「wind」の検索結果

Use when the user asks to run Gemini CLI for code review, plan review, or big context (>200k) processing. Ideal for comprehensive analysis requiring large context windows. Uses Gemini 3 Pro by default for state-of-the-art reasoning and coding.

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。

Optimizes transformer attention with Flash Attention for 2-4x speedup and 10-20x memory reduction. Use when training/running transformers with long sequences (>512 tokens), encountering GPU memory issues with attention, or need faster inference. Supports PyTorch native SDPA, flash-attn library, H100 FP8, and sliding window attention.

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。

RNN+Transformer hybrid with O(n) inference. Linear time, infinite context, no KV cache. Train like GPT (parallel), infer like RNN (sequential). Linux Foundation AI project. Production at Windows, Office, NeMo. RWKV-7 (March 2025). Models up to 14B parameters.

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。

Memory is the cornerstone of intelligent agents. Without it, every interaction starts from zero. This skill covers the architecture of agent memory: short-term (context window), long-term (vector stores), and the cognitive architectures that organize them. Key insight: Memory isn't just storage - it's retrieval. A million stored facts mean nothing if you can't find the right one. Chunking, embedding, and retrieval strategies determine whether your agent remembers or forgets. The field is fragm

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。

Pixel-perfect Figma to React conversion using coderio. Generates production-ready code (TypeScript, Vite, TailwindCSS V4) with high visual fidelity. Features robust error handling, checkpoint recovery, and streamlined execution via helper script.

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。

Extend context windows of transformer models using RoPE, YaRN, ALiBi, and position interpolation techniques. Use when processing long documents (32k-128k+ tokens), extending pre-trained models beyond original context limits, or implementing efficient positional encodings. Covers rotary embeddings, attention biases, interpolation methods, and extrapolation strategies for LLMs.

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。

HeroUI v3 React component library (Tailwind CSS v4 + React Aria). Use when building UIs with HeroUI — creating Buttons, Modals, Forms, Cards; installing @heroui/react; configuring dark/light themes with oklch variables; or fetching component docs. Keywords: HeroUI, Hero UI, heroui, @heroui/react, @heroui/styles.

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。

Connect a ChatGPT or Claude account and sync its conversation history into the brain automatically. Cookie paste-in is the primary lane; sync is incremental (watermark + trailing-window gap-heal), and can run on a schedule via autopilot or host cron. Distinct from the export-file lane (that lives in conversation-archive) — this is the LIVE, account-connected, auto-scraping 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。

Use when an agent needs to drive a real Chrome window via opencli — inspect a page, fill forms, click through logged-in flows, or extract data ad-hoc. Covers the selector-first target contract, compound form fields, stale-ref handling, network capture, and the agent-native envelopes the CLI returns. Not for writing adapters — see opencli-adapter-author for that.

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。

Work inside cua Spaces through the cua MCP server. A Space is a remote or local computer the user can watch; you can run commands in it, move files in and out, show its desktop or a single window on the user's screen, start coding agents inside it, teleport a signed-in app session into it, and share the host's network with it. Use when the user mentions Spaces, asks you to do work "in a Space", or wants a task isolated but visible.

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。

Reads a forwarded customer email or ticket, pulls order and refund status from a payments connector (PayPal, Square, or Stripe) or Shopify, account history from the CRM, and open tickets from a support desk (Zoho Desk), drafts a tone-matched reply in the owner's writing voice, and can issue a refund through the payments connector with explicit owner approval. With Shopify connected it also runs a proactive order-triage mode that surfaces orders needing attention — unfulfilled past the promised window, payment problems, pending refunds, stuck shipments — and drafts the next action for each before the customer has to ask. Use when the user says "draft a response," "answer this customer," "where's my order," "I want a refund," "check my orders," or "anything about to blow up."

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。

Runs the payroll-confidence chain end to end — forecasts cash across the payroll window with cash-flow-snapshot, ranks and drafts overdue-invoice reminders with invoice-chase to close any gap, then assembles timesheets, flags every anomaly, and stages the run with payroll-prep. On Gusto the run is staged for the owner to submit; on QuickBooks Payroll, or with no payroll system, the last step is a validated run sheet the owner keys in. This chain never presses the payroll button. Builds on the ledger (MYOB, NetSuite, QuickBooks, Xero, or Zoho Books), with Gmail or M365, PayPal, Square, Stripe, and either Gusto or QuickBooks Payroll adding detail, and falls back to CSV and timesheet uploads. Use it whenever payroll is the worry, including "can I make payroll," "payroll is due Friday," "plan payroll," "will I have enough to pay the crew," or "I need to run payroll but money is tight." Accepts optional horizon and payroll-date arguments.

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。

Proactive watch over your book - deals gone quiet, close dates slipping into view, champion changes, renewal windows opening, competitor mentions - surfaced as a short alert digest you can act on. Use when the user asks "what changed in my book", "any deals gone quiet", "what should I be worried about today", "run my deal signals", or sets it up as a recurring scheduled task.

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 correct, performant SQL across all major data warehouse dialects (Snowflake, BigQuery, Databricks, PostgreSQL, etc.). Use when writing queries, optimizing slow SQL, translating between dialects, or building complex analytical queries with CTEs, window functions, or aggregations.

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。

MANDATORY routing rules for context-mode. Invoke whenever you analyze, count, filter, compare, search, parse, or transform data; fetch a URL; or run a data-heavy command — so raw bytes stay out of the context window.

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。

Show how much context window context-mode saved this session. Displays token consumption, context savings ratio, and per-tool breakdown. Read-only — shows stats only, no reset capability. To wipe the knowledge base entirely, use ctx_purge instead. Trigger: /context-mode:ctx-stats

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。

Generates high-converting landing pages as complete Next.js/React (TSX) components with Tailwind CSS. Creates hero sections, feature grids, pricing tables, FAQ accordions, testimonial blocks, and CTA sections using proven copy frameworks (PAS, AIDA, BAB). Outputs SEO meta tags, structured data, and performance-optimised code targeting Core Web Vitals (LCP < 1s, CLS < 0.1). Use when the user asks to create a landing page, marketing page, homepage, single-page site, lead capture page, campaign page, promo page, or conversion-optimised web page — or when they want to A/B test landing page variants or replace a static page with one designed to convert.

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。

Generates complete, production-ready SaaS project boilerplate including authentication, database schemas, billing integration, API routes, and a working dashboard using Next.js 14+ App Router, TypeScript, Tailwind CSS, shadcn/ui, Drizzle ORM, and Stripe. Use when the user wants to create a new SaaS app, start a subscription-based web project, scaffold a Next.js application, or mentions terms like starter template, boilerplate, new project, or wiring up auth and payments.

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。

Frontend development skill for React, Next.js, TypeScript, and Tailwind CSS applications. Use when building React components, optimizing Next.js performance, analyzing bundle sizes, scaffolding frontend projects, implementing accessibility, or reviewing frontend code 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。

Use when building a quarterly bookings forecast, ARR projection, pipeline forecast, NRR projection, or commit/best-case/pipe-only board number — especially when the CRO needs to walk the board through funnel math + cohort ARR + per-stage conversion assumptions without the theatre of a single undefended number. Decomposes pipeline into commit, best-case, and pipe-only tiers; projects cohort-level NRR/GRR to surface leaky cohorts before they show up in the consolidated number; scores per-stage funnel confidence so soft-floor stages get treated differently from high-confidence ones. Every output explicitly names the conversion rate used, the data window, and the weighting choice. For Head of Commercial, RevOps, VP Sales, and CRO at quarterly forecast or board prep. NOT financial close (see finance/financial-analysis). NOT strategic CRO hiring/territory (see c-level-advisor/cro-advisor). NOT pricing (see sibling pricing-strategist).

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 a startup is approached by a prospective partner and someone has to decide should we sign this partner, at what partner tier (referral / reseller / OEM / SI-consulting / strategic alliance), with what joint GTM commitment, and at what revshare. Classifies partner tier from independent-demand evidence vs. preferential-terms hunting, designs a 90-day joint GTM plan, models revshare against direct-sale margin, and surfaces kill criteria for unwinding under-performing partnerships. For Head of Partnerships, Head of BD, and Founder-CEOs doing reseller agreement, OEM deal, or strategic alliance review — not technical sale enablement, not channel cost economics, not M&A.

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。

Screenshot or record a cmux window from the CLI, for a pull request, a bug report, or to see what the app actually looks like right now. Use when a change is visual, when a drag or an animation cannot be described in text, or when a reviewer needs evidence rather than a claim.

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。

End-user control of cmux topology and routing (windows, workspaces, panes/surfaces, focus, moves, reorder, identify, trigger flash). Use when automation needs deterministic placement and navigation in a multi-pane cmux layout.

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 the user explicitly asks for a desktop or system screenshot (full screen, specific app or window, or a pixel region), or when tool-specific capture capabilities are unavailable and an OS-level capture is needed.

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。