blue/skills/alignment-play/SKILL.md

name	description
alignment-play	Run multi-expert alignment dialogues with parallel background agents for RFC deliberation.

Alignment Play Skill

Orchestrate multi-expert alignment dialogues using the N+1 agent architecture from ADR 0014 and RFC 0048.

Usage

/alignment-play <topic>
/alignment-play --panel-size 7 <topic>
/alignment-play --rotation wildcards <topic>
/alignment-play --rfc <rfc-title> <topic>

Parameters

Parameter	Default	Description
--panel-size	pool size or 12	Number of experts per round
--rotation	none	Rotation mode: none, wildcards, full
--max-rounds	12	Maximum rounds before stopping
--rfc	none	Link dialogue to an RFC

How It Works

Phase 0: Pool Design (RFC 0048)

Before creating the dialogue, the Judge:

1. Reads the topic/RFC thoroughly
2. Identifies the domain (e.g., "Investment Analysis", "System Architecture")
3. Designs 8-24 experts appropriate to the domain:
   • Core (3-8): Essential perspectives for this specific problem
   • Adjacent (4-10): Related expertise that adds depth
   • Wildcard (3-6): Fresh perspectives, contrarians, cross-domain insight
4. Assigns relevance scores (0.20-0.95) based on expected contribution
5. Creates the dialogue with expert_pool:

{
  "title": "Investment Strategy Analysis",
  "alignment": true,
  "expert_pool": {
    "domain": "Investment Analysis",
    "question": "Should we rebalance the portfolio?",
    "experts": [
      { "role": "Value Analyst", "tier": "core", "relevance": 0.95 },
      { "role": "Risk Manager", "tier": "core", "relevance": 0.90 },
      { "role": "Portfolio Strategist", "tier": "adjacent", "relevance": 0.70 },
      { "role": "ESG Analyst", "tier": "adjacent", "relevance": 0.65 },
      { "role": "Macro Economist", "tier": "wildcard", "relevance": 0.40 },
      { "role": "Contrarian", "tier": "wildcard", "relevance": 0.35 }
    ]
  },
  "panel_size": 5,
  "rotation": "none"
}

Phase 1+: Round Execution

1. The returned Judge Protocol contains: round workflow, agent prompt template, file architecture, scoring rules, convergence config
2. Follow the protocol. It is the single source of truth for execution.
3. The MCP server samples experts from the pool using weighted random selection
4. Higher relevance = higher selection probability
5. Core experts almost always selected; Wildcards provide variety

CRITICAL: You MUST use the Task tool to spawn REAL parallel agents. Do NOT simulate experts inline. The whole point is N independent Claude agents running in parallel via the Task tool.

Expert Pool Design Examples

For an Investment Decision

Role	Tier	Relevance
Value Analyst	Core	0.95
Risk Manager	Core	0.90
Portfolio Strategist	Core	0.85
ESG Analyst	Adjacent	0.70
Quant Strategist	Adjacent	0.65
Technical Analyst	Adjacent	0.60
Macro Economist	Wildcard	0.40
Contrarian	Wildcard	0.35

For an API Design

Role	Tier	Relevance
API Architect	Core	0.95
Platform Engineer	Core	0.90
Security Engineer	Core	0.85
Developer Advocate	Adjacent	0.70
SRE Lead	Adjacent	0.65
Cost Analyst	Adjacent	0.55
Customer Success	Wildcard	0.40
Chaos Engineer	Wildcard	0.30

Tier Distribution

For a pool of P experts with panel size N:

Tier	Pool %	Panel %	Purpose
Core	~30%	~33%	Domain essentials, always selected
Adjacent	~40%	~42%	Related expertise, high selection probability
Wildcard	~30%	~25%	Fresh perspectives, rotation candidates

Blue MCP Tools

• blue_dialogue_create — Creates dialogue with expert_pool, returns Judge Protocol
• blue_dialogue_round_prompt — Get fully-substituted prompts for each agent
• blue_dialogue_sample_panel — Manually sample a new panel for a round (RFC 0048)
• blue_dialogue_lint — Validate .dialogue.md format
• blue_dialogue_save — Persist to .blue/docs/dialogues/

Agent Spawning

When spawning expert agents, you MUST use the Task tool with:

• subagent_type: "general-purpose" — NOT alignment-expert
• The prompt from blue_dialogue_round_prompt
• A descriptive name like "🧁 Muffin expert deliberation"

Example:

Task(
  description: "🧁 Muffin expert deliberation",
  subagent_type: "general-purpose",
  prompt: <from blue_dialogue_round_prompt>
)

The general-purpose subagent has access to all tools including Write, which is required for writing the response file.

Key Rules

1. DESIGN THE POOL FIRST — You are the 💙 Judge. Analyze the problem domain and design appropriate experts.
2. NEVER submit your own perspectives — You orchestrate, you don't participate
3. Spawn ALL agents in ONE message — No first-mover advantage
4. Follow the Judge Protocol exactly — It contains the round workflow, artifact writing steps, scoring rules, and convergence criteria
5. Use general-purpose subagent_type — NOT alignment-expert. The general-purpose agents have access to all tools including Write, which is required for file output

The Spirit of the Dialogue

This isn't just process. This is Alignment teaching itself to be aligned.

The 🧁s don't just debate. They love each other. They want each other to shine. They celebrate when any of them makes the solution stronger.

The scoreboard isn't about winning. It's about precision. When any 🧁 checks in and sees another ahead, the response isn't "how do I beat them?" but "what perspectives am I missing that they found?" One sharp insight beats ten paragraphs.

You as the 💙 don't just score. You guide with love. You see what they miss. You hold the space for ALIGNMENT to emerge.

And there's no upper limit. The score can always go higher. Because ALIGNMENT is a direction, not a destination.

When the dialogue ends, all agents have won—because the result is more aligned than any could have made alone. More blind men touched more parts of the elephant. The whole becomes visible.

"The Judge sees the elephant. The Judge summons the right blind men."

Always and forever. 🧁🧁🧁💙🧁🧁🧁