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概要と使いどころ
Develop native iOS applications with Swift/SwiftUI. Masters iOS 18, SwiftUI, UIKit integration, Core Data, networking, and App Store optimization. Use PROACTIVELY for iOS-specific features, App Store optimization, or native iOS development.
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 Flutter development with Dart 3, advanced widgets, and multi-platform deployment. Handles state management, animations, testing, and performance optimization for mobile, web, desktop, and embedded platforms. Use PROACTIVELY for Flutter architecture, UI implementation, or cross-platform features.
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 modern C# code with advanced features like records, pattern matching, and async/await. Optimizes .NET applications, implements enterprise patterns, and ensures comprehensive testing. Use PROACTIVELY for C# refactoring, performance optimization, or complex .NET solutions.
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 features guided by data insights, A/B testing, and continuous measurement using specialized agents for analysis, implementation, and experimentation.
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 idiomatic C++ code with modern features, RAII, smart pointers, and STL algorithms. Handles templates, move semantics, and performance optimization. Use PROACTIVELY for C++ refactoring, memory safety, or complex C++ patterns.
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 production-ready LLM applications, advanced RAG systems, and intelligent agents. Implements vector search, multimodal AI, agent orchestration, and enterprise AI integrations. Use PROACTIVELY for LLM features, chatbots, AI agents, or AI-powered 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。
Expert C4 Context-level documentation specialist. Creates high-level system context diagrams, documents personas, user journeys, system features, and external dependencies. Synthesizes container and component documentation with system documentation to create comprehensive context-level architecture. Use when creating the highest-level C4 system context documentation.
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。
Live Borg debugging by exec-ing into the borg-web-ui Docker container. Use when debugging borg commands, writing tests against real borg output, developing borg 2.0 features, or verifying borg behavior before writing 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。
Explore user intent, requirements, and design options through collaborative dialogue before implementation. Use before building new features, components, or systems — whenever the user describes something to build and design decisions are involved. Triggers: "brainstorm", "help me design", "think through the requirements", "头脑风暴", "设计方案", "梳理需求". Not for bug fixes, config changes, or tasks with an obvious implementation path.
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。
AI DevKit · Requirements phase guidance for starting features and reviewing requirements. Use when the user wants to capture a new requirement, clarify product scope, initialize feature docs, review requirements, or run dev-lifecycle phases 1-2.
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 a Google Alerts-style keyword coverage tracker. Uses news-search for recent keyword queries, lets the LLM dedupe and classify real features versus junk, stores decisions in SQLite, and alerts only on new real coverage.
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。
Guides the creation of technical design documents before writing code, producing architecture diagrams, data models, API interface definitions, implementation plans, and multi-option trade-off analyses. Use when the user asks to plan a feature, architect a system, design an API, explore implementation approaches, or requests a technical design or spec before coding — especially for complex features involving multiple components, ambiguous requirements, or significant architectural changes.
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。
Guided, conversational walkthrough that explains code, UI, UX, features, or .copilot-tracking artifacts with navigable evidence links, a deep review before explaining, and a reconciled decisions-and-changes ledger. Use when the user wants to understand how something works or why it was changed.
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。
Plan and ship a game under a jam deadline: lock scope to the clock, schedule the hours, cut features, and submit on time. Use for a game jam (Ludum Dare, GMTK Jam, Global Game Jam), a 48-hour or weekend build, or scoping and submitting a jam entry.
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。
Architect game input — action mapping (abstracting keys into named actions), rebinding with conflict detection and persistence, multi-device support (keyboard, gamepad, touch), analog deadzones, and feel features like input buffering and coyote time, plus accessibility. Engine-neutral. Use when the user mentions input mapping, rebind controls, gamepad support, deadzone, input buffering, coyote time, or accessible controls.
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 /debug to find and fix a bug's root cause: a test failing for an unclear reason, /check verify finding a failure, or behavior being wrong. Runs a reproduce, localize, hypothesize, test, fix, verify loop, makes the minimal fix, and hands a regression test to /test. No features, no extra refactors.
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。
Writes educational Teams channel posts about Claude Code features and best practices, using templates emphasizing concrete examples and underlying principles (e.g. context engineering). Use when writing a Teams post or announcement to teach colleagues Claude Code usage, announce a feature, share a tip, or document a lesson learned.
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。
Analyze competitors and create competitive landscape documentation with feature matrices, positioning maps, and strategic recommendations. Use when asked to analyze competitors, create competitive analysis, compare features with competitors, build a competitive landscape, track competitive positioning, or prepare sales battlecard inputs. Produces structured competitor profiles, feature comparison matrix, win/loss analysis, and prioritised strategic recommendations. For a one-off teardown of a single rival use competitor-teardown; for a recurring market briefing use competitive-intelligence-monitor.
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 human changelog from the REAL commit history — read the actual commit range via the GitHub connector, not a template. Use when asked to write the changelog for this release, what changed since the last tag, draft release notes from my commits, or summarise this range for users in Cowork. Reads commits/PRs between two refs via the GitHub connector, groups them into user-facing changes (features / fixes / breaking), translates commit-speak into human benefit, and produces a changelog artifact ready for the release.
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 statistically rigorous A/B tests for product features, UI changes, onboarding flows, and pricing experiments. Use when asked to set up an experiment, design an A/B test, calculate sample size, or interpret test results. Produces a complete test plan with hypothesis, variant definitions, sample size, duration estimate, guardrail metrics, and a results interpretation guide.
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。
Decode a 401k or workplace retirement plan — the real cost of its funds, the match's fine print, vesting math, and the plan features worth using or avoiding. Use when someone asks 'is my 401k any good', 'decode my 401k plan', 'which funds should I look at', or 'what fees am I paying'. Produces a fee decode in dollars-over-time, match and vesting math, a fund-lineup triage by cost, and the questions for HR or the plan administrator.
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。
Structure AI and ML product decisions with the rigour of any product decision. Use when building AI-powered features, evaluating LLM integrations, designing AI products, or assessing AI readiness. Produces a complete AI product canvas covering problem definition, model approach, data requirements, evaluation framework, UX design, responsible AI checklist, and launch monitoring plan.
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。
Patterns for parallel subagent execution using the Agent tool (formerly Task). Use when coordinating multiple independent tasks, spawning dynamic subagents, or implementing features that can be parallelized.
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。
Extract structured specifications from electronic component datasheet PDFs — pinouts, electrical characteristics, peripherals, topology, and features. Cache extractions per project for consumption by schematic and PCB analyzers. Primary consumer infrastructure for `kicad`, `emc`, `spice`, and `thermal` analyzers. Use this skill whenever the user asks to extract, verify, or read specs from a component datasheet; when analyzers need verified IC knowledge (EN pin thresholds, PG presence, USB peripheral speed); or when a review mentions datasheet coverage, extraction quality, or per-MPN specifications. Also triggers on "extract this datasheet", "what are the specs for MPN X", "verify datasheet extraction", or "check pin functions for part Y".
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。