope to turn a product idea into a living, coarse scope in docs/scope/ and keep it current: plan a new product, plan the next slice, enroll one named feature, or run with no argument to reconcile after shipping and queue what is next. Seeds WHAT to build; /architect designs, /develop builds.

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Run /audit on a greenfield project, an existing codebase with missing docs, or one area (/audit src/auth) to bootstrap the project's AI context, the AGENTS.md files every later skill reads. Writes tool agnostic AGENTS.md plus thin CLAUDE.md pointers, adding only what is missing; never overwrites curated content.

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Confirm a change before merge. `/check verify` drives the real app to prove behavior against the spec (every acceptance criterion met, every surface built). `/check review` runs a senior code review on a fresh model, one that did not write the code. Verify after /develop, review before a PR. Writes to docs/reviews/, never edits code.

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