'flow' 검색 결과

Design multi-agent workflows for research, reviews, and migrations. Organize work across different evidence sources, add independent checks, and combine findings while preserving source references, unresolved questions, and limits on what was examined.

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• Plan research across multiple evidence sources
• Review implementation and tests from different angles
• Compare candidate solutions with independent judges

Manage n8n automation workflows from the command line. Create, update, and activate workflows, inspect or retry executions, and manage credentials, variables, and tags through n8n's public API with structured JSON output.

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• Create or update an n8n workflow
• Activate or deactivate an automation workflow
• Inspect execution history and retry a run

Assess Airflow Improvement Proposals by comparing Confluence specifications with pull requests, commits, and codebase files. Produce evidence-backed progress reports showing delivered items, remaining work, and specification updates needed across one or more proposals.

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• Check implementation progress for an Airflow proposal
• Compare specification deliverables with shipped code
• Compile a progress report across multiple proposals

Test Temporal workflows in Python with pytest. Use time skipping to avoid long waits, replace activities with controlled test versions, and replay execution histories to check that workflow changes remain consistent with previous runs.

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• Testing long-running workflows without waiting
• Testing workflow logic with mocked activities
• Checking workflow changes against execution histories

Analyze current Git changes to choose a branch for a feature, release, or urgent fix. Create a descriptive name and switch to the new branch using Git Flow, a structured approach to managing development branches.

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• Create a branch for a new feature
• Separate release preparation changes
• Create a branch for an urgent production fix

Turn an existing GitHub Actions workflow into a structured specification for maintenance and AI reference. Document its purpose, triggers, job dependencies, inputs, outputs, permissions, and failure handling, focusing on expected behavior rather than implementation details.

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• Documenting an existing GitHub Actions workflow
• Reviewing job order and dependencies
• Organizing permissions and failure handling

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

Builds a 30/60/90-day cash flow forecast from accounting records, payment services, or CSV data. Uses historical payment timing to estimate uncertainty, flags specific cash risks, and delivers a chat summary, forecast page, and downloadable Excel workbook.

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• Check whether cash will cover the next payroll
• Review cash flow across three 30-day windows
• Identify invoices whose late payment could cause a shortfall

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

Use n8n-mcp tools to find workflow steps, check settings, and create or update automations. Provides guidance on tool arguments, templates, folders, credentials, and security audits so agents can choose appropriate operations.

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• Finding nodes and checking their settings
• Creating and updating workflows
• Finding and deploying workflow templates

Create and manage Volcano jobs and scheduling queues in KubeSphere. Use YAML templates for PyTorch, TensorFlow, MPI, and batch workloads, configure resource requests and execution policies, and investigate jobs that remain pending or fail to create pods.

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• Prepare distributed training jobs for PyTorch or TensorFlow
• Create MPI and batch processing jobs
• Configure queue capacity and scheduling weight

Monitor GitHub Actions builds and tests with the gh command-line tool. Track runs until completion, inspect branch or pull request checks, read failed job logs, and rerun failed jobs or entire workflows.

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• Watch a build or test run until completion
• Check workflow status for a branch or PR
• Read logs from failed jobs

Helps investigate and modify Apache Beam's automated build and test workflows in GitHub Actions. Explains workflow types, failure logs, targeted reruns, local reproduction, and the cloud credentials needed for workflows that access GCP.

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• Investigating failed checks on a pull request
• Rerunning a specific test workflow
• Reproducing CI test commands locally

Turn plain-language descriptions or Mermaid diagrams into interactive, standalone HTML diagrams of systems, workflows, API exchanges, data flows, and state changes. Validate layouts, explore relationships, switch themes, and export diagrams as images or video.

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• Explain system components and their relationships
• Visualize workflows and approval steps
• Map API calls and data pipelines

Use n8n-mcp tools to find workflow steps, check settings, and create or update automations. Provides guidance on tool arguments, templates, folders, credentials, and security audits so agents can choose appropriate operations.

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• Finding nodes and checking their settings
• Creating and updating workflows
• Finding and deploying workflow templates

Manage an assigned Worker task from acceptance through delivery using taskflow. Keep work and optional progress notes in the task directory, submit structured results with deliverable paths, and report completion or blockers to the coordinator.

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• Accept an assigned task and read its requirements
• Organize deliverables and progress notes by task
• Submit results with completion or revision status

Review GitHub agentic workflows, automated workflows guided by AI agents, for security and correctness. Compile changes, inspect permissions and safeguards, and use available run logs to identify supported improvements to cost and execution time.

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• Reviewing workflow permissions and security safeguards
• Checking workflow compilation and security scan results
• Finding cost and runtime improvements in execution logs

Coordinate multiple AI agents with agentic-flow to run tasks in parallel or through ordered stages. Configure shared memory, team structure, workload distribution, retries, and task reassignment, then monitor how the group performs.

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• Run API, interface, and test work in parallel
• Build a design-to-review workflow with ordered stages
• Share specifications and state across agents

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

Manage Flow Nexus accounts, isolated cloud environments for running code, and application deployment. The skill also covers publishing templates, tracking credits and payments, submitting coding challenges, managing stored files, and checking execution logs.

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• Create a cloud environment and run code
• Publish an app or deploy a template
• Check credit balances and configure automatic refills

Create and run BB workflows that coordinate multiple worker agents. Organize independent tasks into parallel stages, verify outputs, pass structured results between steps, and inspect execution progress using validated JavaScript workflow scripts.

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• Review multiple changes and verify each finding
• Process independent items through several stages
• Pass structured results between worker agents

FLOW framework integration for bloggers. Evidence-led content workflow using the Find, Optimize, Win loop with stage-specific AI prompts from the FLOW knowledge base (30 blog-applicable prompts, CC BY 4.0). Use when user says "FLOW", "FLOW framework", "blog flow", "evidence-led blogging", "find optimize win", or wants stage-specific blog prompts.

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Defines cell populations in flow and spectral cytometry through manual gates (rectangle, polygon, quadrant, boolean) and reproducible automated gating (openCyto gating templates, flowDensity data-driven thresholds, flowClust model-based gates), organized as a hierarchical GatingSet (flowWorkspace) and round-tripped with FlowJo via CytoML. Covers the canonical gate order (time -> debris -> singlets -> live -> lineage), FMO-vs-isotype boundary setting, gate-order dependence and recompute semantics, rare-event/MRD gating, and per-population statistics. Use when building a gating strategy, automating a manual FlowJo scheme across samples, choosing manual vs data-driven gates, or extracting population frequencies.

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Reads, inspects, and writes Flow Cytometry Standard (FCS) files from conventional, spectral, and mass cytometry (CyTOF), and parses FlowJo/Cytobank/Diva workspaces. Covers FCS 2.0/3.0/3.1/3.2 internals ($PnE linear-vs-log, $DATATYPE, $SPILLOVER vs SPILL vs $COMP, $TIMESTEP), channel/parameter metadata, the silent linearize/truncate defaults, and R (flowCore, flowWorkspace, CytoML) plus Python (FlowKit, readfcs) readers. Use when loading flow or mass cytometry data, mapping detector channels to antibodies, extracting the event matrix, choosing a reader, or bridging FCS to the scanpy/AnnData ecosystem before preprocessing.

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