'flow' 검색 결과

Build Apache Airflow data pipelines with clear task dependencies and schedules. This skill guides workflow design, retries, monitoring, local testing, and production deployment, with patterns for investigating failed runs.

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• Designing data pipelines and task dependencies
• Scheduling recurring batch jobs
• Testing Airflow workflows locally

Build Solana apps with Phantom wallet connection, message and transaction signing, token-based access, NFT minting, and crypto payments. Add DFlow token swaps and live price or order-book displays, showing current buy and sell orders.

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• Add Phantom wallet login to an app
• Build pages restricted to token holders
• Implement token swaps with DFlow

Build workflows with Vercel's Workflow DevKit that preserve progress and resume after restarts. Structure operations as persistent steps, wait for external events, retry temporary failures, and inspect runs to understand how a process is progressing.

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• Building processes that resume after restarts
• Pausing a workflow until an external event arrives
• Retrying temporary failures in individual steps

Authors and deploys MWAA workflow artifacts: Python Airflow DAGs for provisioned environments or YAML workflow files for Serverless. Covers operator selection, timeout design, retry strategy, scheduling, failure notifications, idempotency, and MWAA Serverless schema compliance. Deploys the artifact (S3 DAG upload or Serverless CreateWorkflow/UpdateWorkflow), creates an environment inline when approved, and redeploys fixes, then optionally hands off to testing-mwaa-workflow. Triggers on: create a DAG, write a pipeline, build a workflow, orchestrate tasks, Airflow DAG, data pipeline, schedule a job, deploy a DAG, deploy a workflow, YAML workflow. Not applicable to converting or migrating existing DAGs between provisioned and serverless (conversion is out of scope), running or smoke-testing a deployed workflow (handled by testing-mwaa-workflow) or diagnosing a failed run (handled by debugging-mwaa-workflow).

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Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

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Automate Anonyflow operations through Rube MCP, a connection for agent tools. The skill guides agents through finding available tools, checking authentication status, and executing operations with the required inputs.

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• Finding available Anonyflow tools and required inputs
• Checking authentication before running operations
• Fetching all pages of a result

Build, run, and troubleshoot data processing pipelines with Nextflow and nf-core. The skill guides modular workflow development, input preparation, testing, cached restarts, and configuration for local computers, computing clusters, or cloud environments.

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• Run an nf-core workflow with a samplesheet
• Build and test reusable DSL2 modules
• Investigate failed runs and restart with cached results

Automate Anonyflow operations through Rube MCP, a connection for agent tools. The skill guides agents through finding available tools, checking authentication status, and executing operations with the required inputs.

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• Finding available Anonyflow tools and required inputs
• Checking authentication before running operations
• Fetching all pages of a result

Design n8n automations for service integrations, scheduled tasks, databases, and AI agents. Choose from six workflow patterns, plan data flow and error handling, check connections before activation, and improve workflows that process large numbers of items.

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• Send form submissions to Slack
• Email a scheduled analytics report
• Synchronize records between databases

Author and review GitHub Actions workflow YAML safely so syntactically-valid YAML can't ship a workflow that GitHub Actions refuses to run. USE FOR: editing, adding, or reviewing any file under .github/workflows/, writing run-name/name/if/env/run values that contain ${{ }} expressions, diagnosing a run that fails with 'This run likely failed because of a workflow file issue' and no jobs starting, deciding when a workflow scalar must be quoted, validating workflows with actionlint. DO NOT USE FOR: authoring application YAML unrelated to GitHub Actions, Azure Pipelines, GitLab CI, or non-workflow YAML. SCOPE: this skill covers *syntactic/structural* correctness of workflow YAML (quoting, parsing, actionlint); for *semantic and functional* workflow design (what a workflow should do, agentic-workflow behavior), see .github/agents/agentic-workflows.agent.md — the two are complementary. INVOKES: actionlint (downloaded pinned binary) plus git/grep for inspection.

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Refactor claude-flow into an extension of agentic-flow@alpha to reduce duplicate implementations. Plan adapter layers, migrate agent and task management, and connect learning, shared memory, and tools while checking compatibility with existing functionality.

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• Consolidate duplicate agent and task management code
• Design an adapter that preserves existing APIs
• Integrate learning, shared memory, and MCP tools

Refactor claude-flow into an extension of agentic-flow@alpha to reduce duplicate implementations. Plan adapter layers, migrate agent and task management, and connect learning, shared memory, and tools while checking compatibility with existing functionality.

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• Consolidate duplicate agent and task management code
• Design an adapter that preserves existing APIs
• Integrate learning, shared memory, and MCP tools

Helps add or update translations for the Apache Airflow interface. Covers language setup, translation file generation, consistent terminology, preserved variables and plural forms, and checks for missing, unfinished, or unused entries.

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• Adding a new interface language to Airflow
• Filling gaps in an existing translation
• Aligning terminology and preserving translation variables

Build Apache Airflow workflows for data pipelines and scheduled jobs. The skill guides task dependencies, retries, waiting for conditions, testing, and deployment, with patterns for tasks that can be safely rerun.

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• Building scheduled data pipelines
• Designing task order and dependencies
• Implementing custom tasks and condition checks

Use this skill when the user is tuning a live or captured `doca-flow` pipeline with `doca_flow_tune` — snapshotting pipe / counter / KPI state, picking a tuning axis (rule placement, resource hints / table sizing, HW-offload mode) and a matching measurement (rule-install rate, lookup latency, hardware-counter delta), running offline or online (read-only or state-changing) modes, reading the dumper CSV / analyze JSON / visualize mermaid, or applying a recommendation back into the Flow program. Trigger even when the user does not explicitly mention "doca_flow_tune" — typical implicit phrasings include "Flow rule-install rate is low on BlueField", "table sizing looks wrong for this pipe", "tune visualize step is empty", "before/after counters don't move", or "which doca-flow knob does this recommendation hit". Refuse and route elsewhere for measuring baseline numbers (doca-flow-perf, doca-flow-dpa-perf), writing the doca-flow application, DOCA install, or streaming Flow telemetry — those belong to other skills.

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Tests Amazon MWAA workflow execution end-to-end: trigger a run and monitor it to completion for Provisioned (Python DAG, via Airflow REST API) and Serverless (YAML workflow, via StartWorkflowRun). Verifies the artifact is deployed and parse-ready, triggers with confirmation, polls to terminal state, and on failure delegates diagnosis to debugging-mwaa-workflow and artifact/redeploy fixes to authoring-mwaa-workflow, then retests up to a capped number of attempts. Triggers on: test my DAG, test my workflow, run my DAG, trigger a test run, does my DAG work, smoke-test the pipeline, verify my workflow runs, execute my DAG to check it. Not applicable to writing or deploying a new workflow (handled by authoring-mwaa-workflow), or for diagnosing why a run failed or root-causing an error (handled by debugging-mwaa-workflow).

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Use when authoring a Relayflows flow (@relayflows/surface / @relayflows/sdk, the journal-based v2 engine — the CLI is `flows`, package versions 2.0.x) in TypeScript or YAML/JSON. Covers the three-rung ladder (run/llm/agent), the resident verbs (human/dispatch/done), verification gates, TypeScript vs YAML authoring, per-step cli/model selection and its resolution order, flows.json, and `flows check`/`run`/`resume` with their real refusal shapes and exit codes. Not for the older, unrelated `@relayflows/core` WorkflowBuilder engine (`.pattern('dag')`/.agent()/.step() chains) that `writing-agent-relay-workflows` and `migrating-persona-to-relayflow` cover — that's a different product despite the similar name.

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Build interactive "living workflow" flowcharts in the visual style of https://statuesque-starship-49757d.netlify.app/revenue-engine - vanilla HTML/CSS/JS files with a horizontal step bar, a vertical trunk that grows per step, tool-to-tool columns with animated payload pills between brand-marked tool nodes, alternating left/right step descriptions, and per-step outputs. Use whenever the user says "build a flowchart", "interactive flowchart", "living workflow", "revenue engine flowchart", "demand gen flowchart", "GTM workflow diagram", "process diagram with animations", "Nebor-style flowchart", or asks to recreate the workflow visual from the Nebor site. Always asks which flowchart to build (Revenue Engine / Demand Gen / CRM & RevOps / Custom) and waits for explicit approval of the step + tool + edge table before generating any files.

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Use when deciding which Salesforce automation tool should own a requirement, including before-save and after-save Flow, screen Flow, scheduled Flow, invocable Apex, Apex triggers, and migration off Workflow Rules or Process Builder. Triggers: 'Flow or Apex trigger', 'which automation tool should I use', 'Process Builder migration', 'Workflow Rule retirement', 'same-record update or trigger', 'automation decision record', 'why did we reject Apex', 'one object one order', 'automation inventory for this object', 'scheduled Flow or Batch Apex'. NOT for 'which tool should stamp this field on save, and will it recurse' — use admin/workflow-field-update-patterns. NOT for actually running the conversion of an existing Process Builder — use flow/process-builder-to-flow-migration.

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Interact with DeerFlow AI agent platform via its HTTP API. Use this skill when the user wants to send messages or questions to DeerFlow for research/analysis, start a DeerFlow conversation thread, check DeerFlow status or health, list available models/skills/agents in DeerFlow, manage DeerFlow memory, upload files to DeerFlow threads, or delegate complex research tasks to DeerFlow. Also use when the user mentions deerflow, deer flow, or wants to run a deep research task that DeerFlow can handle.

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Coordinate groups of AI agents in the cloud with Flow Nexus. Assign research or development tasks, automate workflows triggered by events or schedules, and monitor execution, queues, and agent capacity through the platform's tools.

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• Split research and analysis across multiple agents
• Coordinate development across repositories
• Automate testing, builds, and deployment

Create, inspect, edit, and validate Dify workflow definition files from the command line. This wrapper provides access to node and connection edits, model and prompt settings, file comparisons, and import or export through the upstream CLI.

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• Create a Dify workflow from a template
• Inspect and validate workflow definition files
• Edit nodes and their connections

Build and operate bioinformatics workflows on Latch using Python, Nextflow, or Snakemake. Configure computing resources and input forms, move data, connect Registry records, and register, debug, launch, and monitor analysis runs.

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• Create and register Python analysis workflows
• Package Nextflow or Snakemake pipelines for Latch
• Connect Latch Data and Registry records

Build and operate genomics workloads on DNAnexus using its command-line tools and Python SDK. The skill covers data transfers, app configuration checks, workflow imports, execution monitoring, and explicit cost controls for reproducible analysis workflows.

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• Transfer and organize genomics data on DNAnexus
• Validate app configuration and build applets
• Import WDL, CWL, or Nextflow workflows