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trigger-agents

Build Trigger.dev agent workflows with orchestration, routing, and human-in-the-loop gates.

Updated Apr 5, 2026
Search Tags:#routing#orchestration#parallelization#human-in-the-loop#ai-agents#tool-calling#trigger.dev
One-click install
npx skills add https://github.com/oren-argu/papermark --skill trigger-agents-oren-argu
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: trigger-agents
Source: https://github.com/oren-argu/papermark/tree/main/.agents/skills/trigger-agents
Command: npx skills add https://github.com/oren-argu/papermark --skill trigger-agents-oren-argu

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

Coordinating multiple AI tasks and services into reliable, production-ready agent workflows can be complex, requiring orchestration, parallelization, routing, evaluation, and human-in-the-loop controls to ensure quality and governance.

Core Features & Use Cases

  • Orchestrated agent patterns: implement durable sequences and parallel workers for LLM-powered tasks.
  • Routing and decisioning: dispatch tasks to appropriate models or handlers based on input.
  • Human-in-the-Loop gates: pause for approvals and integrate waitpoints to enforce governance.
  • Tool calling and multi-step workflows: enable LLMs to call tools and chain steps with validators and evaluators.

Quick Start

Describe an AI agent workflow that orchestrates parallel tasks, routes results to different models, and pauses for human approval.

Frequently Asked Questions about trigger-agents

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
What AI agent patterns are supported for production-grade task orchestration?▼

Production-grade task orchestration supports a library of core AI agent patterns including Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, and Evaluator-Optimizer. These patterns coordinate LLM tasks with durable execution and gated approvals.

How do I add human-in-the-loop approval gates to an LLM workflow?▼

Human-in-the-loop approval gates are added to an LLM workflow by integrating waitpoints that pause execution. This enforces governance by requiring manual approvals before the workflow resumes subsequent tool calls or routing steps.

What is the best way to route AI tasks to different models dynamically?▼

Routing AI tasks to different models dynamically is best handled through dedicated routing patterns that dispatch tasks to appropriate handlers based on input. This ensures LLM-powered tasks are evaluated and directed to the correct model or workflow step.

Can I use Trigger.dev to orchestrate multi-step tool calling for AI agents?▼

Yes, Trigger.dev orchestrates multi-step tool calling for AI agents by enabling LLMs to call tools and chain steps with validators. It supports production-grade patterns like Prompt Chaining and Orchestrator-Workers for complex task coordination.

What AI agent patterns are supported for production-grade task orchestration?▼

Production-grade task orchestration supports a library of core AI agent patterns including Prompt Chaining, Routing, Parallelization, Orchestrator-Workers, and Evaluator-Optimizer. These patterns coordinate LLM tasks with durable execution and gated approvals.

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