「learning」の検索結果

Generate scaffolds that gradually increase student choice, voice, and ownership within a learning task. Use when students depend heavily on teacher direction and need to develop autonomy.

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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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Audit a proposed assessment for construct validity, reliability, and alignment to learning objectives. Use when reviewing or quality-assuring assessments before deployment.

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。

Design a Concrete-Pictorial-Abstract learning sequence for a mathematical concept using manipulatives. Use when teaching maths through Singapore method or when students struggle with abstraction.

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Capture confidence ratings before and after a learning attempt to identify overconfidence and underconfidence patterns. Use when a student wants to understand how well they actually know something versus how well they think they know it.

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Analyse a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.

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Plan a unit using backwards design from desired outcomes through assessment evidence to learning activities. Use when starting a new unit or redesigning an existing one from standards.

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Document a recently solved problem or durable project vocabulary in docs/solutions/ or CONCEPTS.md. Use when capturing a learning after work.

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Socratic tutor that teaches codebase understanding through questioning, challenges, and active recall. Invoke with /learn-codebase when onboarding to a new codebase or deepening understanding of existing code. Maintains a learning journal that tracks mastery and schedules reviews.

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Self-Evolving Development Loop - Dynamic skill generation with learning and evolution

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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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Audit the reasoning of a document or argument (memo, proposal, investment analysis, board paper, article, the user's own draft): claims, evidence, unstated assumptions, logical gaps, fallacies, and what would falsify it, each anchored to quoted text. Use whenever the user wants reasoning examined, in any language ("audit this argument", "is this analysis sound", "poke holes in this proposal", "review the logic of my draft"; Vietnamese "phản biện giúp tôi", "soi lập luận này", "tài liệu này có lỗ hổng gì", "góp ý bản nháp của tôi"), even when they never say "critical thinking". Also use when the user drops a document and asks whether to trust or act on it, or asks to see their reasoning profile. Not for company-level strategic bets (strategy-board) or learning a topic through dialogue (socratic-questor).

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CASS Memory System - procedural memory for AI coding agents. Three-layer cognitive architecture with confidence decay, anti-pattern learning, cross-agent knowledge transfer, trauma guard safety system. Bun/TypeScript CLI.

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Aural skills development covering interval recognition, chord quality identification, rhythmic and melodic dictation, sight-singing, and progressive pedagogy. Covers the Kodaly method (hand signs, relative solmization, singing-first approach), interval identification strategies, chord quality and inversion recognition, harmonic dictation, solfege systems (fixed-do vs. movable-do), Gordon's Music Learning Theory, Suzuki method principles, and structured difficulty sequencing. Use when developing listening skills, practicing dictation, preparing sight-singing, or designing ear training curricula.

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Traditional Chinese internal arts — tai chi chuan and qigong — as transmitted by classical lineages (Chen, Yang, Wu, Wu/Hao, Sun for tai chi; Ba Duan Jin, Yi Jin Jing, Liu Zi Jue, Wu Qin Xi, and other named sets for qigong) with an emphasis on alignment, rooted stance, breath-posture-mind coordination (the three harmonies), and the traditional lineage voice that Dr. Yang Jwing-Ming's YMAA line preserves. Also covers the safety boundary between traditional practice and clinical "medical qigong" claims. Use for queries about tai chi form learning, qigong set selection, internal-arts pedagogy, and the practical question of where to start.

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Mindset theory, implicit theories of intelligence, and motivation research applied to learning. Covers fixed vs. growth mindset, the difference between process praise and ability praise, the "not yet" intervention, attribution patterns after failure, mindset critiques and the replication record, and the integration of mindset work with self-determination theory (autonomy, competence, relatedness). Use when a learner is stuck on motivation, disengaging after failure, or when a curriculum's feedback language is undermining persistence.

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Teaching home-economics skills as a durable practice across generations and capacity levels. Covers scaffolded task sequencing, the one-in-one-out rule, the apprenticeship model, learning by failure tolerance, family retros, the edible schoolyard framework, and the difference between teaching a recipe and teaching a kitchen. Use when designing a home-ec curriculum for children or adults, diagnosing why a taught skill is not being practiced, or building a household practice that survives the departure of the teacher.

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Technical writing, engineering specifications, reports, presentations, and documentation standards for engineering practice. Covers document types (requirements documents, test reports, design rationale, operations manuals), writing style, data visualization, engineering drawing standards, and oral presentation techniques. Use when writing engineering documents, presenting technical results, creating specifications, or learning documentation best practices.

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Computational thinking as a problem-solving discipline independent of programming languages. Covers the four pillars (decomposition, pattern recognition, abstraction, algorithm design), computational problem-solving methodology (understand, decompose, generalize, formalize, verify), abstraction levels (from hardware through user interface), modeling and simulation, automata and formal languages (DFA, NFA, regular expressions, context-free grammars, Turing machines), computational complexity classes (P, NP, NP-complete, undecidable), and constructionist pedagogy (learning by building, Logo, Scratch, physical computing). Use when approaching unfamiliar problems, teaching problem-solving strategies, analyzing problem complexity, or bridging between domain knowledge and computational solutions.

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Enforcement layer for chunk and story validation. Invokes the chunk-validator agent (haiku), enforces the verdict, routes failures to refine-chunk or test-fix, captures CLI learnings. The orchestrator invokes this skill — never the agent directly.

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Silently restructures the user's natural-language prompt into the format the model CURRENTLY running this skill handles best, then answers. On activation it identifies which model family is executing it (Claude, GPT, Gemini, Llama, DeepSeek, Mistral, Qwen, Grok, Perplexity, Kimi, GLM, Command, Nova, or Phi) and loads that one model's official strategy — so the optimization always matches the model that actually runs it. Activate with /prompt-refine. Use when users want better answers without learning prompt engineering.

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。

Schmidhuber's compression progress as intrinsic curiosity reward for learning 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。

Machine learning environment for automated theorem proving with Coq.

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。

Bengio's causal inference for AI: Interventional reasoning, counterfactuals, and System 2 deep learning. World models with causal structure.

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。