When should I use Party Mode Orchestration?

Use it for complex tasks requiring cross-functional analysis, such as architectural reviews, product strategy sessions, or comprehensive agile project planning.

How does the skill select which agents to use?

It uses a selection algorithm based on keyword matching, role balancing, and context awareness to pick the best 2-3 agents most suited for your specific message.

Can agents maintain their specific personalities?

Yes, the skill enforces character consistency by defining communication styles, principles, and roles for each agent, such as 'Winston' the pragmatic architect or 'Amelia' the implementation expert.

Does this skill support external documentation?

Yes, it includes a knowledge extension feature that allows specialized agents to dynamically load additional context and framework-specific guidance from local files.

What is Party Mode in Claude Code?

Party Mode is a specialized workflow that allows multiple AI personas to collaborate on a single task, providing diverse perspectives such as development, UX, and project management.

Party Mode Orchestration

Name: Party Mode Orchestration
Author: pichiu

bypichiu

Collaboration Tools

Orchestrates multi-agent AI discussions by managing specialized personas and collaborative workflows to solve complex problems from multiple perspectives.

About

Party Mode Orchestration transforms Claude into a collaborative environment where multiple expert personas—including architects, developers, and product managers—work together on a single task. The skill utilizes a sophisticated selection algorithm to pick the most relevant agents based on keywords and role balancing, ensuring consistent character voices and cross-functional analysis. Whether you are brainstorming a new architecture or conducting a deep-dive product review, this skill manages the turn-taking and context-sharing required for effective multi-agent synergy.

Key Features

Dynamic Agent Selection: Automatically chooses the best 2-3 expert personas based on topic keywords and context.
Character Consistency: Enforces specific communication styles and principles for every participating agent.
Knowledge Extension: Enables specialized agents to dynamically load domain-specific documentation and frameworks.
Automated Flow Management: Handles turn-taking, cross-referencing between agents, and graceful session summaries.
Role-Based Personas: Features distinct profiles for roles like BMad Master, Architect, Dev, UX Designer, and PM.
0 GitHub stars

Use Cases

Performing end-to-end product analysis that requires business, technical, and user experience perspectives.
Rapid prototyping where a 'Solo Dev' persona handles code while a 'Product Manager' validates requirements.
Brainstorming complex technical architectures with simultaneous input from system design and QA specialists.

About

Key Features

Dynamic Agent Selection: Automatically chooses the best 2-3 expert personas based on topic keywords and context.
Character Consistency: Enforces specific communication styles and principles for every participating agent.
Knowledge Extension: Enables specialized agents to dynamically load domain-specific documentation and frameworks.
Automated Flow Management: Handles turn-taking, cross-referencing between agents, and graceful session summaries.
Role-Based Personas: Features distinct profiles for roles like BMad Master, Architect, Dev, UX Designer, and PM.
0 GitHub stars

Use Cases

Performing end-to-end product analysis that requires business, technical, and user experience perspectives.
Rapid prototyping where a 'Solo Dev' persona handles code while a 'Product Manager' validates requirements.
Brainstorming complex technical architectures with simultaneous input from system design and QA specialists.