「understand」の検索結果

Research a US employment law topic across federal, state, and city jurisdictions and produce structured research notes with proper source attribution. Use this skill any time the user asks about US employment laws, regulations, or pending legislation - from a single jurisdiction question to a 50-state survey. Triggers include phrases like "research [employment law topic]", "what are the laws on [employment topic]", "state-by-state [employment topic]", "help me understand [topic] across the US", or any prompt that asks for a legal landscape overview before another deliverable. Always run this BEFORE the employment-law-dashboard skill if a dashboard is the eventual output. Output is a structured research note that distinguishes primary sources (statutes, regs, agency guidance) from secondary sources (law firm alerts, tracker orgs).

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Turn complex legal analysis into clear, commercially useful client-facing advice. Use this whenever the user has dense legal material — drafting, internal analysis, counsel notes, research memos, pleadings, a case update, or correspondence — and wants it converted into something a client can actually understand and act on. Trigger on phrases like 'explain this to the client', 'put this in plain English', 'translate this for a non-lawyer', 'turn this into client-facing advice', 'make this client-ready', 'draft a client update', or when the user shares legal analysis and asks 'what does this mean for them'. Also trigger when the user wants a board summary, litigation risk update, or call script derived from legal material. The skill preserves legal nuance, uncertainty, and risk rather than oversimplifying — it makes advice usable, not just simpler.

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Android APK decompiler that converts DEX bytecode to readable Java source code. Use when you need to decompile APK files, analyze app logic, search for vulnerabilities, find hardcoded credentials, or understand app behavior through readable source code.

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When you want multi-source, multi-step research on a topic — competitor research before a sales call, market research for a new business idea, positioning angles, due diligence on a partnership or podcast guest, tech decision research (which DB, which auth), or any "I need to actually understand X." Combines web search, URL fetch, agent-browser, last30days (Reddit/X/YouTube/HN/web recency), memory, and Notion. Outputs a structured brief with citations, contradictions, gaps, and recommended next steps. Archives every research run to ~/.config/makerskills/deep-research/archive/ so past work is searchable. Triggers on "/deep-research," "research X," "investigate X," "do a deep dive on X," "look into X," "what's actually happening with X," "due diligence on X," "validate this market." Differs from a one-shot web search: this is multi-pass with verification.

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Map a current event below the surface into patterns, structures, and mental models. Use when a class or team needs systemic understanding before action.

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Design a RULER emotional literacy sequence for recognising, understanding, labelling, expressing, and regulating emotions. Use when students struggle with emotional regulation, conflict, or anxiety.

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Design structured perspective-taking activities with anti-projection guardrails. Develops genuine understanding of complexity across history, social sciences, and literature — not performed empathy.

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Before any explanation or answer, require the learner to produce a free-recall attempt and confidence rating. Use when a student wants help understanding or reviewing a topic — this skill ensures the AI works from what the learner already knows.

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The learner teaches the concept to the AI, which plays a curious novice peer and identifies gaps through authentic questions. Use when the learner wants to test their understanding — teaching forces a different kind of organisation than studying.

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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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Map a scoped topic across three coexisting horizons — current system under strain (H1), preferred future (H3), and transition innovations sorted as H2+ or H2- — to understand change dynamics and identify responsible next actions.

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Require the learner to explain a concept in their own words before the AI evaluates or extends it. Ensures the AI works from the learner's understanding rather than providing an explanation from scratch.

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Generate a Socratic questioning sequence that develops conceptual understanding through guided inquiry. Use when facilitating philosophical discussions, concept exploration, or critical examination.

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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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Anchor a multidisciplinary unit in a real-world phenomenon that requires multiple subject lenses to understand. Use when designing integrated or phenomenon-based units across disciplines.

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Design a teacher think-aloud that models historical thinking strategies with a specific document. Use when planning explicit strategy instruction or when students follow a protocol without understanding the underlying reasoning.

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Synthesise completed KUD charts into a developmental progression matrix and per-competency narrative sections. Use when you need a programme-level view of how knowledge, understanding, and performance develop across bands.

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Design a learning sequence teaching prompt quality — comparing vague vs. refined prompts to show why specificity and context transform AI output. Use when students use AI without understanding why output quality varies.

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Classify curriculum content into Know, Understand, and Do categories to align teaching and assessment approaches. Use when planning units, writing objectives, or selecting assessment methods.

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Design deliberately flawed examples that develop error-detection skills and deepen understanding. Use when students make characteristic errors and need practice spotting mistakes.

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Authors or reviews Know/Understand/Do charts for competency-based learning targets across developmental bands. Handles seven input types from raw curriculum documents to existing LT sets. Routes to upstream skills when stronger inputs are available.

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Build a learning progression showing prerequisite-to-mastery steps for a target skill or understanding. Use when sequencing content, designing diagnostics, or mapping prerequisite gaps.

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Design metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.

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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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