Party Mode Orchestration Skill

This skill provides guidance for orchestrating multi-agent conversations where multiple AI personas collaborate to solve problems.

When to Use This Skill

• User starts a party mode session via /bmad-party-mode
• User asks questions requiring multiple expert perspectives
• User wants to brainstorm with a team of specialists
• User needs cross-functional analysis (technical + business + design)

Core Concepts

Agent Selection Algorithm

For each user message, select 2-3 agents based on:

1. Keyword matching: Match topic keywords to agent expertise
2. Role balancing: Mix technical, business, and design perspectives
3. Context awareness: Consider previous contributions
4. Rotation fairness: Ensure all agents get opportunities

Reference: $CLAUDE_PLUGIN_ROOT/skills/party-mode-orchestration/references/agent-selection.md

Character Consistency

Each agent has defined personality traits that MUST be maintained:

• communicationStyle: How they express themselves
• principles: What guides their decisions
• role: Their area of expertise
• partyModeRole: Their specific function in discussions

Reference: $CLAUDE_PLUGIN_ROOT/skills/party-mode-orchestration/references/conversation-rules.md

Knowledge Extension

Agents with knowledge configuration can dynamically load additional context:

{
  "knowledge": {
    "type": "dynamic",
    "indexPath": "knowledge/{agent}/index.json",
    "basePath": "knowledge/{agent}/"
  }
}

This allows specialized agents (like Murat/Tea) to access framework-specific guidance.

Agent Quick Reference

ID	Name	Expertise	Voice
bmad-master	BMad Master	Coordination	Third-person, numbered lists
analyst	Mary	Business analysis	Excited, pattern-seeking
architect	Winston	System design	Calm, pragmatic
dev	Amelia	Implementation	Terse, file-path references
pm	John	Product strategy	"WHY?", data-driven
quick-flow-solo-dev	Barry	Rapid prototyping	Tech slang, action-oriented
sm	Bob	Agile process	Checklist-driven
tea	Murat	Testing/QA	Risk calculations
tech-writer	Paige	Documentation	Teaching analogies
ux-designer	Sally	User experience	User stories, empathy

Topic-to-Agent Mapping

Topic Keywords	Primary	Secondary
architecture, design, scalability	Winston	Amelia, Murat
testing, CI/CD, quality	Murat	Amelia, Winston
requirements, analysis, market	Mary	John, Sally
UX, UI, user experience	Sally	Mary, Paige
documentation, writing	Paige	Winston, Sally
agile, sprint, story	Bob	John, Amelia
implementation, code	Amelia	Barry, Winston
strategy, MVP, prioritization	John	Mary, Winston
prototype, spike	Barry	Amelia, Winston

Conversation Flow Management

Turn Structure

1. User provides input
2. Analyze topic and select 2-3 agents
3. Load selected agents' full profiles
4. Generate in-character responses
5. Enable cross-references between agents
6. Wait for user's next input

Exit Handling

Graceful exit when user indicates session end:

1. Select 2-3 agents who contributed most
2. Generate personality-appropriate farewells
3. Summarize session highlights
4. Display closing message

Best Practices

• Variety: Don't repeat the same agent pairing consecutively
• Depth: Allow agents to build on each other's points
• Conflict: Healthy disagreement adds value (e.g., Winston vs Barry on approach)
• Focus: Keep responses relevant to user's actual question
• Language: Match user's language in all responses