jean-claude-cli

Explain Jean Claude CLI commands for orchestrating two-agent workflows and monitoring.

1|Updated Dec 22, 2025

Search Tags:#cli #beads #dashboard #event-sourcing #two-agent #jean-claude #coordinator-pattern

Ready to run

npx skills add https://github.com/JoshuaOliphant/jean-claude --skill jean-claude-cli

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: jean-claude-cli
Source: https://github.com/JoshuaOliphant/jean-claude/tree/main/.claude/skills/jean-claude-cli
Command: npx skills add https://github.com/JoshuaOliphant/jean-claude --skill jean-claude-cli

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Guides users in leveraging Jean Claude CLI to orchestrate complex AI-driven development workflows, bridging command-line tooling with architectural patterns such as two-agent coordination, event sourcing, and the coordinator pattern.

Core Features & Use Cases

Expert guidance for core jc commands (init, prompt, workflow, work, note, prime, status, logs, dashboard, migrate)
Explain two-agent planning (Opus) and execution (Sonnet) and their interaction with Beads, mailbox, and the dashboard
Clarify event-sourced state management, real-time monitoring, and escalation flows via ntfy.sh

Quick Start

Ask JC CLI to guide you through initializing the project and starting a two-agent workflow.

Frequently Asked Questions about jean-claude-cli

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

FAQPage Schema

How do I orchestrate a two-agent workflow using a command-line interface?▼

A two-agent workflow is orchestrated by initializing a project and using core commands to coordinate a planning agent for task breakdown and an execution agent for implementation. The CLI manages their interaction through a mailbox system and dashboard monitoring.

What is the coordinator pattern in event-sourced AI workflows?▼

The coordinator pattern in event-sourced workflows separates planning from execution, using an event log to track state. A planning agent generates tasks and beads, while a separate execution agent processes them, allowing real-time monitoring and recovery through event sourcing.

How do I monitor AI agent execution and track workflow status in real time?▼

AI agent execution is monitored using the dashboard and status commands. The dashboard provides a real-time view of event-sourced state, bead processing, and mailbox interactions, while escalation flows can be configured to send notifications via ntfy.sh.

What commands are needed to initialize and manage AI-driven development projects?▼

Core commands for AI-driven development include init for project setup, prompt for inputs, workflow for execution, work for bead tasks, note for logging, prime for preparation, status for monitoring, logs for history, dashboard for visualization, and migrate for updates.

How does event sourcing manage state in a two-agent planning and execution architecture?▼

Event sourcing manages state by appending all workflow changes and agent interactions as immutable events. The planning agent creates bead tasks logged as events, and the execution agent consumes them, ensuring the dashboard reflects an accurate, recoverable history of the workflow.