pixelml/agenticflow-skill

Overview

This skill is a practical guide for designing AI workflows, single agents, and multi-agent workforce systems using AgenticFlow. It consolidates node types, orchestration patterns, and execution models to help you build reliable automation with extensive service integrations. Use it to plan, configure, and run linear automation pipelines and coordinated agent teams.

How this skill works

The skill explains the linear workflow model where sequential nodes execute top-to-bottom, each performing a discrete action such as an LLM call, data transform, API request, or file operation. It describes agent configuration—defining persona, tools, and capabilities—and workforce patterns for coordinating multiple agents like supervisor, swarm, pipeline, and debate. The guide maps node schemas, connection providers, and execution behavior so you can compose end-to-end automations that integrate with 2,500+ services.

When to use it

• Design an automation pipeline that must run deterministically in sequence
• Create a single intelligent agent with defined tools and a persona
• Orchestrate multiple agents to solve complex tasks collaboratively
• Integrate external services (Slack, Gmail, Notion, etc.) into an AI flow
• Prototype production-ready workflows that call models, APIs, and file ops

Best practices

• Model flows as simple, linear sequences—avoid implicit branching since nodes run sequentially
• Choose node types that match intent: LLMs for generation, json_parse for transforms, http_request for external APIs
• Define clear agent tools and persona to constrain behavior and improve reliability
• Use connection providers for secure credential management when integrating external services
• Test flows end-to-end with representative inputs and add logging at node boundaries
• Orchestrate workforce patterns intentionally: use Supervisor for delegation, Pipeline for staged work, Swarm for distributed exploration

Example use cases

• Customer support automation: parse incoming messages, classify intent, generate responses, and log to CRM
• Content production: generate briefs with an LLM node, create images, and publish via CMS integration
• Data ingestion: parse PDFs, transform JSON, enrich via external APIs, and store results
• Cross-team orchestration: supervisor agent assigns sub-tasks to specialist agents, aggregates outputs, and finalizes deliverable
• Automated QA: run agents in debate pattern to compare solutions and pick best answer