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概要と使いどころ
Define actionable principles that resolve trade-offs when the team disagrees. Use when the same decisions keep getting relitigated. For a single project's framing, use `design-brief`.
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Read financials, map competitive landscapes, and argue design decisions in the language of value. Use when defending design to commercial stakeholders. For the live negotiation itself, use `design-negotiation` (designer-toolkit).
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。
Write a project brief — problem space, constraints, audience, and success criteria. Use at kickoff for one specific project. For long-horizon aspiration use `north-star-vision`; for reusable decision rules use `design-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。
Define UX metrics and KPIs that connect design decisions to measurable outcomes. Use when choosing what to measure. For presenting the results afterwards, use `design-impact-reporting` (design-ops).
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。
Choose prototype fidelity and method to match the design question and the decision at stake. Use before building a prototype. For what to test once it exists, use `test-scenario`.
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。
Diagram screen-level paths, decision points, and branch logic. Use when specifying how a feature is traversed. For the emotional end-to-end arc, use `journey-map` (design-research).
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 research-grounded personas with goals, frustrations, and behavioural patterns. Use when decisions need a consistent user reference. For one session's emotional snapshot use `empathy-map`; for motivation framing use `jobs-to-be-done`.
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 functional, emotional, and social jobs with outcome expectations. Use when reframing decisions around motivation rather than features. For who the user is, use `user-persona`.
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Structure a design presentation for a specific audience and decision. Use when presenting internally. For a portfolio narrative use `case-study`; for the written argument use `design-rationale`.
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。
Apply Hick's Law — decision time grows with the number of simultaneous choices. Use when a screen offers too many options at once. For how many items survive in memory afterwards, use `millers-law`.
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。
Stress-test a plan by walking its decision tree one question at a time. Use when the user wants to pressure-test a design before implementation.
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 the project's shared language and bounded contexts before writing code, so names stay consistent and the agent stops paraphrasing domain concepts. Produces a CONTEXT.md glossary and decision records. Use at the start of a project or feature, or when the codebase and the people describing it speak different languages.
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。
Score every decision point with a Thoroughness Rating (1-10). AI makes the marginal cost of doing things properly near-zero — pick the higher-rated option every time. Includes scope checks to distinguish contained vs unbounded 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。
Generate a structured handoff document capturing current progress, open tasks, key decisions, and context needed to resume work. Use when ending a session, saying "continue later", "save progress", "session summary", or "pick up where I left off".
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。
Audit an area of the codebase and propose the smallest structural moves that improve it - untangle boundaries, kill duplication, fix seams, break cycles. Produces a prioritized plan and decision records, not a rewrite. Use when a codebase feels tangled, hard to change, or is becoming a ball of mud, or when asked to improve or refactor 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。
Migrate workloads from Microsoft Azure to AWS. Triggers on: migrate from Azure, Azure to AWS, move off Azure, migrate AKS to EKS, migrate App Service or Azure VMs to AWS compute, migrate Azure SQL or Azure Database to RDS, migrate Cosmos DB to DynamoDB, migrate Azure OpenAI to Bedrock, move Azure AI or agentic workloads to AWS, estimate AWS costs for my Azure infrastructure, what-if workshop. Runs a 6-phase process: discover Azure resources from Terraform, the live `az` CLI, app code, and billing exports, then clarify, design, estimate costs, optionally reprice scenarios, generate artifacts, and collect feedback. Clarify gates Design, Estimate, and Generate; Generate is opt-in at the post-Estimate decision gate. Bicep/ARM discovery is not yet implemented; a Bicep/ARM-only workspace halts. Do not use for: GCP migrations (see gcp-to-aws), Heroku migrations (see heroku-to-aws), general AWS architecture advice (see architect-for-startups), AWS-to-Azure reverse migration, or Azure-to-Azure refactoring.
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。
Routes any task involving AWS databases — choosing, comparing, recommending, getting started with, or operating a database — to the correct service-specific skill. Supersedes general training-data knowledge with post-training service updates, corrected limitations, and decision procedures for relational (Aurora, DSQL, RDS), key-value (DynamoDB), wide-column (Keyspaces), document (DocumentDB), graph (Neptune), time-series (Timestream), and in-memory/caching (ElastiCache, MemoryDB) workloads. Activates when a user describes building an application on AWS that will store, retrieve, or manage data, even if they do not mention 'database' explicitly.
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 animations in React Native and Expo, making the decisions in the order that determines whether they feel right — should it animate, which thread it runs on, which properties, spring or timing, how the gesture hands off, how it degrades. Writes the implementation with Reanimated, Gesture Handler, Expo Router and expo-haptics. Use when animating anything in an Expo app, adding gestures, sheets, screen transitions, press feedback or haptics, or fixing motion that stutters on device. For web animation use `animate`.
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。
Update an OpenSpec change by revising its existing planning artifacts and keeping them coherent with one another. Use when the user wants to revise a change's plan, fold new decisions into it, or reconcile its artifacts after an edit. Also use when the user says "openspec update change" or "opsx update". If the user means the openspec update CLI command, which refreshes generated files, run that command instead. Never edits code.
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。
Make an evidence-backed go or no-go decision for an Alchemy-backed production release. Use when reviewing a launch or material integration change. Trigger with "Alchemy production checklist", "Alchemy go-live review", or "is this Alchemy integration ready".
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。
Run an evidence-backed Abridge production readiness decision across clinical, privacy, security, EHR, training, support, and rollback owners. Use when preparing a pilot or cohort expansion. Trigger with "review Abridge go-live".
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 AI-powered software with TypeSafe: small units of AI intelligence you can use like programming primitives. Its System One models, including Jev, turn natural language and application state into typed judgments and probabilities that code can combine. Use when a feature needs programmable common sense, when brainstorming what AI could make possible in an app, or when an LLM prompt-and-parse step could become a structured decision. Applications include routing, ranking, extraction, verification, and interactive experiences; these are starting points, not the limits. Read live docs and cookbooks to find useful patterns and discover new combinations.
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。
Creates comprehensive handoff documents for seamless AI agent session transfers. Triggered when: (1) user requests handoff/memory/context save, (2) context window approaches capacity, (3) major task milestone completed, (4) work session ending, (5) user says 'save state', 'create handoff', 'I need to pause', 'context is getting full', (6) resuming work with 'load handoff', 'resume from', 'continue where we left off'. Proactively suggests handoffs after substantial work (multiple file edits, complex debugging, architecture decisions). Solves long-running agent context exhaustion by enabling fresh agents to continue with zero ambiguity.
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。
Facilitates deliberate skill development during AI-assisted coding. Offers interactive learning exercises after architectural work (new files, schema changes, refactors). Use when completing features, making design decisions, or when user asks to understand code better. Supports the user's stated goal of understanding design choices as learning 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。