'mode' 검색 결과

Interactive Domain-Driven Design modeling sessions based on DDD Distilled (Vaughn Vernon) and Domain Modeling Made Functional (Scott Wlaschin). Guides users through strategic and tactical design via 13 phases: discover, storming, contexts, mapping, aggregates, events, validate, glossary, workflows, types, simulate, publish, sync. Phase 13 (sync) reconciles divergences found by --analyze between the domain model and the implementation — judging whether the model or the code is authoritative, then planning and implementing the fix and re-aligning the model. Phases 9-11 take the conceptual model from phases 1-8 and produce workflow pipeline designs, compilable type definitions (TypeScript, Kotlin, Scala, Rust, C#, F#), workflow verification via type-level tests, and automatic UI field enumeration from the domain model. Phase 12 (publish) renders all docs/domain artifacts into one self-contained, cross-linked HTML site (separate files joined by a shared nav). Each phase is an interactive dialogue where AI acts as facilitator and domain expert challenger. Use when: "DDD", "ドメイン設計", "ドメインモデリング", "Event Storming", "Bounded Context", "集約設計", "ユビキタス言語", "コンテキストマップ", "ワークフロー設計", "パイプライン設計", "ステップ分割", "中間型", "型駆動フロー", "型駆動設計", "Domain Modeling Made Functional", "Railway Oriented Programming", "入力画面項目", "フォーム項目洗い出し", "UI 項目抽出", "HTMLにまとめる", "ドキュメントサイト化", "成果物を1つのHTMLに", "publish", "モデルと実装の差異", "差異解消", "実装との乖離", "モデルとコードを一致", "差異の実装計画", "sync", "複数フェーズを逐次実行", "フェーズをまとめて実行", "/ddd run", "サブコマンド一覧", "/ddd list", "/ddd", "ドメイン分析したい", "モデリングしたい", or any domain design / type modeling activity.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Plan and coordinate the deployment of a model to Amazon SageMaker AI. Use this skill whenever the user wants to deploy, host, serve, or expose a model on SageMaker or AWS — including phrases like "deploy a model", "host this LLM on AWS", "serve this embedding model", "deploy a reranker", "deploy a text-to-image / diffusion model", "host this for async inference", "create an endpoint", "serve my fine-tuned model", or any request that involves making a model available for inference on AWS. Use this even when the user is vague (e.g. "I just want to get this running on AWS, you figure it out"). Works for text-generation LLMs, embedding models, rerankers, classifiers, text-to-image / diffusion models — picks the right serving stack and chooses between real-time and async inference. This is the entry-point skill for SageMaker deployment work — it asks clarifying questions, picks a deployment pathway, and coordinates the other deployment skills.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Check the latest Claude (Anthropic), OpenAI Codex, and Google Gemini model releases from official primary sources, update model selections in this dotfiles repo, and update the Claude Code, Codex, and Gemini CLIs through their configured managers. Every run checks both the model settings and CLI for the selected provider, then scans the invoking repository for hardcoded model IDs. Use when the user asks to "モデル更新", "モデルを最新に", "最新モデル確認", "Codex/Claude Code/Gemini CLI本体の更新", "model bump", "update models", or "update agent CLIs". Do NOT use for one-off model selection in a single conversation, general model questions, or unrelated package updates.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Pick the right business-model canvas (Lean Canvas, Business Model Canvas, or Value Proposition Canvas) for the stage and fill it with specifics — one segment, one primary canvas, top-3 assumptions, no fluff in the moat or channel boxes. Load when the user asks to fill a business model canvas, lean canvas, value proposition canvas, model this business, map the business model, says "fill the BMC", "make a Lean Canvas", "Value Proposition Canvas for this", "model this idea", "what's the business model", "design the business model". Sub-skill of `venture-exploration`. Hard-bans "everyone" segments, generic channels ("SEO/social/content/ads"), and "unfair advantage = AI/data/network effects" with no concrete asset. Does NOT score viability — for that use `idea-evaluation`.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Power BI semantic modeling assistant for building optimized data models. Use when working with Power BI semantic models, creating measures, designing star schemas, configuring relationships, implementing RLS, or optimizing model performance. Triggers on queries about DAX calculations, table relationships, dimension/fact table design, naming conventions, model documentation, cardinality, cross-filter direction, calculation groups, and data model best practices. Always connects to the active model first using power-bi-modeling MCP tools to understand the data structure before providing guidance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

대규모 조사나 생성 작업을 여러 AI 모델에 분담해 계획 검토, 병렬 실행, 결과 검증과 통합을 진행합니다. 담당별 진행 상황과 재시도도 기록합니다.

• 여러 경쟁사를 병렬로 조사
• 대규모 생성 작업 분담
• 조언자에게 계획이나 초안 검토 요청

최신 gstack 템플릿에 맞춰 iOS 앱의 디버깅 연결 코드를 다시 생성합니다. 관련 Swift 파일과 추가된 상태를 타입에 맞게 읽고 쓰는 접근자를 갱신합니다.

• gstack 업그레이드 후 디버깅 연결 갱신하기
• 추가한 ViewModels에 대응하기
• 추가 속성의 접근자를 다시 생성하기

Develop machine learning models from local data, covering data preparation, model selection, training, evaluation, and deployment preparation. Use it to build workflows for tasks such as predicting customer churn or classifying images.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a model to predict customer churn
• Training a neural network for image classification
• Preparing missing values and categorical data for training

Use Hugging Face Transformers to load pretrained models, generate text, and classify text, images, or audio. Prepare model inputs, control generation settings, and fine-tune models on custom datasets with the Trainer API.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Prototype text, image, or audio classification
• Configure text generation and conversational responses
• Fine-tune a model on a custom dataset

Complete an existing financial model template by entering historical data and forecast assumptions, then linking the income statement, balance sheet, and cash flow statement with formulas. Check statement balances, cash consistency, and scenario behavior as each section is completed.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Complete a partially filled financial model
• Link forecasts across three financial statements
• Check balance sheet and cash flow consistency

Document a local codebase with file references, architecture diagrams, and verified project commands. For legacy modernization, assess what still runs and create a phased migration plan with validation methods suited to each component’s current condition.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Creating an onboarding guide for a codebase
• Mapping system architecture and dependencies
• Planning a legacy system migration or rewrite

Guide changes and reviews for SGLang’s Scheduler, TokenizerManager, and ModelRunner classes. Keep initialization in small, replaceable helpers and separate computation from coordination so large classes retain clear responsibilities and explicit dependencies.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Reviewing changes to SGLang’s three large classes
• Splitting initialization into replaceable helpers
• Moving computation out of ModelRunner

Monitor requests to an AI prediction API for signs of model copying or training-data exposure. Test an authorized model with simulated attacks, measure the risk, and evaluate defenses such as request limits and reduced prediction detail.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Monitoring a public or partner prediction API
• Measuring model copying risk before deployment
• Testing request limits and reduced prediction detail

Build and evaluate Mamba models for long sequences using state-space models, which summarize past inputs in an internal state. Covers setup, pretrained text generation, differences between Mamba-1 and Mamba-2, and speed comparisons with Transformers.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Generate text with pretrained Mamba models
• Compare Mamba-1 and Mamba-2 configurations
• Benchmark generation speed against Transformers

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports

Evaluate code generation models with HumanEval, MBPP, and other benchmarks. Test generated programs, compare models across programming languages, and record pass@k scores, which estimate the chance that at least one of k solutions passes the tests.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Comparing code generation models on shared benchmarks
• Measuring correctness across programming languages
• Evaluating models trained to follow instructions

Build formula-driven Excel financial models for linked financial statements, business valuation, and fundraising forecasts. Separate assumptions, calculations, and results, compare scenarios, and check that statement balances and saved calculation results are consistent.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build linked income, balance sheet, and cash flow forecasts
• Estimate business value with discounted cash flows
• Compare results under different assumptions

Check a user prompt through an existing Google Model Armor template. Supply the text directly, submit a JSON request, or pass text through standard input, which lets the command read piped content.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking an AI prompt against an existing template
• Passing prompt text from another command
• Submitting a prompt check as a JSON request

Check how much conversation capacity context-mode has saved during the current session. View token consumption, the context savings ratio, and a breakdown by tool in full tables, with one sentence highlighting the main savings figure.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Checking context savings in the current session
• Reviewing token consumption
• Comparing usage statistics across tools

Find previously indexed project files, documentation, and session memory in context-mode's persistent full-text search database. Search related questions together and narrow results to a named project or source without rereading raw material into the conversation.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find information in previously indexed project files
• Look up saved documentation or session memory
• Search several related questions together

Delete stored context-mode data for one session or an entire project after confirming the scope. Remove an unwanted session while preserving others, or clear project knowledge, session records, and statistics for a fresh start.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Remove data from one unwanted session
• Clear a project database containing outdated information
• Reset all stored project data for a fresh start

Plan and analyze A/B tests, prepare tabular data, and train and evaluate prediction models. Estimate intervention effects from observational data, track model experiments with MLflow, and translate statistical findings into business decisions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Planning A/B test sample sizes and analyzing results
• Preparing tabular data for prediction models
• Comparing model performance and recording experiments

Call Gemini and OpenMaaS models such as Llama, DeepSeek, and Qwen through Google Cloud Agent Platform. Prepare authentication and SDK code, run prompts, configure connection endpoints, and troubleshoot common request errors.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Send a test prompt to Gemini
• Call Llama or DeepSeek with the OpenAI SDK
• Write code to call an existing custom endpoint

Assess language models with over 60 academic benchmark tests using consistent prompts and scoring. Compare models, track performance during training, and organize research results with support for HuggingFace models, vLLM, and APIs.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Compare models on standard knowledge and reasoning tests
• Track model performance across training checkpoints
• Organize evaluation results for research reports