'hierarchical' 검색 결과

Automatic graph layout using dagre with React Flow (@xyflow/react). Use when implementing auto-layout, hierarchical layouts, tree structures, or arranging nodes programmatically. Triggers on dagre, auto-layout, automatic layout, getLayoutedElements, rankdir, hierarchical graph.

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Generate hierarchical AGENTS.md structures for codebases. Use when user asks to create AGENTS.md files, analyze codebase for AI agent documentation, set up AI-friendly project documentation, or generate context files for AI coding assistants. Triggers on "create AGENTS.md", "generate agents", "analyze codebase for AI", "AI documentation setup", "hierarchical agents".

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Decision matrix for selecting multi-agent topologies (Supervisor, Swarm, Hierarchical, Conductor) with token economics, failure modes, and escalation paths

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Convert an existing prose, flat, or loosely organized mathematical proof into a source-mapped Lamport-style hierarchy without repairing or completing its mathematics. Use when a submitted proof needs a traceable hierarchical rendering before a forward Lamport audit or for clearer presentation. Do not use to invent a proof, certify validity, or restructure an already hierarchical proof merely to audit it.

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Design state-of-the-art multi-agent systems, orchestration patterns, and wiring from scratch. Load when the user asks to build an agent system from scratch, design agent orchestration patterns, choose between sequential/parallel/hierarchical workflows, or define how multiple agents should collaborate. Also triggers on "agent architecture", "multi-agent wiring", "agent orchestration pattern", "how to connect these agents", "design the communication structure". Called by agent-builder for complex topologies (>4 agents or hierarchical). For process-driven agent design, use agent-builder instead.

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Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

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• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues

여러 AI 에이전트의 방향과 역할을 정하고 진행 상황과 자원 배분을 관리합니다. 상황에 따라 의사 결정 방식을 바꾸고 협업과 담당 업무 인계를 준비합니다.

• 조사·구현·기록 담당을 나눕니다
• 자원 배분과 작업 우선순위를 조정합니다
• 전체 진행 상황과 응답을 확인합니다

여러 AI 코딩 에이전트를 연결하는 Ruflo의 설치와 운영을 안내합니다. 작업 분담, 세션 간 정보 저장과 검색, 상태 확인, 플러그인 선택을 지원합니다.

• 여러 에이전트용 개발 환경을 구성합니다
• 에이전트 간 병렬 작업을 조율합니다
• 세션 간 정보를 저장하고 검색합니다

claude-flow v3 구현을 15개 에이전트에 배분하고 보안, 기반 시스템, 통합, 품질 확인부터 출시까지 의존 관계와 14주 일정을 관리합니다.

• v3 구현을 15개 에이전트에 배분할 때
• 의존 관계에 맞춰 병렬로 개발할 때
• GitHub에서 일정과 진행 상황을 추적할 때

에이전트 작업에서 얻은 지식을 AgentDB에 저장하고 다음 세션에서 검색해 재사용합니다. 기억의 계층 관리, 관련 기억 통합, 인과관계 기록도 지원합니다.

• 이전 세션의 지식을 재사용할 때
• 학습한 패턴을 저장하고 검색할 때
• 관련 기억을 모아 맥락을 정리할 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.

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

Analyze flow cytometry data in Python by correcting fluorescence spillover and applying gates that select cell populations. Produce population counts, percentages, and fluorescence summaries, or reproduce supported FlowJo 10 workspace analyses with recorded analysis settings.

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• Applying the same gating strategy to multiple FCS samples
• Reporting cell-population counts and percentages with denominators
• Reproducing supported FlowJo 10 analyses in Python

Documents a codebase with linked AGENTS.md files that explain each directory, important files, dependencies, and working rules. It can also update existing documentation while preserving human-written notes and checking that the document hierarchy remains consistent.

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• Documenting an unfamiliar codebase for AI agents
• Recording directory-specific rules and dependencies
• Updating documentation after structural changes

Design collaborative AI teams with CrewAI by defining each agent’s role, goals, tasks, and expected results. Covers passing work between agents, manager-led coordination, memory settings, and execution planning for workflows such as research and writing.

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• Build a team that researches and writes articles
• Coordinate specialists through a manager agent
• Define agent roles and expected task outputs

Build collaborating AI teams with CrewAI by defining roles, tasks, and shared context. Configure ordered execution or manager-led delegation, add memory and tools, and create workflows that branch based on events or results.

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• Build a research-to-writing agent workflow
• Coordinate specialists through a manager agent
• Create workflows with conditional branches

Design workflows in which AI agents handle multiple steps or specialist tasks. Choose a suitable structure, generate starter configurations, and define information handoffs, retries, validation checks, and limits on cost and context size.

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• Choosing between one agent and a multi-agent workflow
• Generating a starter configuration for a multi-step pipeline
• Defining information handoffs and error recovery rules

Helps choose and tune Cloud Storage FUSE mounts, which expose bucket objects through file paths. It covers cache sizing, training performance, checkpoint write safety, and cost diagnosis on GKE, Compute Engine, and Cloud Run.

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• Access training data through file paths
• Choose between FUSE, native reads, and shared file systems
• Check checkpoint and concurrent write safety

Explore how coordinating agents can pass relevant working state to workers through KV cache, a model's internal attention memory. Covers task-specific selection, runtime requirements, and comparisons of accuracy, token use, and processing overhead.

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• Reducing repeated history sent to worker agents
• Comparing internal memory sharing with text summaries
• Assessing runtime support for KV cache compaction

Build teams of AI agents with CrewAI, assigning roles such as researcher, writer, and analyst. Guidance covers passing results between tasks, running work in sequence or under a manager, adding memory, and creating workflows with conditional branches.

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• Connecting research, writing, and editing agents
• Coordinating specialist agents through a manager
• Branching workflows based on analysis results

Build and debug Python agents with Swarms, a framework for coordinating multiple AI agents. Covers tool connections, saved memory, autonomous execution, and workflow choices such as sequential steps, parallel work, specialist delegation, and combined answers.

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• Building an agent that calls Python tools
• Coordinating sequential or parallel agent workflows
• Saving agent memory across restarts

Turn content into AntV Infographic syntax, a text format for describing infographics. The skill organizes titles, items, relationships, and metrics, then selects a suitable template for sequences, lists, comparisons, hierarchies, or charts.

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• Turn procedural text into sequence syntax
• Structure a two-sided comparison
• Describe hierarchical information

Coordinate 15 agents implementing claude-flow v3 across security, core systems, integration, testing, performance, and release work. Provides role assignments, task dependencies, GitHub tracking examples, and a four-phase schedule spanning 14 weeks.

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• Assign claude-flow v3 work across 15 agents
• Run tasks in parallel while respecting dependencies
• Track milestones and progress through GitHub issues

Automatically send Claude Code conversations and tool calls to Braintrust. View nested activity records that connect each session, conversation exchange, and operation, including file reads, edits, and terminal commands, to inspect how coding work unfolded.

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• Review the flow of a Claude Code session
• Inspect tool calls alongside conversation exchanges
• Set up automatic session logging in Braintrust

Compute federated statistics over tabular data (count, sum, mean, stddev, var, histogram, quantile, noise-protected min/max) and image data (count, failure_count, pixel-intensity histogram) across NVFLARE sites via FedStatsRecipe — automatic and non-interactive from the dataset, feature names (header or supplied), and optionally a README or notes declaring which statistics to compute; do not use for model training conversion, hierarchical statistics, deployment, POC/production lifecycle, or failed-job diagnosis.

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