'analogie' 검색 결과

Build learning activities where learners explain an idea first, then improve their explanation through focused questions. Include plain language, checks on analogies, application to unfamiliar situations, and a record of remaining gaps and next goals.

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• Learn a concept by explaining it in your own words
• Turn existing materials into explanation-based activities
• Test explanations without relying on technical terms

Ideenschürf-Workflow für festgefahrene, schwere Probleme (Beweise, Forschungsfragen, hartnäckige Design-/Architekturprobleme): einen Ideenspeicher über acht Schürftechniken füllen (Wiedererkennung, Fern-Disziplin-Analogie, Alltags-Allegorie, Störgefühl/Ästhetik, Märchen-Reframing, Web-/Literatur-Recherche, Geschwisterprojekte, Bestandsquerlauf), dann gegen bereits Versuchtes filtern, eine Idee wählen und bis zur Substanz verfolgen. Nutze diesen Skill, wenn ein Problem trotz mehrerer Anläufe feststeckt, wenn „neue Ideen für X" gebraucht werden, bei „wir drehen uns im Kreis", oder für periodische Innovations-Läufe über ein Projekt. Für breites, freies Ideensammeln ohne festgefahrenes Problem stattdessen brainstorm (SCAMPER, Six Hats etc.).

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Define reusable reasoning and decision patterns for agents, embed them in executable workflows, and review their performance. Share successful approaches, including conditional decision trees, stepwise problem solving, analogies, and evaluation from multiple perspectives.

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• Define conditional rules for agent decisions
• Turn problem-solving steps into executable workflows
• Solve new problems using similar past cases

Test whether you understand a concept and whether evidence supports a decision using frameworks drawn from Richard Feynman’s work. Explore concrete examples, simple demonstrations, counterevidence, and the limits of analogies to expose gaps in reasoning.

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• Checking understanding beyond memorized terminology
• Finding counterevidence to a favored conclusion
• Reviewing processes that prioritize form over results

Use for first principles thinking, or when the user questions an inherited default: "why do we do it this way?", "is this cost really necessary?", "should we copy what competitors do?". Strips analogies and rebuilds from fundamentals. Skip for convention-following checklists.

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Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

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Skill for adding or updating system design guides tailored for college students and beginners, using simple analogies and no simulations.

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Explain code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"

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Defensive counterpart to federated-harbor-redteam: closes the smells that red team opens against The Federated Harbor whitepaper. Federation-specific defenses for cross-harbor primitives — mechanization commitments, scope hedges, cross-paper dependency formalization, pre-emptive analogies (CT, Macaroons, atomic swaps), and defense-in-depth framing (refuses vs prices). Use during a versioned red-vs-white round; pairs with federated-harbor-redteam. NOT for ad-hoc code review, NOT for the Anchor or Bonded papers (use whitehat-defense).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Introduce people to AI, agentic AI, and AI skills with appropriate pedagogy for any audience. Covers LLM analogies, progressive complexity paths, aha-moment demos, audience-tailored explanations, workshop design, and overcoming emotional barriers to AI adoption. Activate on 'explain AI', 'introduce AI', 'AI workshop', 'AI demo', 'teach AI', 'AI literacy', 'onboard to AI', 'AI evangelism', 'explain agentic', 'explain skills to', 'AI training session', 'AI onboarding'. NOT for teaching ML engineering (use ai-engineer), not for building AI features (use ai-engineer), not for prompt engineering (use prompt-engineer), not for building training data pipelines.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Explains code with visual diagrams and analogies. Use when explaining how code works, teaching about a codebase, or when the user asks "how does this work?"

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.