'dag' 검색 결과

React Flow (ノードベース UI / フロー図ライブラリ for React) リファレンス。 ReactFlow コンポーネント、Node, Edge, Handle, Viewport、 カスタムノード / エッジ、Controls / MiniMap / Background、 レイアウトアルゴリズム (dagre, elkjs)、ドラッグ&ドロップ、エッジ接続。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Ranks eligible skill candidates with explicit retrieval evidence, capability coverage, and uncertainty. NOT for discovery, authorization, or execution planning.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Records and evaluates event-specific probability forecasts for agent outputs. NOT for validation or automatic acceptance.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Monitors a specified object against explicit stopping criteria and distinguishes replica convergence, iterative progress, and workflow terminal state. NOT for execution or setting goals.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Choose, compare, and decompose work into the right coordination topology: dag, workflow, manager-driven team, swarm, blackboard, team-builder, or recurring loop. Use when the coordination shape is unclear, when you need topology-native breakdown instructions instead of a generic DAG, or when planning topology and runtime topology may differ. NOT for actual execution, node-level skill matching, or framework/tool installation.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

RFCドリブンのマルチエージェントDAG実行パターン。品質ゲート、マージキュー、ワークユニットオーケストレーションを含む。

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

自律型Claude Codeループのパターンとアーキテクチャ — シンプルなシーケンシャルパイプラインからRFC駆動のマルチエージェントDAGシステムまで

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Build production Apache Airflow DAGs with best practices for operators, sensors, testing, and deployment. Use when creating data pipelines, orchestrating workflows, or scheduling batch jobs.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

Multi-agent task orchestration pack. DAG-based parallel task execution and team status monitoring. 2 orchestration tools.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.

대규모 기능의 설계 제안을 의존 관계와 통과 기준이 있는 작업으로 나눕니다. 여러 에이전트의 조사, 구현, 테스트, 검토부터 통합과 최종 검증까지 정리합니다.

• 큰 설계 제안을 검증 가능한 작업으로 나눕니다
• 여러 에이전트에 의존 관계가 있는 일을 배정합니다
• 변경 브랜치를 검토하고 순서대로 통합합니다

Claude Code에서 개발 작업을 반복하는 자율 루프 설계를 안내합니다. 순차 실행부터 병렬 작업까지 진행 상황 인계, 품질 확인, 변경 사항 통합과 중단 조건을 정리합니다.

• 순차적으로 실행할 개발 절차를 설계할 때
• 반복 실행 사이에 진행 상황을 인계할 때
• 병렬 작업과 변경 사항 통합을 조율할 때

Document a project's structure, commands, and specific conventions in a hierarchy of AGENTS.md files. Analyze directory complexity to choose useful locations, update existing guidance, and check that child files avoid repeating their parents.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Create project guidance for coding agents
• Refresh existing AGENTS.md documentation
• Document rules specific to complex directories

Search Danish job listings on Jobdanmark.dk by role, location, and employment type. Retrieve matching vacancies, read posting details and application deadlines, or view current job counts across categories to explore available work.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Find full-time IT jobs in Copenhagen
• Look for student jobs in Aarhus
• Check a job description and application deadline

Search Denmark’s official Jobnet.dk portal by occupation, region, distance, working hours, or employment duration. Retrieve full job descriptions, employer contacts, deadlines, and application links, and explore Danish job titles to refine searches.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Finding Danish jobs by occupation and region
• Searching within a set distance of a postal code
• Filtering by working hours and employment duration

Build machine learning workflows that connect data checks, training, evaluation, and deployment. The skill implements experiment tracking, model version management, and automated retraining, with reproducible environments and explicit validation thresholds before models enter production.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Automate model training and retraining workflows
• Record and compare machine learning experiments
• Build a feature store for model input data

Create a practice-area guide for a legal clinic that sets intake questions, how much help AI gives students, supervisor review requirements, checks using other plugins, and local rules. The guide configures how student-facing skills behave.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Set intake questions for each clinic practice area
• Adjust AI assistance to match teaching goals
• Define which student outputs require supervisor review

Helps a QwenPaw Worker choose between direct work, short parallel checks, and temporary agents with custom roles. It guides dependent task execution, shared files, progress updates, result merging, and removal of temporary agents after the work ends.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Choosing how to split work within one Worker
• Running parallel reviews with specialized roles
• Executing tasks with dependencies

Assign ready tasks from a project plan to Worker agents and check their submitted results. The skill defines task specifications, file ownership, and result statuses, helping a coordinator decide whether to accept work or plan follow-up tasks.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Assign a ready project task to a Worker
• Check submitted outputs and result statuses
• Define ownership of task files

Build a reproducible Stata analysis from data cleaning through modeling, diagnostic checks, and robustness tests. Generate runnable .do files, regression tables, and figures for empirical papers, with additional workflows for epidemiology and machine learning approaches to causal inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building a reproducible Stata analysis pipeline
• Comparing regression specifications in a paper table
• Checking causal estimates with robustness tests

Build a Python workflow for empirical research, from data cleaning and statistical checks to estimation, robustness checks, and paper-ready tables and figures. Economics is the default, with additional modes for epidemiology, public health, and machine learning for causal inference.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Building an end-to-end empirical analysis workflow in Python
• Replicating an applied economics paper
• Producing regression tables and event-study plots

Build reproducible empirical analyses in R, from data cleaning to statistical modeling and publication tables and figures. Covers economics, epidemiology, and machine learning approaches to causal inference, which estimates effects of interventions.

한국어 요약은 준비 중입니다. 영어 요약을 표시하고 있습니다.

• Build a reproducible R or Quarto analysis workflow
• Compare regression models with progressively added controls
• Test robustness across samples and statistical methods

Run the PAIDF Orchestration Event Video Generation DAG on Kubernetes - image-to-video anomaly generation, auto-labeling, and anomaly dataset generation. Select for requests about event video generation, anomaly video generation, image-to-video synthesis, Cosmos3 image2video, anomaly dataset creation, safety/surveillance SDG, or generating person-falling, person-climbing, person-running, fighting, smoking/vaping, fire/smoke, or shoplifting video clips from a seed image. Runs environment setup first when controller readiness is unknown. Not for person-crop clothing/attribute augmentation (that is image-attribute-augmentation-workflow) and not for video style transfer.

한국어 요약은 준비 중입니다. 원문 설명을 표시하고 있습니다.