“workflows”的搜索结果

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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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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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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Creates, debugs, and optimizes GitHub Actions workflow YAML files. Recommends current action versions with SHA pinning from a daily-updated index of 120+ actions. Configures matrix builds, dependency caching, reusable workflows, OIDC authentication, secrets management, and runner selection. Reviews existing workflows for outdated actions, security issues, and performance problems. Use when the user asks about GitHub Actions, workflows, CI/CD, .github/workflows, any action by name (actions/checkout, setup-node, setup-python, etc.), or workflow optimization.

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审查REA的分析流程与缓存设计，在保持结果正确性和依据可访问性的同时，改善耗时、CPU与内存使用，以及智能体需要处理的信息量。

• 调查分析流程缓慢的原因
• 审查缓存设计
• 降低CPU和内存负载

读取仓库设置和智能体运行环境，盘点已连接工具与可用功能，区分缺少工作流程和缺少外部集成的情况，并按优先级提出三至五项ECC后续建议。

• 检查Claude Code设置
• 盘点已连接工具及其功能
• 判断是否需要添加插件

从交易平台规则、数据质量、机密信息、隐私和交易执行等方面审查预测市场及交易代理流程，整理发现的问题、应停止的操作和必要防护措施。

• 接入交易服务认证前审查
• 检查资产信息与API密钥处理
• 检查市场数据质量与来源标注

将本地反复执行的Hermes工作流程整理为可公开复用的ECC技能和发布资料，移除个人信息与凭据，替换本机路径，并列明公开输入、已移除的私有输入和剩余风险。

• 将重复任务整理为公开技能
• 整理可公开的交接资料
• 编写不含私有数据的夜间任务文档

为电子病历中的诊疗记录、处方和检验结果界面提供设计指导。涵盖药物相互作用警告、异常值显示、签署后记录锁定、追补记录与审计历史，并兼顾患者安全和录入便利。

• 设计诊疗流程
• 实现药物相互作用警告
• 显示检验结果中的异常值

通过 dmux 将开发、研究、测试或评审分配给多个 AI 智能体并行推进，明确职责、隔离工作目录，并在检查成果后汇总。

• 并行开展研究与实现
• 分工开发数据库、API 和界面
• 分别负责测试监控与修复

Run ComfyUI workflows to generate images, video, audio, and 3D content. Prepare models and processing nodes, adjust generation settings, create batches of variations, monitor progress, and download results from a local server or Comfy Cloud.

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• Generate images from text with Stable Diffusion or Flux
• Transform an image or replace a selected area
• Create batches with different generation settings

Find LLM workflows in a codebase and label them by the job they perform, so Caveman Cloud can separate spending by workflow. Present a labeling proposal for approval, apply focused code changes, and check an affected execution path.

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• Breaking down LLM spending by job
• Inventorying LLM use in APIs, scheduled jobs, and scripts
• Separating evaluation spending from other workflows

Use the Venice.ai API for video generation and transcription workflows. This catalogue entry helps agents discover the skill; running the complete workflow requires installing the upstream bundle with its scripts, assets, and references.

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• Generating videos through the Venice.ai API
• Requesting transcription through Venice.ai
• Adding video workflows to an agent's planning

Submit groups of AI requests as batch jobs with the OmniRoute command line. Manage input files, monitor or cancel jobs, save results and errors, and incorporate batch processing into automated build and deployment workflows.

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• Submitting AI requests from a JSONL file
• Monitoring batch jobs and waiting for completion
• Saving batch results and error details

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

Identify and test critical workflows where APIs, services, databases, and interface components work together. Review normal behavior, important failures, and CI checks to find gaps that isolated unit tests can miss and keep those interactions continuously verified.

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• Testing interactions between APIs, services, and databases
• Checking components connected to state management
• Reviewing CI coverage for critical workflows

Manage ComfyUI image generation from the command line. Validate workflows, which define generation steps, submit them for execution, monitor queued jobs, inspect available models, and download images from completed runs.

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• Validating and submitting image generation workflows
• Checking or interrupting queued generation jobs
• Listing available models and node types

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

Build and troubleshoot TorchDrug 0.2.1 workflows for molecular property prediction, molecule generation, protein modeling, and knowledge graph reasoning. Connect datasets, models, training tasks, and evaluation while checking version compatibility and common implementation errors.

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• Training and evaluating molecular property prediction models
• Building molecule generation workflows
• Learning representations of protein sequences and structures

Run molecular property predictions, explore molecular shapes, and estimate how compounds bind to proteins through a cloud Python API. Process compound batches and connect modeling steps without maintaining local high-performance computing infrastructure.

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• Predicting properties across a compound library
• Exploring molecular shapes and tautomeric forms
• Estimating how compounds bind to proteins

Inspect microscopy data stored on OMERO.server, export selected image metadata, and plan imports or exports. The skill uses explicit data scopes and limits, with dry-run helpers and reviewed targets for workflows that change server data.

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• Inventory selected OMERO objects with result limits
• Export annotations and regions of interest as JSON
• Plan microscopy image imports and exports

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 machine learning workflows from data preparation through training, validation, deployment, and monitoring. Use templates and guidance to connect stages, track versions and experiments, handle failures, and plan gradual releases with rollback options.

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• Designing an end-to-end machine learning pipeline
• Building reproducible training workflows
• Comparing models before production deployment

Design AI applications with LangChain 1.x and LangGraph, libraries for coordinating language models and workflows. Build agents that use external tools, manage conversation history and saved state, process documents, and support tracing and testing.

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• Build an AI agent that calls external tools
• Manage conversation history and saved execution state
• Create document processing and retrieval workflows