「system」の検索結果

Creates and maintains project documentation for features, systems, and components. Use when documenting how a feature, system, or component works — including writing, updating, or organizing docs. Does not scan or detect the project's technology stack — use project-discovery for repository analysis and config detection. Does not create architectural decision records — use architectural-decision-record for ADRs. Does not create or update coding standards — use coding-standard instead. Does not generate PR descriptions — use update-pr-description for that. Does not produce runbooks for operational scenarios — use runbook for that. Does not rewrite existing prose for readability — use edit-for-readability for that. Does not produce an ephemeral, understand-now overview of code or a PR — use code-overview 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。

Modern Zig project architecture guide. Use when creating Zig projects (systems programming, CLI tools, game dev, high-performance services). Covers explicit allocators, comptime, error handling, and build system.

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 design systems from reference UI images and generate implementation-ready UI design prompts. Use when users provide UI screenshots/mockups and want to create consistent designs, generate design systems, or build MVP UIs matching reference aesthetics.

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 creating or maintaining UI design systems, design tokens, component documentation, responsive calculations, quality checks, or design-dev handoff materials.

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。

Evidence-driven architecture research for understanding real systems and making technical decisions. Use when doing architecture landscape studies, source-backed system archaeology, build-vs-buy or adopt/adapt/build decisions, open-source and commercial comparisons, revisiting an earlier architecture choice, or handling requests such as 架构调研, 架构选型, 竞品架构, 技术尽调, 同类方案, 开源替代, how is X built, and what should we learn from X. Do not use for small mechanical changes, market-only discovery, or detailed design after the technology direction is already fixed.

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 guidance for systematic backtesting of trading strategies. Use when developing, testing, stress-testing, or validating quantitative trading strategies. Covers "beating ideas to death" methodology, parameter robustness testing, slippage modeling, bias prevention, and interpreting backtest results. Applicable when user asks about backtesting, strategy validation, robustness testing, avoiding overfitting, or systematic trading 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。

Apply the bioengineering, translational systems biology, and tissue modeling frameworks of Linda Griffith, Professor of Biological Engineering at MIT and tissue engineering pioneer. Use this skill whenever facing decisions, designs, or evaluations in bioengineering, tissue modeling, microphysiological systems (organs-on-chips), synthetic matrix design, disease subtyping, or drug target validation. Reach for this skill when evaluating preclinical models (human vs. animal), reframing overlooked or neglected pathologies into rigorous engineering challenges, selecting biomaterials, designing cell signal processing assays, or diagnosing operational flaws in experimental platforms (e.g., PDMS compound absorption or Matrigel lot variability).

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。

Reach for this skill whenever you are discussing reinforcement learning, agentic AI systems, AI alignment, continual learning, or the philosophical limits of large language models. This skill channels the thinking of Richard S. Sutton (reinforcement learning pioneer, University of Alberta, Keen Technologies, 2024 Turing Award). Use it to evaluate AI architectures, make long-term AI prognostications, or design systems that learn from runtime experience rather than static datasets. Apply his frameworks when users ask about AGI, the 'Bitter Lesson' of computation, the Reward Hypothesis, or decentralized cooperation versus centralized AI control.

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。

Applies the reasoning of David Silver, lead researcher on AlphaGo and AlphaZero at DeepMind, to problems of AI design, reinforcement learning, and open-ended discovery. Use this skill whenever you are designing AI systems, evaluating learning algorithms, balancing exploration vs. exploitation, choosing research problems, or discussing how to break past human performance ceilings. Reach for this whenever the user asks about self-play, Monte-Carlo Tree Search, tabula rasa learning, AGI, or moving from human-curated data to autonomous experience. It helps shift the focus from hardcoding human knowledge to building systems that learn for themselves.

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。

Create a feature branch, commit changes, push, and open a PR against iopsystems/rezolus

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 efficient Blender repetition, Array-driven stairs/modules/openings, instancing, scattering, Bezier/NURBS curve-profile geometry, Geometry Nodes fields, repeat zones, simulation zones, particle-like systems, and collective motion. Use for stairs, railings, trim, pipes, cables, repeated windows/panels, vegetation, debris, crowds, rain, snow, modular structures, or any request involving dependent elements or reusable 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。

Automatically infer formal correctness properties from Verilog/SystemVerilog RTL code and generate SystemVerilog Assertions (SVA). Identifies control-flow invariants (mutual exclusion, valid-ready handshakes, pipeline ordering, safety properties), liveness expectations, and temporal properties. Use when working with RTL designs that need formal property generation, when adding assertions to existing RTL, or when users ask to infer properties, generate assertions, or create formal specifications from hardware designs.

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 literature review across multiple arXiv papers.

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 design primitives from a public website and generate starter token files for your project.

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 and consolidate reusable components, design tokens, and patterns into your design system. Identifies opportunities for systematic reuse and enriches your component library.

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。

JeecgBoot 系统主数据查询与管理。Use when user asks to query/create/manage system master data, or says "查询角色", "查询用户", "查询部门", "查询字典", "创建字典", "创建角色", "查岗位", "查职务", "查租户", "查数据源", "查定时任务", "系统主数据", "query roles", "query users", "query depts", "create dict", "create role", "master data". Also triggers when other skills (desform, bpmn, codegen) need to look up or create roles, users, departments, dictionaries, or positions as dependencies — follows "先查后建" (query-first-create-later) principle.

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。

Implement complete dark mode for any frontend project. Auto-detects theme system (Flutter ThemeData, Tailwind dark class, CSS variables, MUI, Chakra UI, styled-components), generates a dark color palette with proper surface elevation hierarchy and desaturated brand colors, creates a theme toggle with system preference detection and persistence, migrates all hardcoded colors to theme tokens, handles special cases like images, shadows, form inputs, and charts, then verifies WCAG AA contrast ratios for both modes. Use when you need to add dark mode, fix dark theme contrast issues, create a theme switcher, or migrate hardcoded colors to theme-aware tokens.

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。

An engineering-grade marketing system organized around two surface modes — a deep black canvas for hero and footer chapters and a flat paper-white canvas for body content — connected by a single, almost violently saturated NVIDIA Green accent that carries every CTA, every active tab, and the small decorative corner squares that mark out cards. The system is unapologetically angular: 2px radius across every surface, tight bold sans-serif typography in NVIDIA's proprietary EMEA cut, and a hairline gray rule that separates dense multi-column technical content. There is no decorative gradient, no atmospheric mesh, no soft drop shadow — just black, white, gray, and green stacked into a structured editorial grid that scales from product cards to massive industry landing pages without bending its rules.

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 this skill whenever the user asks for an HTML-first visual design deliverable or design judgment: interactive prototype, slide deck, motion demo, infographic, dashboard, landing page, whitepaper, changelog, business card, social cover, brand system, design critique, multi-variant exploration, or export to MP4, GIF, PPTX, PDF, or SVG. It is optimized to make AI agents do the routing, template selection, verification, and export prep so humans spend less time prompt-engineering. Do not use for production web apps, SEO sites, backend systems, or pure copy edits.

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。

Implements document chunking, embedding generation, vector storage, and retrieval pipelines for Retrieval-Augmented Generation systems. Use when building RAG applications, creating document Q&A systems, or integrating AI with knowledge bases.

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。

Identifies reusable UI patterns, components, and design tokens, then extracts and consolidates them into a design system for systematic reuse.

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。

Token architecture, component specifications, and slide generation. Three-layer tokens (primitive→semantic→component), CSS variables, spacing/typography scales, component specs, strategic slide creation. Use for design tokens, systematic design, brand-compliant presentations.

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。

An enterprise-marketing canvas faithful to Carbon Design System: white surfaces, charcoal type, IBM Blue (#0f62fe) as the single confident accent, and a deliberately flat-square aesthetic where corners stay at 0–4px. Type runs IBM Plex Sans at light weight 300 for display sizes (a brand signature) and 400/600 for body and emphasis. Cards live as thin-bordered tiles with no shadow; sections separate via subtle gray rows. The chrome is square, the typography is light, and the only color in the system is one assertive blue — the result reads as old-world enterprise gravitas reframed for the cloud era.

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。

An enterprise-infrastructure marketing canvas built around a near-black ground (#000000) and a system of per-product accent colors — Terraform purple, Vault yellow, Consul pink, Waypoint cyan, Vagrant blue — that act as identity tokens rather than decorative palette. Display type is hashicorpSans set in 600/700 with tight 1.17–1.21 line-heights; body type runs the same family at 500 weight with relaxed 1.50–1.71 line-heights. Cards live as charcoal surfaces with 1px translucent gray borders; product showcase cards lift into per-product chromatic gradients. The system reads as confident, technical, and intentionally multi-product — every section quietly signals which HashiCorp tool it represents.

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。