'code generation' 검색 결과

Loads org- and repo-level coding rules from Qodo before code tasks begin, ensuring all generation and modification follows team standards. Use before any code generation or modification task when rules are not already loaded. Invoke when user asks to write, edit, refactor, or review code, or when starting implementation planning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Marp形式プレゼンテーションの専門レビュー・評価・改善スキル。 10軸スコアリング（100点満点）で品質を定量評価し、具体的な改善コード付きで提案する。 Use when the user asks to: - Review, evaluate, or critique a Marp presentation / プレゼンをレビュー / 評価 / 添削 - Improve, polish, or upgrade existing slides / スライドを改善 / ブラッシュアップ - Check slide quality, score slides, or audit a deck / スライドの品質チェック / 採点 - "このプレゼンどう？" "スライドを見てほしい" "プレゼン資料のフィードバック" Reads .md files with Marp frontmatter, scores across 10 dimensions, and outputs a detailed review report with concrete before/after code improvements. Works as a companion to the marp-slides skill (generation → review → improvement cycle).

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Create Kotlin Compose design tokens from one of three supported sources — Google Stitch (MCP), Figma (MCP or JSON), or Codex Design (kta-design-spec output, Codex HTML/markdown artifacts). Three-layer architecture (Primitive/Semantic/Component) with code generation. Use when user asks to "generate design tokens", "create theme from Figma/Stitch/Codex design", "build Compose tokens from design spec", or "convert design source to tokens". Refuses any other input source and asks user for correct seed data.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Expert en conception de compilateurs (lexing, parsing, AST, code generation, LLVM, interpreters)

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Guarantee valid JSON/XML/code structure during generation, use Pydantic models for type-safe outputs, support local models (Transformers, vLLM), and maximize inference speed with Outlines - dottxt.ai's structured generation library

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Elite website image-to-code skill for Codex. For visually important web tasks, it must first generate the design image(s) itself, deeply analyze them, then implement the website to match them as closely as possible. In Codex, it must prefer large, readable, section-specific images instead of tiny compressed boards, generate fresh standalone images for sections or detail views instead of cropping old ones, avoid lazy under-generation, avoid cards-inside-cards-inside-cards UI, and keep the hero clean, spacious, readable, and visible on a small laptop.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Loads org- and repo-level coding rules from Qodo before code tasks begin, ensuring all generation and modification follows team standards. Use before any code generation or modification task when rules are not already loaded. Invoke when user asks to write, edit, refactor, or review code, or when starting implementation planning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

색상·간격·글꼴을 공통 설정으로 정리하고 CSS 변수를 생성합니다. 화면 구성 요소의 상태별 규격과 코드의 직접 지정 값을 확인하며, 같은 브랜드 규칙으로 HTML 발표 자료를 만듭니다.

• 색상·간격·글꼴의 공통 기준 정리
• 버튼의 상태별 규격 정의
• 코드에 직접 지정한 스타일 값 확인

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Generate, compose, and edit product images, posters, illustrations, and social graphics through the paid Beatra service. The workflow covers reviewing upload files and estimated costs before each paid call, then checking results and actual charges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating product images
• Designing posters and social graphics
• Editing reference images and making photo variations

Create and maintain OpenAPI 3.1 specifications from existing code or initial designs. Use these API descriptions to prepare documentation, generate client SDKs—libraries for calling an API—and check whether implementations match their specifications.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create API documentation from existing code
• Define an API specification before implementation
• Generate client SDKs from API specifications

Write or revise instructions, specifications, tickets, and skill files so an agent can act without prior chat history. Make rules checkable, explain their reasons, define completion criteria, and keep authoritative information in one place.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Clarifying instructions in CLAUDE.md
• Making specifications and tickets actionable
• Adding completion criteria to skill procedures

Build and debug sglang tests comparing token probabilities during prompt processing and step-by-step generation. Separate numerical differences from reused-state bugs, choose test thresholds, and locate the first operation that diverges.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding probability consistency tests for a model
• Choosing or justifying a kl_div threshold
• Separating numerical differences from cache state bugs

Add a diffusion model or Diffusers generation pipeline to SGLang. Examine reference code, choose a suitable pipeline structure, reuse existing components, and implement model settings and processing stages, with guidance for later multi-GPU support.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding a new diffusion model to SGLang
• Porting a Diffusers generation pipeline
• Adding a variant by reusing existing model components

Guide reinforcement learning, training models through rewards, with slime. Prepare data, configure Megatron-LM training and SGLang response generation, and set up asynchronous runs or custom multi-turn workflows for supported language models.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Preparing reward-based training for GLM or Qwen models
• Overlapping response generation and training
• Training agents that use tools across multiple turns

Review code for privacy risks, prepare data protection impact assessments, and track personal data requests and deadlines. The skill supports GDPR and German privacy compliance work, with final compliance decisions referred to a data protection officer or legal counsel.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking a service's code for privacy risks
• Preparing a data protection impact assessment
• Tracking deadlines for access and erasure requests

Generate photos, illustrations, product mockups, and other bitmap images, or edit existing pictures with background changes, object removal, and transparent cutouts. Use reference images to guide variations and save selected assets for the project.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create a website hero image or illustration
• Replace a product photo background or remove objects
• Make a transparent cutout

Turn game design documents and character profiles into asset specifications and image generation prompts. Define appearance, dimensions, file formats, and naming rules, then maintain an asset list that tracks progress and identifies shared assets.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• List the visual assets needed for a game
• Define consistent character and environment asset specifications
• Prepare image generation prompts from an art direction guide

Helps build AI features in Go with Genkit, including text generation, structured data output, streaming, tool calls, and conversational agents. Guides HTTP serving and trace inspection to check model responses and tool execution.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Adding text generation or structured output to a Go app
• Building a chatbot that retains conversation state
• Connecting model responses to tool calls

Collect social posts, business listings, and product reviews through Apify actors, ready-made data collection tools. Filter results by date, rating, or engagement in code before sending relevant records to the AI for research or lead generation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find competitors' most popular social posts
• Build a contact list of local businesses
• Extract recent negative Amazon reviews

Build a Three.js 3D model from an object or character reference image using code. The workflow develops shapes and materials in stages, compares rendered views with the reference, and guides corrections through quality checks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Rebuilding a photographed object as a browser-based 3D prop
• Creating a stylized character from a reference image
• Planning model parts, materials, and animation pivots