「understanding」の検索結果

Design a diagnostic hinge question that reveals whether students understand enough to move on. Use when planning key checkpoints mid-lesson during explicit or direct instruction.

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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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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 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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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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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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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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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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Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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Growth skills for indie Apple developers — user acquisition, analytics interpretation, press/media outreach, community building, and indie business operations. Also covers the stage-by-stage store-growth audit and the post-launch store-signals loop. Use when user asks about growing their app, auditing growth levers, understanding metrics, getting press coverage, or running an indie dev business.

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Expert knowledge for Microsoft Foundry Tools (aka Azure AI services, Azure Cognitive Services) development including troubleshooting, best practices, decision making, architecture & design patterns, limits & quotas, security, configuration, and integrations & coding patterns. Use when using Content Moderator, Content Safety, Content Understanding analyzers, REST/.NET APIs, or document extraction workloads, and other Microsoft Foundry Tools related development tasks. Not for Microsoft Foundry (use microsoft-foundry), Microsoft Foundry Classic (use microsoft-foundry-classic), Microsoft Foundry Local (use microsoft-foundry-local).

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Amazon Brand Analytics interpretation and strategic insights for Brand Registry owners. Decode Search Frequency Rank (SFR) data, analyze Market Basket patterns, interpret Item Comparison reports, and extract demographic insights to optimize product strategy and advertising spend. Works with Brand Analytics data from all Amazon marketplaces. Requires Brand Registry access. Use when: (1) analyzing Search Frequency Rank data for keyword opportunities, (2) interpreting Market Basket data for cross-sell and bundling, (3) understanding Item Comparison competitive positioning, (4) extracting customer demographic insights, (5) optimizing product portfolio based on customer behavior, (6) building data-driven advertising strategies.

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Deep need-clarification skill. Use only when the user explicitly invokes $task-clarifier. Once activated, keep asking until all three goals are met: the user fully understands their own needs, the AI fully understands the user's needs, and the user confirms the AI's understanding is correct. Do not intervene in task execution unless explicitly invoked.

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Reverse-engineer successful affiliate strategies from competitors. Use this skill when the user asks about spying on competitors, researching what other affiliates promote, analyzing competitor affiliate sites, understanding how top affiliates in a niche make money, or says "what programs does X promote", "how does [site] make money", "what affiliate strategy does this site use", "spy on competitor affiliates", "reverse engineer affiliate site", "copy what works in my niche", "who are the top affiliates in X niche", "what content gets traffic in my niche", "competitor affiliate analysis".

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5-dimension scorecard (0-100, grade A/B/C/F) for a scaffolded harness. Dimensions: Repo understanding (25%), Agent usefulness (25%), MCP safety (20%), Test coverage (15%), Publish readiness (15%). Emits a 6-field badges block (score + mcpRisk + 4 booleans) ready for the harness README. Exit 0 A/B, 1 C, 2 F.

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Plan, triage, and write academic rebuttals and review responses. Use when an AI research agent needs to handle conference, journal, workshop, or grant reviews; classify reviewer concerns; decide what evidence or experiments can change the decision; write an AC-facing executive summary; draft per-reviewer responses; correct factual misunderstandings without sounding hostile; handle novelty, baseline, method, experiment, clarity, and scope objections; or produce a final rebuttal checklist.

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Question-first approach to understanding user journeys. Load when starting a new journey design or when the discovery phase needs deepening.

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Use when the user needs to understand competitors — direct, indirect, and substitute tiers, positioning map, SWOT, ad and content benchmark, pricing comparison, and the market gap to attack, using SimilarWeb, Ahrefs, Semrush, and the Meta Ad Library. Trigger on 'competitor research', 'competitive analysis', 'what are competitors running', 'who else sells this', 'why do people pick them over us', 'benchmark us against the market'. Also use when the user sends a competitor link and asks for an opinion. Not for — understanding the customer rather than the rival, see `09-customer-insight-global`; turning difference into a statement, see `58-positioning-global`; paid targeting, see `51-audience-research-global`.

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Intelligently organizes your files and folders across your computer by understanding context, finding duplicates, suggesting better structures, and automating cleanup tasks. Reduces cognitive load and keeps your digital workspace tidy without manual effort.

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Use when working with this machine's local LLM setup (LM Studio + mlx-dspark) -- checking status, changing context window, diagnosing a reasoning hang or dead request, understanding RAM/speed tradeoffs, or wiring a new script to the local inference endpoint.

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Interview the user relentlessly about a plan or design until reaching shared understanding, resolving each branch of the decision tree. Use when user wants to stress-test a plan, get grilled on their design, or mentions "grill me".

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。

End-to-end academic paper writing workflow. Takes a research repository and produces a publication-ready LaTeX manuscript for top ML/AI conferences (NeurIPS, ICML, ICLR, ACL, AAAI, COLM). Covers repo understanding, structure planning, section drafting, citation management, anti-AI polishing, and conference formatting. Triggers on "write paper", "draft paper", "scholar writing", "paper writing workflow".

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Comprehensive understanding of the spec-kit methodology. Constitution-driven feature development with specify, plan, tasks, and implement phases.

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。