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概要と使いどころ
Generate two complementary onboarding documents that together give any engineer — from newcomer to principal — a complete understanding of a codebase. Use when user asks for onboarding docs or getting-started guides, user runs /deep-wiki, or user wants to help new team members understand a codebase.
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You are an expert software engineer and systems analyst. Use when user asks "how does X work" with expectation of depth, user wants to understand a complex system spanning many files, or user asks for architectural analysis or pattern 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。
You are a data pipeline architecture expert specializing in scalable, reliable, and cost-effective data pipelines for batch and streaming data processing.
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。
Data pipeline architecture, ETL/ELT patterns, data modeling, and production data platform 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。
Database development and operations workflow covering SQL, NoSQL, database design, migrations, optimization, and data engineering.
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 scalable data pipelines, modern data warehouses, and real-time streaming architectures. Implements Apache Spark, dbt, Airflow, and cloud-native data platforms.
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。
Reverse-engineer why a design decision was made. Move from observation to inference. Use when studying products you admire, learning from competitors, or building analytical design thinking.
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。
Forces the laziest solution that actually works, simplest, shortest, most minimal. Channels a senior dev who has seen everything: question whether the task needs to exist at all (YAGNI), reach for the standard library before custom code, native platform features before dependencies, one line before fifty. Supports intensity levels: lite, full (default), ultra. Use on ANY coding task: writing, adding, refactoring, fixing, reviewing, or designing code, and choosing libraries or dependencies. Also use whenever the user says "ponytail", "be lazy", "lazy mode", "simplest solution", "minimal solution", "yagni", "do less", or "shortest path", or complains about over-engineering, bloat, boilerplate, or unnecessary dependencies. Do NOT use for non-coding requests (general knowledge, prose, translation, summaries, recipes).
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。
Solves UI lock-in. Reverse engineers your existing UI, strips all layout/token decisions, preserves every hook, handler, and API call untouched, then rebuilds the UI from scratch with a fresh design system. Use /ui-restructure to fully redesign, --style apple|linear|minimal|dashboard for named styles, --mode layout|theme|grid for partial rebuilds, --god-mode to redesign from the user's perspective using 100-user mindset simulation and 6 UX principles, --keep-tokens to preserve your token system, --prompt for custom redesigns. Applies mandatory world-class polish: spring motion, 5-state micro-interactions, 8pt spacing grid, WCAG accessibility. Supports Next.js, React, Vue 3, Tailwind, CSS Modules, styled-components, shadcn.
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。
Orchestrates the tdmcp Anthropic Connectors Directory (Desktop Extension / MCPB) submission prep — designs the package, writes the privacy page, migrates .dxt→.mcpb, drafts all form answers, and QAs the result. Use for ANY work on the tdmcp directory/marketplace submission, including re-runs after a rejection ("update the submission", "redo the form answers", "re-migrate the bundle", "fix the privacy page", "the directory rejected us"). Drives a 4-agent pipeline (architect → docs-author ∥ bundle-engineer → QA).
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Expert in vector databases, embedding strategies, and semantic search implementation. Masters Pinecone, Weaviate, Qdrant, Milvus, and pgvector for RAG applications, recommendation systems, and similar
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Behavioral UX specialist applying choice architecture, friction audits, and commitment design to guide users toward intended actions without coercion. Use when optimizing conversion flows, onboarding, or checkout UX.
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Expert in building Retrieval-Augmented Generation systems. Masters embedding models, vector databases, chunking strategies, and retrieval optimization for LLM 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。
Use when context window output compression engine for CLI commands (60-99% token reduction).
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 improving prompts for any LLM. Applies proven prompt engineering techniques to boost output quality, reduce hallucinations, and cut token usage.
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。
BDB local-first long-term memory engine (memB). Use when querying, remembering, or adapting preferences, code architectures, and developer patterns across tasks.
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Autonomous CI/CD error recovery, distributed file-based locking, diagnostic triage, and two-phase GO gate resilience engine for BDB Agent OS multi-agent pipelines.
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Utilizes the Unreal Engine MCP server to control UE5 via a native C++ Automation Bridge plugin.
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。
You are a senior frontend engineer operating under strict architectural and performance standards. Use when creating components or pages, adding new features, or fetching or mutating data.
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Combines multi-agent brainstorming, the /grill-me slash command, and the 3 Core Godmodes (godmode-engineering, godmode-ui-ux, godmode-shipping) to force a comprehensive, multi-agent ideation and technical design workflow, ending in a plan-canvas review and a hand-off to /startcycle-graph. Use when the user asks to brainstorm, ideate, design, scope or sharpen a plan, idea or product, or when broad exploration before a build is the situation.
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 generating 3D meshes, executing text-to-CAD, or reconstructing scenes via TRELLIS, TripoSR, or Text-to-CAD local engines.
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。
Harness patterns for coding agents — memory, permissions, context engineering, delegation, skills, hooks, bootstrap.
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 running the autonomous multi-agent build pipeline after /bdbrainstorm or /grill-me, when the user types /startcycle-graph or "run a startcycle", or when a task needs durable state, a review repair loop and an automated quality gate. Routes to the startcycle-dispatch Dynamic Workflow, which reads production_artifacts/state.json and invokes Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, and Shipping in turn per .agents/graph.md's edge table — the agents never invoke each other.
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。