1.2万 件 ・ 関連度順
概要と使いどころ
Use when the user needs to design evaluation datasets, create test cases, stratify samples, generate adversarial examples, extract eval dimensions from traces/specs, or build a labeled evaluation set. Also use when the user mentions test data design, eval coverage, difficulty stratification, synthetic data generation for eval, or "how to create good evaluation data." Outputs datasets in OpenJudge-compatible format.
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。
Diagnose motivation problems in a task using self-determination theory and redesign for autonomy, competence, and relatedness. Use when students are disengaged, resistant, or going through the motions.
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 lesson that embeds PERMA wellbeing elements alongside academic learning objectives. Use when planning lessons that intentionally support both content mastery and student flourishing.
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。
Coordinates UDL and differentiation tools through a universal-first hierarchy: barrier removal before targeted differentiation before individualised accommodation. Use when planning accessible learning.
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。
Redesign a classroom as a prepared environment optimised for independent learning, calm transitions, and material access. Use when classroom layout hinders independence or self-directed 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。
Predicts access barriers in a learning task before delivery, given a learner variability profile. Distinguishes between barriers addressable through design and those requiring specialist 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 complete document-based history lesson using the Reading Like a Historian four-part structure. Use when planning a primary source inquiry lesson or converting a textbook lesson into document-based investigation.
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 a learning task for cognitive load problems and recommend specific design improvements. Use when tasks overwhelm students, instructions feel complex, or materials need simplifying.
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。
Map which elements of an assignment benefit from AI assistance vs. which AI use undermines. Use when redesigning tasks for AI-age classrooms or setting defensible AI use policies for specific assignments.
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。
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.
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 discipline-specific critical thinking tasks grounded in knowledge-contingent reasoning rather than generic skills. Use when embedding higher-order thinking into subject content.
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 scope and sequence showing vertical and horizontal curriculum coherence across a programme or year. Use when building new programmes, restructuring subjects, or ensuring progression.
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。
Map the epistemic structure of a subject to determine knowledge types and inform curriculum sequencing. Use when designing courses, restructuring programmes, or analysing knowledge architecture.
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。
Performance scorecard design, post-matter feedback collection, QBR preparation, and firm comparison for in-house legal ops teams evaluating outside counsel. Design a scorecard framework calibrated to your team's maturity level. Produce post-matter feedback forms for supervising attorneys to complete at matter close. Prepare a QBR pack with agenda, data summary, and talking points for structured business reviews. Produce a side-by-side firm comparison for panel decision-making. Trigger on: 'design a performance scorecard', 'how do we evaluate our firms', 'build us a scorecard', 'post-matter feedback form', 'feedback at matter close', 'collect feedback on the firm', 'QBR', 'quarterly business review', 'prep for the firm review', 'what should I raise with the partner', 'compare the firms', 'which firm is performing better', 'rank our panel', 'firm performance data', 'scorecard framework', 'evaluate outside counsel', 'annual firm review', 'how are our firms performing'.
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。
Panel structure design, firm selection criteria, right-sourcing analysis, and coverage gap assessment for in-house legal teams. Build panel frameworks from scratch or formalise existing informal arrangements. Define weighted selection criteria for firm evaluation. Run right-sourcing analysis (ABC tiering) to identify work sent to expensive providers that doesn't require that level of resource. Assess existing panels for practice area, geographic, and capability gaps. Trigger on: 'design our panel', 'how many firms do we need', 'panel structure', 'firm selection criteria', 'which firms should we use', 'right-sourcing', 'are we sending work to the right firms', 'panel gaps', 'too many firms', 'consolidate our panel', 'we need a firm for', 'preferred provider', 'panel tiers', 'do we need BigLaw for this', 'convergence', 'D&I panel targets', 'how do we choose firms'.
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。
Audits a legal document against legal design principles. Scores the document on six pillars: language patterns (bureaucratese, archaisms, passive voice, nominalization), readability (Flesch Reading Ease and language-specific equivalents), structure and navigation, hidden conditions (material obligations buried away from their headings), statutory duplication (restating the law instead of citing it), and visual hierarchy where layout is available. Assessment-only; does not rewrite the document. Works on English, French, and German documents, inferring the applicable jurisdiction from the document's language and textual signals: governing-law clause, statute citations, party addresses. Use when a user shares a legal document (contract, NDA, T&C, privacy policy, internal policy) asking how good the drafting is from a legal design perspective, whether it is readable, whether it contains buried clauses or restates the law, or how it scores on Flesch / LIX / Amstad / Kandel-Moles readability metrics.
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。
AFA design, scope-to-fee matching, negotiation preparation, health check, and scope dispute assessment for in-house legal ops teams managing outside counsel arrangements. Design fee structures (fixed, capped, collar, blended, phased, success). Assess whether scope supports a proposed AFA. Prepare commercial positions for fee negotiations. Review existing AFAs against delivery data. Assess OOS claims and build dispute response. Trigger on: 'structure the fee', 'move off hourly', 'design an AFA', 'does scope support a fixed fee?', 'negotiate the fee', 'renegotiate the arrangement', 'is our AFA holding?', 'fee health check', 'cap is being approached', 'firm is claiming OOS', 'is this in scope?', 'is this OOS?', 'AI should reduce the cost', 'blended rate vs fixed', 'collar arrangement', 'phased fees'.
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 the user wants to design, construct, or improve an offer — the thing they actually sell — including value framing, bonus stacking, guarantee design, scarcity/urgency, naming, and payment structure. Also use when the user mentions 'offer,' 'offer design,' 'build an offer,' 'grand slam offer,' 'irresistible offer,' 'value stack,' 'bonus stack,' 'guarantee,' 'risk reversal,' 'money-back guarantee,' 'scarcity,' 'urgency,' 'high-ticket offer,' 'productize a service,' 'naming an offer,' 'payment plan,' 'down-sell,' 'upsell offer,' or 'why isn't my offer converting.' Best for services, agencies, courses, coaching, info products, high-ticket B2B, and direct-response. If you run pure self-serve SaaS, read pricing first — tiers and packaging do more work there. For price level itself (tiers, freemium, value metric), see pricing. For the page that presents the offer, see copywriting. For the launch moment, see launch. For sales collateral, see sales-enablement.
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。
Expert blueprint for platformer games including precision movement (coyote time, jump buffering, variable jump height), game feel polish (squash/stretch, particle trails, camera shake), level design principles (difficulty curves, checkpoint placement), collectible systems (progression rewards), and accessibility options (assist mode, remappable controls). Based on Celeste/Hollow Knight design research. Trigger keywords: platformer, coyote_time, jump_buffer, game_feel, level_design, precision_movement.
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。
Expert patterns for multi-platform exports including export templates (Windows/Linux/macOS/Android/iOS/Web), command-line exports (headless mode), platform-specific settings (codesign, notarization, Android SDK), feature flags (OS.has_feature), CI/CD pipelines (GitHub Actions), and build optimization (size reduction, debug stripping). Use for release preparation or automated deployment. Trigger keywords: export_preset, export_template, headless_export, platform_specific, feature_flag, CI_CD, build_optimization, codesign, Android_SDK.
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。
Expert architectural standards for building scalable Godot GAMES (RPGs, Platformers, Shooters) using the Composition pattern (Entity-Component). Use when designing player controllers, NPCs, enemies, weapons, or complex gameplay systems. Enforces "Has-A" relationships for game entities. Trigger keywords: Entity-Component, ECS, Gameplay, Actors, NPCs, Enemies, Weapons, Hitboxes, Game Loop, Level Design.
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。
Expert patterns for adding multiplayer to single-player games including client-server architecture, authoritative server design, MultiplayerSynchronizer, lag compensation (client prediction, server reconciliation), input buffering, and anti-cheat measures. Use when retrofitting multiplayer, porting to online play, or designing networked gameplay. Trigger keywords: MultiplayerPeer, ENetMultiplayerPeer, SceneMultiplayer, MultiplayerSynchronizer, rpc, rpc_id, multiplayer_authority, client_prediction, server_reconciliation, lag_compensation, rollback.
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 when a new Godot system or feature has open design decisions — interrogates them in batched rounds, scope first, each question with a recommended answer, and records the answers before any design or code. Triggers on "grill me", "ask me first", "question me on the design", "what do I need to decide before building". Not for choosing between nodes or APIs, and not for bug fixes.
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。
Designing for visionOS — spatial layout and ergonomics (60pt eye targets, field-of-view placement, dynamic scale), eyes-and-hands input with hover rules, motion and visual comfort (the 0.2 Hz rule, vection, depth-cue agreement), immersion strategy, spatial sound, and the video-format decision guide. Use when designing or reviewing any visionOS app, window, volume, or immersive experience.
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。