'multi-step workflows' 검색 결과

Build reusable parts for n8n workflows instead of copying shared logic. This skill guides typed inputs, consistent outputs, naming, and execution settings so workflows and AI agents can call the same processing steps.

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• Sharing common logic across workflows
• Splitting a large workflow into reusable parts
• Exposing shared processing as an AI agent tool

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Build Python workflows that constrain language model output using text patterns and grammar rules. Generate structured JSON, restrict classifications to allowed labels, extract information, and combine reusable generation steps with loops and tool selection.

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• Generate JSON with a defined structure
• Constrain date, email, and identifier formats
• Classify text using a fixed set of labels

Build AI agents on Cloudflare Workers with the Agents SDK. Implement persistent state, real-time chat, scheduled tasks, multi-step background workflows, and MCP servers, using current Cloudflare documentation to guide implementation.

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• Build agents that retain and synchronize state
• Create real-time chat with resumable streaming
• Implement scheduled tasks and multi-step workflows

Build reusable, composable n8n sub-workflows. Use when extracting shared logic, building anything multi-step or reused across workflows, or any workflow over ~10 nodes — and whenever the user mentions sub-workflows, Execute Workflow, reuse, shared/common logic, modular workflows, "Define Below" inputs, waitForSubWorkflow, mode each vs all, or exposing a workflow as an agent tool. Covers typed sub-workflow inputs, all-vs-each execution, verb-first naming for discovery, stateless vs stateful design, and splitting by input shape.

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Integrate Firecrawl browser actions into application code to retrieve content that requires clicks, form entry, or pagination. Use it for multi-step page interactions and workflows that need to preserve the browser session after scraping.

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• Retrieving content revealed by clicks
• Working with search forms and filters
• Collecting results across multiple pages

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

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 when a task involves running a Scenario workflow through MCP, including anything the user calls a Scenario app, a saved pipeline, or a multi-step generation graph. Triggers include listing workflows, building workflow_run's inputs object, pricing a run with a dry run, approving or rejecting a stuck approval node, a run rejected for input length, or a workflows_list reply flooding the context. Creating or editing graphs is scenario-workflow-authoring. Keywords: workflow, app, approval gate.

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Cloudflare Workflows for durable long-running execution. Use for multi-step workflows, retries, state persistence, or encountering NonRetryableError, execution failed errors.

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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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Work with the @upstash/workflow TypeScript/JavaScript SDK for durable, long-running workflows in serverless functions, multi-step processes that survive timeouts, retries, and restarts (built on QStash). Use when defining a workflow endpoint with serve(), running steps with context.run, sleeping for minutes to days without holding a function open, calling external APIs with context.call, waiting for an external event or webhook, invoking other workflows, configuring retries, failure callbacks, and a DLQ, controlling concurrency, rate, and parallelism, triggering, cancelling, or inspecting runs with the Workflow client, building AI agents and orchestrators, human-in-the-loop approvals, realtime updates, local development with the QStash dev server, adding middleware, or migrating workflows safely. Also use when the user asks for durable execution, step functions, saga or orchestration patterns, background jobs with checkpoints, or long-running tasks on Vercel, Next.js, Cloudflare Workers, or other serverless platforms.

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Use this skill when working with scientific research tools and workflows across bioinformatics, cheminformatics, genomics, structural biology, proteomics, and drug discovery. This skill provides access to 600+ scientific tools including machine learning models, datasets, APIs, and analysis packages. Use when searching for scientific tools, executing computational biology workflows, composing multi-step research pipelines, accessing databases like OpenTargets/PubChem/UniProt/PDB/ChEMBL, performing tool discovery for research tasks, or integrating scientific computational resources into LLM workflows.

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Control LLM output with regex and grammars, guarantee valid JSON/XML/code generation, enforce structured formats, and build multi-step workflows with Guidance - Microsoft Research's constrained generation framework

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Designs multi-step agent workflows with tool usage, retry logic, state management, and budget controls. Provides orchestration diagrams, tool execution order, fallback strategies, and cost limits. Use for "AI agents", "agentic workflows", "multi-step AI", or "autonomous systems".

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Control local language model output with patterns and grammar rules to generate structured JSON, formatted dates, and fixed classification labels. The skill also covers reusable Python functions and workflows that combine multiple generation steps.

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• Generate JSON that follows a defined schema
• Keep dates and email addresses in a specified format
• Classify text using a fixed set of labels

Build repeatable evaluations for AI agent responses, tool choices, and multi-step tasks. Use reference datasets and scoring criteria to compare changes, detect quality regressions, and set automated checks that must pass before deployment.

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• Compare agent quality before and after prompt changes
• Test tool selection and multi-step task execution
• Evaluate resistance to prompt injection

Design collaborative AI teams with CrewAI by defining each agent’s role, goals, tasks, and expected results. Covers passing work between agents, manager-led coordination, memory settings, and execution planning for workflows such as research and writing.

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• Build a team that researches and writes articles
• Coordinate specialists through a manager agent
• Define agent roles and expected task outputs

Build and operate AutoGPT agents that carry out multi-step workflows. Covers a visual editor for connecting processing blocks, scheduled and webhook triggers, deployment, execution monitoring, and benchmarks for evaluating agent performance.

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• Build an AI workflow in a visual editor
• Run agents on a schedule or external event
• Add custom agent abilities with Forge

Manage isolated working environments in OpenShell using its command-line tool. Register gateways, create and connect to sandboxes, configure credential providers, and reuse workload templates with shared images, environment settings, and computing resources.

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• Create and connect to a sandbox
• Configure and refresh provider credentials
• Inspect and import provider profiles

Test application business rules by mapping workflows and checking unexpected actions, inputs, identities, and repetition. The skill helps investigate checkout, refunds, subscriptions, permissions, and concurrency flaws, then document reproduction steps and financial impact.

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• Checking checkout and refund workflows
• Testing price, quantity, and coupon rules
• Reviewing role and tenant boundaries

Find and run Refly skills that match a task, then track the workflow through completion. Retrieve generated files or text, or check whether an action finished, using results verified by the Refly command-line tool.

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• Find an installed skill for a task
• Run an image or video generation workflow
• Retrieve research results as text

Create specialized assistants for Cursor that work with their own context, separate from the main conversation. Define responsibilities, instructions, output formats, model choices, and execution settings for reusable workflows such as verification, debugging, and documentation.

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• Creating an assistant to verify completed work
• Defining a specialist for debugging and test failures
• Creating a read-only security audit assistant