orchestrate

Coordinate multiple AI subagents in parallel via Gemini CLI and MCP Memory.

Updated Mar 27, 2026

Search Tags:#workflow automation #parallel execution #agent coordination #ai orchestration #cli automation #mcp memory

Ready to run

npx skills add https://github.com/BaGyun0107/harness-starterkit --skill orchestrate-bagyun0107

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: orchestrate
Source: https://github.com/BaGyun0107/harness-starterkit/tree/main/.claude/skills/orchestrate
Command: npx skills add https://github.com/BaGyun0107/harness-starterkit --skill orchestrate-bagyun0107

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

It simplifies the management and execution of multiple AI agents working in parallel, reducing manual oversight and coordination efforts.

Core Features & Use Cases

Parallel Agent Execution: Spawn and run multiple subagents simultaneously via Gemini CLI.
Workflow Coordination: Synchronize agent actions through MCP Memory for consistent state management.
Progress Monitoring and Verification: Track agent activities and verify results automatically, streamlining complex tasks like data processing or decision workflows.
Use Case: Automate a multi-step data analysis pipeline where various subagents fetch, process, and validate information concurrently, ensuring smooth workflow execution.

Quick Start

Read and follow the instructions in the orchestrate.md workflow file to set up and initiate agent coordination.

Frequently Asked Questions about orchestrate

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

FAQPage Schema

How do I coordinate multiple AI agents running in parallel?▼

Parallel agent execution is coordinated by spawning multiple subagents simultaneously via Gemini CLI and synchronizing their actions through MCP Memory for consistent state management and validation.

Can I use MCP Memory to synchronize workflow state across multiple subagents?▼

MCP Memory synchronizes workflow state across multiple subagents by tracking agent activities and validating results automatically, ensuring consistent progress during complex multi-agent task execution.

Do I need Gemini CLI to automate multi-step data analysis pipelines with parallel agents?▼

Gemini CLI is required to automate multi-step data analysis pipelines, as it spawns and runs the multiple subagents concurrently to fetch, process, and validate information.

What is the best way to monitor parallel agent progress during complex workflow automation?▼

Monitoring parallel agent progress during workflow automation is handled by dedicated monitoring scripts that track agent activities and verify results automatically to streamline execution.

How do I set up multi-agent workflow coordination for concurrent data processing tasks?▼

To set up multi-agent workflow coordination, read and follow the instructions in the orchestrate workflow file to configure the environment and initiate synchronized subagent execution.