'flow' 검색 결과

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

Design long-running processes across services with Temporal, a system that preserves workflow progress. Covers separating coordination from external operations, handling retries and failures, and structuring workflows so they can resume after interruptions.

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• Designing order or booking processes across services
• Building approval flows with timeouts and escalation
• Planning recovery for distributed transactions

Use MLflow to record machine learning experiments, compare results, and manage model versions. The skill covers logging training settings, metrics, and files, organizing a model registry, and preparing models for local or cloud deployment.

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• Comparing training settings and model performance
• Recording experiments for later reproduction
• Managing model versions and release stages

Design n8n automations for incoming events, API connections, database synchronization, AI agents, scheduled tasks, and batch processing. Choose a suitable structure, plan error handling, check connections, test results, and review performance when processing large numbers of items.

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• Sending form submissions to Slack
• Generating and emailing scheduled reports
• Synchronizing records between databases

Organize SEO work into five stages, from keyword research and backlinks to page improvements, conversions, and local search. Apply a library of 41 prompts, selecting relevant guidance for the page and its current challenges.

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• Research keywords and gaps in search results
• Plan backlinks and off-site authority
• Choose improvement prompts for a specific page

Adjust how AI agents generate workflows in cc-wf-studio by reviewing node descriptions, examples, and misleading constraints. The skill guides minimal schema edits, format regeneration, and testing to address biased node choices.

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• Investigating repeated selection of the wrong node type
• Revising node descriptions and workflow examples
• Checking for constraints the runtime does not enforce

Remote control of DOCA Flow over gRPC with the shipped `doca_flow_grpc` server: program Flow pipes and entries from Python, Go, Rust or Java, or from another host, instead of linking `libdoca_flow.so`. PLAINTEXT-ONLY: the server uses `grpc::InsecureServerCredentials()` with NO TLS / mTLS / token-auth knob; transport security must come from external infrastructure (e.g. an mTLS proxy / sidecar) on a trusted segment. Trigger even without "gRPC": "program Flow rules from Python on another host", "remotely configure pipes on the BlueField", "client times out connecting to the Flow server", "where is the .proto for Flow", "UNAUTHENTICATED / FAILED_PRECONDITION on a Flow RPC". Covers bringing up, configuring, hardening and debugging the server. Route elsewhere for the underlying doca-flow C API, generic gRPC tooling (protoc, language bindings), or DOCA install / BFB bring-up.

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Diagnoses and root-causes Amazon MWAA workflow failures across Provisioned (Python DAG) and Serverless (YAML workflow) environments. Provisioned uses aws mwaa invoke-rest-api, CloudWatch log groups, and get-environment; Serverless uses aws mwaa-serverless API (GetWorkflowRun, ListWorkflowRuns, GetTaskInstance) and CloudWatch logs. Covers failed runs and tasks, DAGs not appearing, import errors, worker OOM, IAM denials, and dependency drift. Triggers on: DAG failed, task failed, workflow run failed, MWAA error, debug my DAG, why did my workflow fail, DAG not showing up, MWAA import error, requirements failing, worker crashed, serverless run failed. Not applicable to authoring workflows (handled by authoring-mwaa-workflow), running or smoke-testing a workflow (handled by testing-mwaa-workflow), or CI-CD deploy failures.

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Expert guidance for Salesforce Flow implementation from design through deployment. Use this skill when building Screen Flows, Record-Triggered Flows, Schedule-Triggered Flows, or Autolaunched Flows. Covers Flow design patterns, metadata XML generation, variable/element reference validation, governor limit optimization, and sf CLI deployment. Critical for preventing reference errors (undeclared variables, invalid element references) and deployment failures. Use when creating new Flows, debugging Flow errors, optimizing Flow performance, or deploying Flows to production.

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Install and authenticate the flowctl CLI for Estuary. Use when setting up flowctl for the first time, troubleshooting authentication, upgrading to the latest version, or configuring programmatic access for CI/CD. Use when user says "install flowctl", "set up flowctl", "flowctl auth", "how to authenticate", "token expired", "not authenticated", "FLOW_AUTH_TOKEN", "CI/CD setup", "programmatic access", "upgrade flowctl", "flowctl not found", "command not found flowctl", "Exec format error", or "flowctl on Windows".

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Use when: User wants to run Archon workflows, CREATE workflows or commands, set up Archon, or manage Archon configuration. Triggers (run): "use archon to", "run archon", "archon workflow", "use archon for", "have archon", "let archon", "ask archon to". Triggers (create): "create a workflow", "write a workflow", "make a command", "author a workflow", "new workflow", "new command", "archon workflow yaml". Triggers (setup): "set up archon", "install archon", "how to use archon", "configure archon", "archon setup", "get started with archon". Triggers (config): "change my archon config", "modify archon config", "archon config", "change archon settings", "update my config", "help me change my config", "edit archon config", "archon configuration". Triggers (init): "initialize archon", "set up .archon", "archon init", "add archon to repo". Capability: Runs AI workflows in isolated git worktrees for parallel development. Also: Creates and manages workflow YAML files, command files, and configuration. NOT for: Direct Claude Code work - only for delegating to Archon CLI.

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Helps contributors develop and fix Airflow’s Java SDK for JVM tasks. Explains public API boundaries, JAR packaging, Python coordination, communication schema updates, and the test commands used to validate changes.

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• Adding features or fixing bugs in the Java SDK
• Reviewing public API and internal implementation boundaries
• Investigating JAR discovery and JVM startup failures

Guides preparation and authorized publication of Apache Airflow changes, including existing pull request checks, remote verification, reviews, tests, rebasing, and a prefilled submission page. Also explains how to track work deferred by temporary fixes.

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• Planning the steps for submitting Airflow changes
• Checking existing pull requests and Git remote destinations
• Reviewing and testing changes before pushing

Guides contributors through adding a new programming language to Airflow, covering SDK structure, runtime coordination, communication rules, executable packaging, tests, and the documentation and configuration changes needed for a pull request.

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• Adding support for a new programming language
• Choosing how Airflow launches a language runtime
• Implementing communication and executable bundles

Write Workflow scripts that divide research, reviews, or migrations among agents. The reference explains parallel and staged processing, independent verification, failure handling, budgets, and resuming runs so the script has a clear execution structure.

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• Design a review workflow with independent verification
• Divide broad research among agents and combine findings
• Plan staged processing for a large migration

Use the Archon CLI to launch AI workflows on projects, inspect their results, and approve, reject, cancel, or resume runs. The skill also guides setup, configuration changes, workflow creation, and improvements to workflow prompts.

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• Running an AI workflow to address an issue
• Asking Archon to review a pull request
• Inspecting, approving, or resuming a run

Configure and troubleshoot Apache Beam runners, the engines that execute data processing pipelines. Covers local testing, Dataflow, Flink, and Spark, with Java and Python examples, runner validation tests, and guidance on logs and execution settings.

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• Test a Beam pipeline locally
• Configure pipeline execution on Dataflow
• Review Flink or Spark execution settings

Write FlowGram component documentation from source code, with runnable Story examples, tables of public component options, and implementation explanations. Prepare Chinese documentation first, then translate the approved version into English.

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• Document a new FlowGram component
• Create basic and advanced Story examples
• Explain public options and implementation details

List GitHub Actions workflows with a script that lets you choose the page size and page number. Get JSON containing the total count, workflow names, file paths, states, timestamps, and links for inspection or further processing.

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• Inspect a repository's workflow list
• Review workflows in smaller batches
• Retrieve a specific page of workflows

Set up gh-aw, a GitHub CLI extension that turns Markdown workflows into GitHub Actions. The skill provides installation options and points agents to further instructions for creating, updating, debugging, and optimizing those workflows.

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• Setting up gh-aw
• Turning Markdown workflows into GitHub Actions
• Finding guidance for workflow creation and updates

Use this skill when the user is invoking doca_flow_dpa_perf on DPA-capable hardware (ConnectX-7 minimum supported, ConnectX-8 recommended, or BlueField-3) to measure rule update / disable rates on the DPA-offloaded DOCA Flow path — picking the active / passive device split, choosing workload-shape axes (burst, queue, completion threshold, workers, hash pipe algo, PSL tables), or reading Kops/sec iteration stats and the optional self-test. Trigger even when the user does not explicitly mention "doca_flow_dpa_perf" or "DPA Provider" — typical implicit phrasings include "how fast can the DPA program path-selector entries", "baseline rule-update rate on ConnectX-8", "tool reports zero ops on my BlueField", "self-test sentinel never shows on tcpdump", or "is my BlueField-2 DPA-capable". Refuse and route elsewhere for the host / DPU-CPU Flow path (doca-flow-perf), Flow pipeline tuning (doca-flow-tune), writing doca-flow / doca-dpa applications, or DOCA install — those belong to other skills.

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Scheduled Paths in record-triggered Flow: delayed execution, time-offset from field, batch size tuning, monitoring Paused Flow Interviews, async limits. NOT for picking between Scheduled Path, Wait and Scheduled Flow, or migrating time-based workflow rules — use flow/flow-time-based-patterns. NOT for a flow that runs on a recurring schedule — use flow/scheduled-flows.

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Monitor Power Automate flow health, track failure rates, and inventory tenant assets using the FlowStudio MCP cached store. The live API only returns top-level run status. Store tools surface aggregated stats, per-run failure details with remediation hints, maker activity, and Power Apps inventory — all from a fast cache with no rate-limit pressure on the PA API. Load this skill when asked to: check flow health, find failing flows, get failure rates, review error trends, list all flows with monitoring enabled, check who built a flow, find inactive makers, inventory Power Apps, see environment or connection counts, get a flow summary, or any tenant-wide health overview. Requires a FlowStudio for Teams or MCP Pro+ subscription — see https://mcp.flowstudio.app

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