4,783 件 ・ 関連度順
概要と使いどころ
Design a focused lesson observation protocol with specific look-fors and evidence collection methods. Use when planning peer observations, coaching visits, or developmental classroom visits.
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 multi-informant assessment approaches for dispositional competencies like curiosity or resilience. Use when assessing character strengths or competencies that written tests cannot capture.
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。
Select the right formative assessment technique for a specific learning moment, purpose, and age group. Use when choosing how to check understanding during, between, or after lessons.
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 checking-for-understanding protocol with specific techniques for each lesson stage. Use when planning systematic comprehension checks during explicit or direct instruction.
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 an adaptive assessment loop where each student response triggers the next instructional move. Use when building technology-enhanced formative assessment cycles.
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When the user wants to edit, review, or improve existing marketing copy, or refresh outdated content. Also use when the user mentions 'edit this copy,' 'review my copy,' 'copy feedback,' 'proofread,' 'polish this,' 'make this better,' 'copy sweep,' 'tighten this up,' 'this reads awkwardly,' 'clean up this text,' 'too wordy,' 'sharpen the messaging,' 'refresh this content,' 'update this page,' 'this content is outdated,' or 'content audit.' Use this when the user already has copy and wants it improved or refreshed rather than rewritten from scratch. For writing new copy, see copywriting.
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 optimize, improve, or increase conversions on any marketing page or form — including homepage, landing pages, pricing pages, feature pages, lead capture forms, or contact forms. Also use when the user says 'CRO,' 'conversion rate optimization,' 'this page isn't converting,' 'improve conversions,' 'why isn't this page working,' 'my landing page sucks,' 'form abandonment,' 'nobody's converting,' 'low conversion rate,' or 'this page needs work.' Use this even if the user just shares a URL and asks for feedback. For signup/registration flows, see signup. For post-signup activation, see onboarding. For popups/modals, see popups.
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。
Turn a lawyer's bounded expertise, workflow, review standard, or legal judgment pattern into a safe, inspectable legal AI skill. Use when a user provides legal memos, comments, checklists, negotiation notes, review habits, compliance playbooks, or professional feedback and wants a reusable Lawve-style skill. Do not impersonate the lawyer or claim to reproduce a person; distill only authorized, source-bounded capability.
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。
Practitioner skill for advising on EU Regulation 2023/2854 (Data Act). Covers Chapters II-VII (IoT data access, mandatory B2B sharing, unfair contract terms, public-sector exceptional need, cloud switching, third-country governmental access) and Chapter VIII (interoperability and smart contracts, gate-only). Use when the user asks about Data Act rights or obligations, drafts a Data Act notice or letter, reviews a data-sharing or cloud-switching contract under the Data Act, runs a Data Act gap analysis, or asks how the Data Act interacts with GDPR, the DMA, the Trade Secrets Directive, or sectoral law. Triggers include "Data Act", "Datengesetz", "Regulation (EU) 2023/2854", "Art. 4(1) request", "Art. 5(1) third-party request", "trade-secret handbrake", "cloud switching obligations", "Chapter VI", "Ch V exceptional need", and references to specific Data Act articles or recitals.
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。
EU Data Act (Regulation (EU) 2023/2854) skill for lawyers. Use when the user asks about Data Act classification, drafting, lookup, analysis, or audit. Triggers include "Data Act", "Regulation 2023/2854", "connected product", "related service", "data processing service", "DPS switching", "Article 3(2) pre-contract", "Article 25 contract", "trade-secret handbrake", "international government access", "Chapter VI cloud switching", "Article 50 timeline", "FAQ Q22a", "data holder", "exportable data", "functional equivalence", "Art. 4(10) competing product", and similar EU Data Act phrases. The skill produces lawyer-style Word output and cites verbatim from bundled regulation and FAQ source texts.
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。
EU AI Act Knowledge Engine — authoritative regulatory Q&A grounded in 70 official EU source documents (including the 2026 Commission draft guidelines on Art. 6 high-risk classification — general principles, Annex I, Annex III). Answers any question about the AI Act with article-level citations from the full regulation text, Commission guidelines, EDPB/EDPS opinions, codes of practice, harmonised standards, FRIA guides, incident templates, and sector-specific guidance. This skill should be used when the user asks about the "EU AI Act", "AI Act regulation", "Regulation 2024/1689", "KI-Verordnung", asks to "explain an AI Act article", "what does Article X say", "AI Act requirements", "AI Act penalties", "AI Act timeline", "GPAI obligations", "high-risk AI", "prohibited AI practices", "AI Act and GDPR", "fundamental rights impact assessment", "AI Act standards", "AI Act national implementation", "AI literacy", "serious incident reporting", "Digital Omnibus", "AI regulatory sandbox", or any question about EU artificial intelligence regulation. Also triggers on German terms: "KI-Gesetz", "KI-Verordnung", "Hochrisiko-KI", "KI-Kompetenz", "Grundrechte-Folgenabschaetzung", "GPAI-Verhaltenskodex". For assessment workflows that produce a classification decision rather than a knowledge answer, offer to run a structured risk-tier classification in-session.
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 you want to capture into, compile, query, lint, or connect your personal Second Brain. Wraps the Karpathy LLM Wiki schema (Obsidian or any markdown vault) — raw/ (unprocessed sources), wiki/ (AI-compiled interlinked topic pages), outputs/ (generated artifacts). Tool-agnostic in design but defaults to a vault at ${SECOND_BRAIN_VAULT:-$HOME/Documents/SecondBrain}/. Six modes — capture (drop something into raw/), compile (process unprocessed raw files into wiki pages, update INDEX.md), query (answer a question from the wiki, save to outputs/), lint (orphans / contradictions / stale / unprocessed raw / topic gaps), connect (suggest new wikilinks between pages), search (quick lookup). Triggers on "/second-brain," "/sb," "capture this," "save this to my brain," "compile the wiki," "process raw notes," "query my wiki," "ask my brain," "lint the wiki," "find connections," "search my notes." Complements deep-research (external corpus) — this is the internal corpus.
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 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.
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 procedural content generation (dungeons, terrain, loot, levels) using FastNoiseLite, random walks, BSP trees, Wave Function Collapse, and seeded randomization. Use when creating roguelikes, sandbox games, or dynamic content. Keywords procedural, generation, FastNoiseLite, Perlin noise, BSP, drunkard walk, Wave Function Collapse, seeding.
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 GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.
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 Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
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 puzzle games including undo systems (Command pattern for state reversal), grid-based logic (Sokoban-style mechanics), non-verbal tutorials (teach through level design), win condition checking, state management, and visual feedback (instant confirmation of valid moves). Use for logic puzzles, physics puzzles, or match-3 games. Trigger keywords: puzzle_game, undo_system, command_pattern, grid_logic, non_verbal_tutorial, state_management.
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 open world games including chunk-based streaming (load/unload regions dynamically), floating origin (prevent precision jitter beyond 5000 units), HLOD (hierarchical LOD for distant meshes), persistent state (track entity changes across unloaded chunks), POI discovery systems (compass, markers), and threaded loading (prevent stutters). Use for RPGs, sandboxes, or exploration games. Trigger keywords: open_world, chunk_streaming, floating_origin, HLOD, persistent_state, POI_discovery, threaded_loading.
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 debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.
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 GPU particle systems (explosions, magic effects, weather, trails) using GPUParticles2D/3D, ParticleProcessMaterial, gradients, sub-emitters, and custom shaders. Use when creating VFX, environmental effects, or visual feedback. Keywords GPUParticles2D, ParticleProcessMaterial, emission_shape, color_ramp, sub_emitter, one_shot.
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 Godot 3D physics (Jolt/PhysX), including Ragdolls, PhysicalBones, Joint3D constraints, RayCasting optimizations, and collision layers. Use for rigid body simulations, character physics, or complex interactions. Trigger keywords: RigidBody3D, PhysicalBone3D, Jolt, Ragdoll, Skeleton3D, Joint3D, PinJoint3D, HingeJoint3D, Generic6DOFJoint3D, RayCast3D, PhysicsDirectSpaceState3D.
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 procedural content generation (dungeons, terrain, loot, levels) using FastNoiseLite, random walks, BSP trees, Wave Function Collapse, and seeded randomization. Use when creating roguelikes, sandbox games, or dynamic content. Keywords procedural, generation, FastNoiseLite, Perlin noise, BSP, drunkard walk, Wave Function Collapse, seeding.
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 debugging and profiling for leaks, GPU/Visual Profiler, headless CI QA, orphan nodes, thread-safe logs, and custom Debugger monitors — not print/breakpoint tutorials. Trigger on OBJECT_ORPHAN_NODE_COUNT, ObjectDB growth, Visual Profiler GPU spikes, flaky headless exits, or remote device consoles. Keywords: orphan nodes, Performance.get_monitor, Visual Profiler, Time.get_ticks_usec, EditorDebuggerPlugin, headless QA, push_error, backtrace.
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。