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概要と使いどころ
Guidance for distinctive, intentional visual design when building new UI or reshaping an existing one. Helps with aesthetic direction, typography, and making choices that don't read as templated defaults.
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。
Release pipeline for CLI, mobile, web, and server. Guides through version bumping, building, testing, publishing, and deploying. Replaces the old interactive release-it flow with a Claude Code-native experience. Use when user types /release or asks to release, publish, deploy, or ship any component.
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。
Orchestrate multi-agent swarms with agentic-flow for parallel task execution, dynamic topology, and intelligent coordination. Use when scaling beyond single agents, implementing complex workflows, or building distributed AI 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 semantic vector search with AgentDB for intelligent document retrieval, similarity matching, and context-aware querying. Use when building RAG systems, semantic search engines, or intelligent knowledge bases.
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。
Master advanced AgentDB features including QUIC synchronization, multi-database management, custom distance metrics, hybrid search, and distributed systems integration. Use when building distributed AI systems, multi-agent coordination, or advanced vector search applications.
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 and train AI learning plugins with AgentDB's 9 reinforcement learning algorithms. Includes Decision Transformer, Q-Learning, SARSA, Actor-Critic, and more. Use when building self-learning agents, implementing RL, or optimizing agent behavior through 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。
Implement persistent memory patterns for AI agents using AgentDB. Includes session memory, long-term storage, pattern learning, and context management. Use when building stateful agents, chat systems, or intelligent assistants.
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。
Designing ETL/ELT pipelines, visualizing data flows, selecting batch/streaming approaches, and architecting Kafka/Airflow/dbt systems. Use when building data pipelines or managing data quality.
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。
Authoring Storybook stories, component catalogs, and Visual Regression integration (CSF 3.0/Factories, Storybook 10 ESM-only, React Cosmos). Use when building a component catalog.
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。
Building rapid prototypes for frontend (UI components/pages) and backend (API mocks, simple servers). Use to validate new features or turn ideas into working demos. Working software over perfection.
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。
Defining and managing design tokens, applying token systems to existing codebases, building design system foundations. Use for spacing, color, typography, dark mode, cross-platform output.
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。
Constructing landing pages from a focused section to a premium multi-stage studio pipeline: structure, copy, conversion, responsive build, craft gates, and launch handoffs. Use when building or optimizing an LP, CTA, conversion flow, or premium launch surface.
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。
Generate AI design contract (AI-SPEC.md) for phases that involve building AI systems — framework selection, implementation guidance from official docs, and evaluation strategy
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。
Perform a read-only, evidence-first intake of an existing mini-program repository before planning or modifying code. Use when users ask to take over, understand, audit, resume, scope, or continue a WeChat or other mini-program project; when project facts may conflict with historical documents; or when an agent needs the framework, rules, Git state, build path, risks, unknowns, protected behavior, and change boundary. Produces a project fact map and handoff without changing code, installing dependencies, building artifacts, or claiming runtime, device, cloud, upload, acceptance, or release status.
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。
Builds a talk as a beatdeck - a fixed 1920x1080 React stage where one click is one beat, with authored motion, presenter view, overview, and an offline self-contained build. Covers turning source material (PPTX, Keynote/PDF export, Markdown, notes) into a content audit and a scene/beat map, choosing or applying a theme, writing scene layers, automatic beats, and verifying every beat with npm run verify. Use whenever the task is creating, porting, restyling, rehearsing or fixing a presentation, talk, keynote, slide deck or conference session that should feel like a live system rather than slides, or whenever the repo contains src/beatdeck/.
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 learning tasks built on Ubuntu philosophy emphasising collective knowledge-building and mutual responsibility. Use when fostering collaboration that values community over individual competition.
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 restorative practice protocol for addressing harm, rebuilding trust, and restoring relationships. Use when managing conflict, behavioural incidents, or relationship breakdown between students.
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 SEEDS regenerative inquiry cycle connecting place-based learning to ecological awareness for young learners. Use when building early childhood or primary inquiry around ecosystems and community.
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。
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。
Create an interactive digital worked example sequence with fading for online or blended delivery. Use when building e-learning modules, LMS content, or app-based 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。
A harness that makes Opus (or any Claude model) behave like Fable — it enforces seeing a task through to the end, with evidence and verification, as procedure. Use when starting a multi-step task (2+ sequential stories), long autonomous work, debugging or root-cause investigation, building render/executable artifacts (HTML, SVG, games, charts), or when the user says "fablize", "see it through", "verify as you go", "split into goals".
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。