“flow”的搜索结果

DOCA Flow packet steering on NVIDIA NICs/DPUs, linked into a C application: match/action pipes, ports, representors, forwarding, hairpin, counters. Trigger on packet steering, classifier, rule-matching, representor, hairpin or 5-tuple-to-queue questions even when "DOCA Flow" is not named. Remote or non-C++ control over gRPC belongs to doca-flow-grpc-server. Also covers pipe validation before hardware programming, Flow/DOCA version match, and Flow API errors. First app: follow the local bundle (doca-setup, doca-programming-guide, doca-flow, doca-public-knowledge-map). Stage 1 pull nvcr.io/nvidia/doca/doca (NGC catalog tag, never invent); Stage 2 needs a real NIC. Then ls samples inside the container; never emit main.c, a Dockerfile, or hand-typed -l flags. Route software-only host pipelines, plain DPDK `rte_flow`, kernel TC, OVS, BFB bring-up, and DPU OS installation elsewhere. DPU OS installation is destructive and always requires explicit confirmation.

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Use this skill when the user is measuring the host or DPU-CPU control-plane rate of a DOCA Flow pipeline with doca_flow_perf — picking a JSON policy from configs/, choosing the DPDK or DOCA backend, running the single-iteration smoke then the iterative eval loop, interpreting per-iteration CPU cycles and num_pushed / num_failed, or capturing the four-tuple (DOCA version, BlueField/firmware, JSON policy, worker/queue/burst config) that makes a Kops/sec number defensible. Trigger even when the user does not explicitly mention "doca-flow-perf" — typical implicit phrasings include "how many rules per second can my BlueField insert", "5-tuple hairpin rule rate", "Kops/sec for steering", "flow-perf number does not match release notes", "DPDK vs DOCA benchmark", or "rule-install variance too high". Refuse and route elsewhere for optimizing a live Flow app (doca-flow-tune), the DPA-offloaded path (doca-flow-dpa-perf), dataplane throughput or latency, or library-internal pipe semantics — those belong to other skills.

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Upgrades an MWAA environment to a newer Airflow version — within 2.x, within 3.x, or across the 2.x-to-3.x boundary. Computes the version-jump path, inserting the 2.11.x stepping-stone and Python-transition step when needed. Chooses an approach by whether run history and the same environment (URL/ARN) must be kept: a new-environment upgrade (blue-green), a rehearsed in-place upgrade validated on a test copy, or a direct in-place upgrade. Runs Ruff scanning and deprecation-warning log scans for 3.x moves, plus Docker validation, batched deployment, and switchover. Saves a resumable upgrade plan for multi-session work. Triggers on: upgrade MWAA, upgrade Airflow, migrate to Airflow 3, MWAA Airflow 3, Airflow 2 to 3, preserve Airflow history, keep same MWAA environment, blue-green cutover, validation environment before cutover. Not for authoring new DAGs (authoring-mwaa-workflow), debugging unrelated DAG failures (debugging-mwaa-workflow), or MWAA Serverless YAML workflows (provisioned Python DAGs only).

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Create complete Claude Code workflow directories with curated skills. Use when user wants to (1) create a new workflow for specific use case (media creator, developer, marketer, etc.), (2) set up a Claude Code project with pre-configured skills, (3) download and organize skills from GitHub repositories, or (4) generate README.md and AGENTS.md documentation for workflows. Triggers on phrases like "create workflow", "new workflow", "set up workflow", "build a xxx-workflow".

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Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Apache Airflow DAG Patterns workflow skill. Use this skill when the user needs 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 and the operator should preserve the upstream workflow, copied support files, and provenance before merging or handing off.

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Author and own one flow's design document (the FLOW.md objective flow) at .unikit/gamedesign/flows/FLOW-<slug>.md — the dynamics layer of the GDD (what the player does over time). Create the skeleton and walk the collaborative section-cycle to write its objectives, pacing, dependencies, and funnel events ("design the first-session flow", "map the onboarding sequence", "write the FLOW for the boss encounter"), AND revise it after approval under the delta discipline ("retune the pacing", "add a branch", "change a GOAL", "rework the onboarding") — a cue/number change is Tuning, a small step change a Tweak, restructuring a Rework; each bumps the version and logs the delta. Selects the wiring mode (linear | conditional | emergent) and re-renders the ## Flow Map [gen] / ## Funnel [gen] blocks in GAME.md. To add or detail a system use /unikit-gd-system; to edit GAME.md content use /unikit-gd-spec; to apply a multi-zone edit use /unikit-gd-apply; for a new game concept use /unikit-gd-brainstorm.

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Use when working with Argo Workflows — argo Workflows management for Kubernetes-native workflow orchestration. Covers workflow templates, cron workflow scheduling, artifact management, workflow execution, parameter handling, and resource monitoring. Use when checking workflow status, investigating step failures, managing templates, or auditing cron workflows.

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Debug failing Power Automate cloud flows using the FlowStudio MCP server. The Graph API only shows top-level status codes. This skill gives your agent action-level inputs and outputs to find the actual root cause. Load this skill when asked to: debug a flow, investigate a failed run, why is this flow failing, inspect action outputs, find the root cause of a flow error, fix a broken Power Automate flow, diagnose a timeout, trace a DynamicOperationRequestFailure, check connector auth errors, read error details from a run, or troubleshoot expression failures. Requires a FlowStudio MCP subscription — see https://mcp.flowstudio.app

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Give your AI agent the same visibility you have in the Power Automate portal — plus a bit more. The Graph API only returns top-level run status. Flow Studio MCP exposes action-level inputs, outputs, loop iterations, and nested child flow failures. Use when asked to: list flows, read a flow definition, check run history, inspect action outputs, resubmit a run, cancel a running flow, view connections, get a trigger URL, validate a definition, monitor flow health, or any task that requires talking to the Power Automate API through an MCP tool. Also use for Power Platform environment discovery and connection management. Requires a FlowStudio MCP subscription or compatible server — see https://mcp.flowstudio.app

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按具体工作梳理代码中使用大语言模型的流程，先提出用途标签方案，获批后再添加标签，便于在 Caveman Cloud 中按流程分别查看费用。

• 按具体工作了解模型费用
• 盘点接口和定时任务的模型使用
• 单独统计评估流程的费用

将移动应用的使用流程绘制为横向排列的三至四个手机框，用灰色占位块、虚线箭头、编号操作标签和简短注释，帮助检查画面结构与跳转关系。

• 梳理引导页到首页的流程
• 检查商品详情到确认的路径
• 在视觉设计前讨论页面跳转

用于检查claude-flow v3的安全设计，梳理威胁与保护范围，完善依赖包、密码存储、认证凭据、输入验证和命令执行方面的措施与测试。

• 梳理安全边界和威胁
• 更新并审计依赖包
• 检查密码存储与认证凭据生成

Handle user registration, login, and account management in Flow Nexus through its MCP tools, which connect agents to platform operations. Check authentication status, update profiles, guide password resets, and troubleshoot access problems.

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• Register users and guide email verification
• Log in and check session status
• Reset passwords and recover account access

在Flow Nexus应用市场中查找和发布应用、管理版本并部署模板，还可查看使用情况、评分与市场统计，并检查文档、安全和性能等上架质量。

• 查找符合需求的应用
• 发布应用并管理版本
• 部署应用模板

Create workflows that pause for approval and resume later, or scripts that distribute work across multiple agents. Choose a saved template for ongoing processes or a JavaScript script to collect structured results from parallel tasks.

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• Build a workflow with approval checkpoints
• Split review work across multiple agents
• Collect structured results from research tasks

Manage Flow Nexus accounts, run code in sandboxes, deploy app templates, and track credits. The skill guides platform operations through connected tools, including file storage, execution monitoring, and coding challenge submissions.

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• Create a sandbox and run development code
• Find and deploy an app template
• Check credit balances and payment history

测量agentic-flow工作单元在触发检测、执行代理选择、缓存及并行运行中的速度和内存占用，可运行全部或指定基准测试，并与设定的性能目标比较。

• 检查工作单元系统的整体性能
• 测量触发检测和执行代理选择速度
• 调查缓存行为与内存占用

将软件开发划分为需求、设计、测试先行的实现、审查和部署等阶段，通过Claude Flow协调多个智能体分工，管理开发全过程。

• 从需求出发开发新功能
• 结合测试改造现有代码
• 排查故障并添加回归测试

用于检查claude-flow v3的安全设计，梳理威胁与保护范围，完善依赖包、密码存储、认证凭据、输入验证和命令执行方面的措施与测试。

• 梳理安全边界和威胁
• 更新并审计依赖包
• 检查密码存储与认证凭据生成

通过agentic-flow让多个AI智能体分工，安排并行任务和有先后依赖的流程，并协调信息共享、负载均衡及失败后的任务重分配。

• 并行开发API、界面和测试
• 衔接从设计到审查的各个阶段
• 在不同智能体间共享API规范

分析Claude Flow中多个智能体的处理、通信和内存延迟，生成性能报告与改进建议，支持对比历史运行结果和自动优化。

• 调查多个智能体的延迟
• 比较变更前后的性能
• 制作每周性能报告

Design scheduled AI workflows for document ingestion, batch processing, and model training using Prefect, Airflow, or Dagster. Compare orchestration tools and plan task dependencies, retries, caching, and monitoring through implementation fragments.

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• Schedule document ingestion and RAG index refreshes
• Design batch workflows for LLM requests
• Connect model training with quality evaluation

Update Flask-AppBuilder in Apache Airflow’s FAB provider, including pinned versions, dependency locks, and documentation. Check compatibility tests and review copied security code, with asset and database migration updates when the upstream release requires them.

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• Upgrade the FAB provider to a newer Flask-AppBuilder release
• Review upstream authentication fixes against Airflow's copied code
• Resolve compatibility failures after a dependency update