「spa」の検索結果

Azure Machine Learning SDK v2 for Python. Use for ML workspaces, jobs, models, datasets, compute, and pipelines.

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Premium brand-kit image generation skill for creating high-end brand-guidelines boards, logo systems, identity decks, and visual-world presentations. Trained for minimalist, cinematic, editorial, dark-tech, luxury, cultural, security, gaming, developer-tool, and consumer-app brand systems. Optimized for intentional logo concepting, refined composition, sparse typography, strong symbolic meaning, premium mockups, art-directed imagery, and flexible grid layouts.

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 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。

Apache Spark, Hadoop, distributed computing, and large-scale data processing for petabyte-scale workloads

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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。

Systematic design quality evaluation. Hierarchy, type, color, space, craft, system. Use when evaluating whether a design is ready to ship, running quality audits, or setting quality standards.

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。

The 10-second design audit. Look at any design and name what's working and what's not within seconds. Trains rapid pattern recognition for hierarchy, spacing, type, and color. Use when evaluating designs quickly, giving first-impression feedback, or building perception speed.

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。

Micro-decision analysis. Catalog the border radii, spacing scales, type sizes, and state coverage that most people miss. Use when auditing design systems, evaluating craft quality, or training granular perception.

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Annotate and map the anatomy of any design. Identify visual hierarchy, spacing systems, color roles, and tension points through clinical analysis. Use when doing formal design audits, teaching design analysis, or building structured observation skills.

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Pull design principles from architecture, fashion, film, industrial design, and physical spaces into screen-based design. Use when seeking inspiration beyond digital, expanding creative range, or building cross-disciplinary design vocabulary.

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Reads Copilot conversation history and extracts executive-ready success stories. Use when the user asks to study prior Copilot sessions, summarize what was done before, mine evidence, or quantify time, cost, risk, and priority alignment from GitHub Copilot history in any workspace or repository.

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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.

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Spin up N Rhino MCP sessions in parallel and fan out one agent per Rhino. Use when the user wants multiple Rhinos running simultaneously with each agent driving its own — e.g. "spawn 3 agents and have each launch Rhino", "run 5 Rhino tasks in parallel". For a single Rhino, use `/launch-rhino` instead.

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Survey one surface of tdmcp (artist controls, library/packaging, CLI/DX, AI/LLM, or TouchDesigner depth) for candidate NEW features — inventory what exists, cross-check the roadmap, apply the gap-finding lenses, vet each idea, and emit a structured, novelty- and confidence-labelled candidate list to _workspace/discovery/. Use when a td-surveyor agent is scouting a surface during the feature-discovery harness.

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Drive a whole tdmcp feature BACKLOG to completion as resumable, wave-by-wave releases — the campaign layer ABOVE tdmcp-pipeline/tdmcp-feature-lead. Use whenever the user wants to implement an ENTIRE backlog or discovery file (e.g. _workspace/discovery*/FEATURE_BACKLOG*.md), 'all the features', many features across multiple releases, or a long autonomous build campaign, AND for every follow-up: continue/resume the campaign, run the next wave, re-run a failed wave, fold in QA results, or check campaign status. Backed by a ledger.json so re-running is idempotent (skips shipped work, resumes interrupted work) and resilient (retry-once then quarantine-and-continue). For a SINGLE feature or one small batch, use tdmcp-pipeline instead; this skill is for the whole ledger.

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Use when executing implementation plans with independent tasks in the current session — trigger phrases "execute the plan", "independent tasks", "parallel tasks", "subagents for the plan","split the plan into subagents". Dispatches a fresh subagent per task with two-stage review after each: spec compliance first, then code quality.

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Use when one Claude Code session must supervise several others on this machine — adopting sessions the user started by hand, or spawning workers itself. Covers roster, status requests, a GO board, idle notices, and the GO-token protocol. Works with AOS alone; the AO daemon is optional.

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Reference for how BDB structures autonomous software engineering work — the seven-node dispatcher graph (Architect, TechLead, UI/UX, Engineering, Media/EventTech, Reviewer, Shipping) that /startcycle-graph actually runs. Use when you need the high-level lifecycle framing without inventing your own process.

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Use when a task needs a small, throwaway multi-agent fan-out (a couple of parallel workers plus a review pass) in ANY project — trigger phrases "spawn a few parallel agents", "fan out", "quick worker team" — without the full /startcycle-graph contract, no .agents/graph.md bootstrap, no state.json schema, no persistent files. Roles run on the session model unless `.aos/pipeline.json` overrides one (an external CLI can take the worker step) and portable across machines that may not have Antigravity/OpenCode/Codex installed.

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Linear multi-agent build pipeline with file hand-offs in production_artifacts/, run after /bdbrainstorm or /grill-me. This is the linear variant — no state.json, no repair loop, no dispatcher graph — distinct from startcycle-graph, which adds durable state and automated escalation. Use when an approved plan should be built, when the user types /startcycle or "run a startcycle", or when you pick a startcycle variant from the situation yourself (no user prompt needed).

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Use when acting as the multi-project orchestrator and lead coordinator across repositories using the Agent Orchestrator (AO) daemon. Spawns, inspects, coordinates, and reviews parallel coding agents in isolated Git worktrees.

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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.

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AI image generation and editing: e-commerce templates (hero/banner/detail/lifestyle), image refinement (background replace/remove, enhance, retouch, style transfer), icon extraction (bg-removal + detect + smart crop with transparent output).

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iOS design system tokens for Pencil MCP: SF Pro typography scale, iOS semantic colors (light + dark mode hex values), spacing grid, touch targets, corner radii, and component heights. Use when initializing a .pen file for iOS design or setting up design variables.

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。