1,235 件 ・ 関連度順
概要と使いどころ
Use when building a custom UI (page, dashboard, buttons) that should wake a Grok Bot over a webhook, when the user must provide a webhook sender key, or when exposing that UI on Tailscale.
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Design an auditable playbook when no narrower one fits: a large migration, an ambitious multi-part change, or work a human reviews after stepping away. Scales rigor to the task, runs a hypothesis loop, and logs decisions. Use for /figure-it-out or 'figure it out'.
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 in monorepo architecture, build systems, and dependency management at scale. Masters Nx, Turborepo, Bazel, and Lerna for efficient multi-project development. Use PROACTIVELY for monorepo setup,
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Optimize Bazel builds for large-scale monorepos. Use when configuring Bazel, implementing remote execution, or optimizing build performance for enterprise codebases.
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Generate, extend, or audit design tokens in DTCG format with the 3-tier architecture (primitive → semantic → component). Use when the user wants a color palette, type scale, spacing/shadow/radius/motion tokens, multi-brand theming, or wants to validate token files. Covers colors, typography, spacing, shadows, borders, breakpoints, motion, gradients, opacity, blur, sizing, states, theming.
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Turn a reference image, screenshot, or mockup into token-driven, accessible code — infer the design system from the reference (palette, type scale, spacing, radius, layout archetype), map it to the 3-tier tokens, rebuild it, then verify with the kit's gates. Use when the user provides a design/screenshot and wants matching UI 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。
React composition patterns that scale. Use when refactoring components with boolean prop proliferation, building flexible component libraries, or designing reusable APIs. Triggers on tasks involving compound components, render props, context providers, or component architecture. Includes React 19 API changes.
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When a change has effects past the immediate fix—incentives, scale, feedback—trace consequence chains with timing and probability before committing.
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Authoring conventions for exploratory data analysis notebooks and analytical dashboards, covering section sequence, visualization selection, scale thresholds, caching and state, and dashboard validation budgets. Use when composing or reviewing an EDA notebook, an analytical dashboard, or a dashboard test pass.
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。
Investigates network, streaming and protocol failures with falsification-first layered experiments, the general fallback for unknown root causes. Use for ECONNRESET / RST_STREAM / SSE stalls, CDN/CGNAT timeouts or certificate errors, throughput collapse where everything returns 200 but transfers crawl, or LAN identity/topology mysteries. Not for known Tailscale/TUN/DNS/proxy-path conflicts (use tunnel-doctor).
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 a genuinely good drink from the bottles already on your shelf — no special trip, no 12-ingredient recipe. Use when asked what can I make with [spirits], cocktail from what I have, I've got [bottles] what can I drink, or make me a drink without buying anything. Produces two or three cocktails you can build right now with ratios, a method, sensible substitutions for what you're missing, and a zero-proof version — scaled to how many you're making.
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Get your attention back with a 30-day protocol that assumes you'll break it — a screen-time ledger without moralizing, friction engineering (what to delete, grayscale, where the phone sleeps), planned relapses, and honest replacement activities for the boredom that shows up on day 3. Use when someone says 'my screen time is 7 hours', 'I want a dumbphone', 'digital detox', 'I can't read books anymore', or 'my attention span is gone'. Produces the ledger, a personal friction plan, and the 30-day protocol with expected failure points.
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Make an AI agent or automation reliable enough to trust — the tests, checks, and guardrails that catch its failures before they reach anything real. Use when asked how do I test my AI agent, make my automation reliable, my agent works sometimes, or how do I trust an AI workflow in production. Produces a map of where the agent can fail (bad input, hallucination, wrong tool call, edge cases, silent errors), the checks that catch each (validation, evals on real cases, human-in-the-loop gates, monitoring), a right-sized reliability plan scaled to the stakes, and a rollout that earns trust incrementally.
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 a 360-degree feedback survey or write a structured 360 feedback report. Use when asked to build a 360 feedback process, write 360 feedback for a colleague, design a feedback survey, or produce a feedback report. Produces either a complete survey instrument with rating scales and open-ended questions, or a structured narrative feedback report with themes, strengths, and development areas.
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Help users master professional writing to bridge gaps between stakeholders, scale their leadership influence, and ensure organizational clarity through structured narratives and async-first norms.
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。
Help users transition from individual execution to a coaching-centric leadership style that scales through the growth of their team. It focuses on identifying skill deficits, providing reflection space, and leveraging frameworks to accelerate professional maturity.
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Help users recruit compatible co-founders, sequence early leadership hires to fill capability gaps, and manage the evolution of the executive team as the company scales.
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Help users build and scale a high-velocity growth experimentation engine that prioritizes impact and fosters a culture of rapid learning.
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Help users understand the core mechanics of marketplace businesses, from solving the cold start problem to maintaining quality and preventing platform leakage at scale.
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。
Ideogram text-to-image generation with the strongest text rendering in the catalog: generate, edit, remix, upscale, describe, balance. Trigger phrases: ideogram, text in image, thumbnail with words, ideogram generate, magic fill.
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 swinging spotlight text-reveal HTML animation with configurable text, swing angle, lamp scale, glow, and colors. Use when users ask for 聚光灯扫字动画, spotlight text reveal, light logo reveal, 发光文字揭示动画, or want a reusable HTML animation instead of a static image.
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。
Scalable data processing for ML workloads. Streaming execution across CPU/GPU, supports Parquet/CSV/JSON/images. Integrates with Ray Train, PyTorch, TensorFlow. Scales from single machine to 100s of nodes. Use for batch inference, data preprocessing, multi-modal data loading, or distributed ETL pipelines.
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 meaning-carrying editorial data-illustrations in the monotykamary / Linear aesthetic (near-black grayscale, Inter display + mono labels, hairline framed figures) with a single coral accent. This is a GENERATIVE GUIDE, not a template gallery: it teaches the "claim -> geometry" method so any session can invent the RIGHT figure for what a post actually argues, then render it as a self-contained, theme-aware, reduced-motion-safe HTML/SVG figure. Use when writing/illustrating a blog post, essay, spec, or slide and you want a chart, diagram, or illustration that carries a thesis (not decoration) in the house style. Triggers: "make a chart/diagram/illustration for this", "visualize this argument", "monotykamary/Linear style figure", "editorial graphic".
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 targeting New Phytologist or deciding whether a plant-science manuscript fits this venue. Encodes the journal's fit, the integrative conceptual-advance and whole-plant/ecosystem-scale bar, evidence and data-deposition expectations, house style, official-submission re-check, and desk-reject heuristics.
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。