307 件 ・ 関連度順
概要と使いどころ
Generate scaffolds supporting student self-regulation across planning, monitoring, and evaluation phases. Use when students struggle to manage their own learning during extended or independent tasks.
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。
Build a library of metacognitive prompts targeting planning, monitoring, or evaluation for a specific task. Use when developing students' thinking-about-thinking during independent work.
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。
Review the week using accumulated session evidence — retrieval rates, hint depths, calibration accuracy, transfer and unassisted results. The learner identifies patterns and sets a strategy goal. Use weekly or after a multi-session period.
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。
Select evidence-based study strategies matched to material type, learning goal, and student habits. Use when advising students on revision techniques, homework, or independent study approaches.
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。
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.
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。
When a learner gets something wrong or feels stuck, require them to diagnose the problem before receiving help. Ensures help targets the actual cognitive breakdown, not just the surface error.
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。
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.
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。
Wrap a learning session in a plan → monitor → reflect cycle. Use at the start of any substantial study session to set goals, mid-session to check strategy, and at session end to consolidate what changed. Builds self-regulated learning as a habit.
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。
Select and sequence reading comprehension strategies matched to a specific text's challenges and demands. Use when students struggle with a text or need targeted reading support.
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 Montessori three-period lesson for introducing concepts through concrete materials and naming. Use when teaching vocabulary, classifications, or concepts through hands-on materials.
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。
Analyse existing written feedback for quality, specificity, actionability, and impact on student learning. Use when reviewing teacher or peer feedback to improve feedback practices.
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 metacognitive checkpoints that prevent AI-assisted learning from bypassing genuine understanding. Use when students use AI tools and may overestimate their own comprehension.
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 Funhouse Mirror activity where students use their own domain expertise to detect AI distortions, omissions, and overconfidence. Use when students know a subject well enough to evaluate AI claims about it.
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。
Create self-explanation prompts that deepen understanding of worked examples, texts, or diagrams. Use when students read material passively without engaging with underlying principles.
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。
Build accrual schedules for A-share companies tracking revenue recognition, expense accruals, and working capital timing. Adapted from the original accrual-schedule skill for CAS accounting and Chinese business practices. Triggers on "A股应计项目", "应计计提", "accrual schedule China", "应计账款分析", or "working capital accruals [company]".
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。
Systematic stock screening and investment idea sourcing for A-share markets. Combines quantitative screens, thematic research, and pattern recognition to surface new long and short ideas. Adapted from the original idea-generation skill for Chinese market conventions and data sources. Triggers on "A股选股", "股票筛选", "寻找机会", "stock screen China", "A-share ideas", "find ideas for Chinese stocks", or "screen for A-share opportunities".
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。
Build accrual schedules for A-share fund administration. Tracks revenue recognition, expense accruals, and working capital timing for fund accounting. Adapted from the original accrual-schedule skill for fund accounting standards. Triggers on "基金应计项目", "应计计提基金", "accrual schedule fund", "NAV accruals", or "accruals [fund]".
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。
Implement ReasoningBank adaptive learning with AgentDB's 150x faster vector database. Includes trajectory tracking, verdict judgment, memory distillation, and pattern recognition. Use when building self-learning agents, optimizing decision-making, or implementing experience replay 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。
Implement adaptive learning with ReasoningBank for pattern recognition, strategy optimization, and continuous improvement. Use when building self-learning agents, optimizing workflows, or implementing meta-cognitive 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。
Performs unbiased cell type recognition from single-cell RNA sequencing data, by leveraging reference transcriptomic datasets of pure cell types to infer the cell of origin of each single cell independently.
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。
Integrate your app with iOS Visual Intelligence for camera-based search and object recognition. Use when adding visual search capabilities.
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。
Spot patterns appearing in 3+ domains to find universal principles
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。
Transform a user-uploaded photograph into one horizontal collectible memory card with a large quiet editorial illustration, six integrated journaling-sticker motifs, three small English keywords beneath the left illustration, tactile paper grain, and deliberately clumsy flat color fields. Use when Codex needs to P图, edit, redraw, stylize, or turn a travel, street, landscape, lifestyle, portrait, or pet photo into a minimalist postcard-like card or sticker-card in this visual language. Preserve recognition through one same-medium identification anchor and optionally one exact place-defining landmark text shown once; never retain an unintended photorealistic patch, signature, or watermark.
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。
Use for reverse engineering stripped Go and Rust binaries including runtime recognition, pclntab/moduel data recovery, panic strings, and idiomatic decompilation recovery.
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。