'engine' 검색 결과

Build browser games and game engines with HTML5, Canvas, WebGL, and JavaScript. Starter templates and reference guides support 2D and 3D graphics, controls, collision detection, audio, multiplayer features, performance improvements, and publishing.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a breakout, maze, or platform game
• Add movement, gravity, and collision detection
• Support touch input and game controllers

Analyze Go malware binaries in Ghidra by extracting build metadata, function names, dependencies, and relevant strings. Use GoResolver to help recover obscured names and organize functions so analysts can focus their reverse engineering work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating the structure of a Go malware sample
• Recovering function names from stripped or obfuscated binaries
• Identifying embedded Go modules and dependencies

Turn an unresolved customer issue into a structured brief for the right team. Gather context, document reproduction steps, assess customer and business impact, and clarify the action and deadline needed from engineering, product, security, or leadership.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing a bug report for engineering
• Consolidating reports of an issue affecting multiple customers
• Escalating an issue that puts a customer renewal at risk

Create or improve AI prompts through a guided conversation about goals, roles, examples, and output formats. Receive a complete, reusable prompt with an explanation of its design, including behavioral boundaries for public-facing AI roles when needed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Improving an existing AI prompt
• Designing prompts for product naming
• Defining boundaries for a customer service bot

Analyze a codebase to draft onboarding documents covering project structure, technologies, local setup, and common tasks. Tailor the depth for junior engineers, senior developers, or contractors, with troubleshooting guidance and contribution checklists.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Onboarding a new engineer or contractor
• Updating project guides after a major refactor
• Preparing technical handoff documentation

Assess startup engineering operations across delivery, hiring, team structure, and production practices. Use delivery metrics, hiring conversion rates, and staffing data to identify bottlenecks, estimate candidate needs, and plan management roles and operational improvements.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find where development work waits before release
• Estimate candidate volume needed for hiring goals
• Review team structure and manager hiring needs

Build a quick, disposable game prototype in HTML, a game engine, or on paper to test one core mechanic. Use playtest findings to decide whether to continue, change direction, stop, or leave the idea unassessed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Test whether a core game mechanic is fun
• Compare several variations of a game concept
• Explore a mechanic change during production

Review whether game design requirements are covered by documented architecture decisions. Identify missing coverage, conflicting decisions, and engine compatibility concerns, with an optional matrix linking requirements to development stories and tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find game requirements without architecture decisions
• Check for conflicts between architecture decisions
• Review compatibility with the pinned engine version

Plan and create Google Kubernetes Engine clusters using templates for production, development, or AI workloads. Compare Google-managed and self-managed node options, review configurations before creation, and check settings for production readiness.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a production Autopilot cluster
• Compare Autopilot and Standard modes
• Prepare a development or test cluster

Review code changes for unnecessary complexity, including unused flexibility, avoidable dependencies, and abstractions with little purpose. Receive concise findings that name what to remove, what can replace it, and how many lines could be cut.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check code changes for over-engineering
• Find code replaceable with standard library features
• Identify unnecessary dependencies and abstractions

Plan and improve AI agent systems by managing the information models receive. This collection covers memory, agent coordination, tool design, context compression, evaluation, and the operating controls needed for reliable autonomous workflows.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning information management for a new AI agent
• Investigating failures in long agent conversations
• Designing agent memory and multi-agent coordination

Builds CARLA's ue4-dev branch on Linux, producing the Unreal Engine 4.26 editor, server modules, and an installed Python client package. Fetches required content, optionally compiles native ROS 2 support, and verifies the build against a source-built server.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building CARLA ue4-dev from source
• Compiling CARLA with native ROS 2 support
• Building the Python client for an existing environment

Evaluate Hugging Face Hub models using inspect-ai or lighteval, with local inference or a provider-backed option. Choose an inference engine, start with a small test, and switch engines when model compatibility requires it.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Run benchmark evaluations on a local GPU
• Choose an evaluation framework and inference engine
• Check a small sample before a full evaluation

Investigate an installed game before making a mod. Identify its engine, available mod loaders, save locations, anti-cheat protection, and compatibility issues, then record a practical approach and unresolved questions in MODDING_PLAN.md.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Check whether an installed game can be modded
• Identify a game's engine and available mod loaders
• Investigate Windows game compatibility on macOS or Linux

Analyze Android packages by decompiling them into readable code. Identify app structure, list API endpoints—the addresses used for network requests—and trace how screen actions reach those requests, producing source files and structured documentation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Investigating the internal structure of Android packages
• Listing API endpoints used by an app
• Tracing screen actions through to network requests

Review how an AI agent loads, retains, summarizes, and shares working information. This historical guide helps examine forgotten constraints, repeated actions, tool selection, and handoffs using context engineering practices published in 2024–2025.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing information loaded at startup versus on demand
• Investigating repeated actions or forgotten constraints
• Planning summaries and persistent task notes

Plan authorized security tests involving phone calls, text messages, physical access, and USB devices. Helps develop scenarios from public information, document interactions, and assess how staff verification procedures and physical controls respond.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning authorized phone and SMS security tests
• Reviewing visitor and badge verification procedures
• Designing USB device security exercises

Set up the repository settings that engineering skills read: where work is tracked, which triage labels to use, and where domain documents belong. Review existing files and confirm drafts before writing configuration documents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prepare a repository before using engineering skills
• Configure GitHub, GitLab, or local Markdown issue tracking
• Match triage labels to an existing workflow

Verify an engine app without a real terminal and record the full interaction as video. Produce a summary page with a clickable timeline, pass or fail checks, and snapshots to review the tested behavior.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Verify text entry and keyboard shortcuts
• Test clicking, dragging, and scrolling
• Record a verification run as video

Cancel the local compatibility-mode state used by Ralph loops in Codex. The bundled PowerShell script removes active state and reports the current iteration, or reports that no active loop state was found.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Canceling local state for ralph-loop --engine compat
• Checking whether active Ralph loop state exists
• Canceling compatibility-mode state in a /vibe session

World-class data engineering skill for building scalable data pipelines, ETL/ELT systems, real-time streaming, and data infrastructure. Expertise in Python, SQL, Spark, Airflow, dbt, Kafka, Flink, Kinesis, and modern data stack. Includes data modeling, pipeline orchestration, data quality, streaming quality monitoring, and DataOps. Use when designing data architectures, building batch or streaming data pipelines, optimizing data workflows, or implementing data governance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Volcengine official documentation lookup helper. Supports both document search and full-content fetch across Volcengine products, developer tools, support content, best practices, pricing, deployment, troubleshooting, API, SDK, and policy pages.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' 'zero-click search,' 'how do I show up in AI answers,' 'LLM mentions,' 'optimize for Claude/Gemini,' 'llms.txt,' 'OKF,' 'Open Knowledge Format,' 'knowledge bundle,' or 'agent-readable site.' Use this whenever someone wants their content to be cited or surfaced by AI assistants and AI search engines. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

When the user wants to optimize content for AI search engines, get cited by LLMs, or appear in AI-generated answers. Also use when the user mentions 'AI SEO,' 'AEO,' 'GEO,' 'LLMO,' 'answer engine optimization,' 'generative engine optimization,' 'LLM optimization,' 'AI Overviews,' 'optimize for ChatGPT,' 'optimize for Perplexity,' 'AI citations,' 'AI visibility,' or 'zero-click search.' This skill covers content optimization for AI answer engines, monitoring AI visibility, and getting cited as a source. For traditional technical and on-page SEO audits, see seo-audit. For structured data implementation, see schema-markup.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.