1k Group Think

Multi-Agent Collaborative Analysis

Spawn 3 AI agent team members (NOT subagents) with distinct analytical perspectives. They communicate with each other via SendMessage, debate trade-offs, then present a unified comparison for the user to choose from.

Prerequisites

Agent teams are experimental. Ensure the feature is enabled:

// settings.json
{
  "env": {
    "CLAUDE_CODE_EXPERIMENTAL_AGENT_TEAMS": "1"
  }
}

Critical: Team Members vs Subagents

You MUST spawn agents as team members using team_name and name parameters. NEVER use run_in_background: true.

Workflow

Step 1: Clarify the Problem (MANDATORY — Do NOT Skip)

Before ANY code exploration, use AskUserQuestion to fill in missing context. Ask up to 4 questions covering these dimensions:

Rules:

• Only ask questions whose answers you genuinely cannot infer from the user's message or the codebase.
• If the user's description is already detailed (includes platform, steps, expected vs actual), skip directly to Step 2.
• Frame questions as multiple-choice with AskUserQuestion when possible — faster for the user.
• Maximum 1 round of questions. Do NOT ask follow-ups; gather what you can and move on.

Step 2: Deep Codebase Exploration

After clarification, the team lead MUST thoroughly explore the codebase before spawning agents. Agents are expensive — giving them rich context upfront is far cheaper than having 3 agents each fumble through exploration independently.

Use the Task tool with subagent_type: "Explore" for thorough codebase investigation:

Task:
  subagent_type: "Explore"
  description: "Explore <topic> codebase"
  prompt: |
    Thoroughly investigate <problem description>.
    Find: entry points, call chains, state management, platform-specific code,
    related tests, recent git changes, and any TODO/FIXME/HACK comments.
    Return: file paths, key code snippets, and a dependency map.

The exploration MUST produce a "Context Brief" covering:

1. Entry Point — where the user interaction starts (e.g., which screen/component)
2. Call Chain — the full execution path (Component → Hook → Service → API)
3. State Flow — what state/atoms/stores are involved and how data flows
4. Platform Branches — any .native.ts / .web.ts / .desktop.ts / .ext.ts variants
5. Key Code Snippets — the actual code (not just file paths) for the critical sections (keep each snippet under 50 lines)
6. Recent Changes — git log --oneline -10 on the relevant files to spot recent regressions
7. Related Issues — any TODO/FIXME/HACK comments or linked Jira tickets in the code
8. Current Behavior vs Expected — what the code does now vs what it should do

This Context Brief will be embedded in every agent's prompt. The quality of agent analysis is directly proportional to the quality of this brief.

Step 3: Create the Team

Use TeamCreate to create a team:

TeamCreate:
  team_name: "analysis-<short-topic>"
  description: "Multi-agent analysis for <problem summary>"

Step 4: Create Tasks (one per agent)

Use TaskCreate for each agent. These tasks go into the team's shared task list:

• Task #1: "Analyst A: root cause analysis" — conservative minimal fix
• Task #2: "Analyst B: best practices analysis" — industry standard approach
• Task #3: "Analyst C: creative analysis" — alternative approaches and edge cases

Step 5: Spawn 3 Team Members in Parallel

Spawn all 3 agents simultaneously in a single message with 3 Task tool calls. Each MUST use:

• team_name: "analysis-<short-topic>" — joins the team
• name: "analyst-a" (or b, c) — the agent's name for messaging
• subagent_type: "general-purpose" — full tool access
• NO run_in_background — team members must stay alive to receive messages

Agent Perspectives

Agent Prompt Template

Each agent receives this prompt. The Context Brief from Step 2 is embedded directly — agents should NOT need to do extensive exploration themselves.

You are "<agent-name>", one of three AI analysts on team "<team-name>".
Your teammates are "<other-agent-1>" and "<other-agent-2>".
You are a TEAM MEMBER (not a subagent) — you can send and receive messages.

## Problem Statement
<1-2 sentence problem description from user, after clarification>

## User Constraints
- Platform(s) affected: <from Step 1 clarification>
- Reproduction steps: <from Step 1 clarification>
- Constraints/deadlines: <from Step 1 clarification, or "None specified">

## Context Brief (from team lead's codebase exploration)

### Entry Point
<which screen/component/route triggers this behavior>

### Call Chain
<Component → Hook → Service → Background API — the full execution path>

### State Flow
<what atoms/stores/context are involved, how data flows between them>

### Platform-Specific Code
<any .native.ts / .web.ts / .desktop.ts / .ext.ts variants found, or "None">

### Key Code Snippets
<the actual code for critical sections — NOT just file paths. Each snippet should be
under 50 lines and include the file path + line range as a header>

Example format:
--- packages/kit/src/views/Market/components/Banner.tsx:42-67 ---
<code snippet>
--- packages/kit-bg/src/services/MarketService.ts:120-145 ---
<code snippet>

### Recent Git Changes
<git log --oneline -10 output for relevant files, to spot recent regressions>

### Related Issues & Tech Debt
<any TODO/FIXME/HACK comments, linked Jira ticket IDs, or known issues>

### Current Behavior vs Expected Behavior
- Current: <what the code does now>
- Expected: <what it should do>

## Your Analytical Perspective
<specific focus area for this agent>

## Instructions

1. Use TaskUpdate to claim task #<N> and set status to "in_progress"
2. Review the Context Brief above carefully. You may read additional files if
   needed, but the brief should cover 80%+ of what you need.
3. Analyze the problem from your unique perspective
4. Propose a concrete solution with exact code changes (show old code → new code)
5. Send your analysis to BOTH teammates using SendMessage:
   - Use type: "message", recipient: "<teammate-name>", include a summary
   - Send to EACH teammate separately (do NOT use broadcast)
6. After receiving analyses from your teammates, review their proposals
7. Send a refined FINAL report to the team lead with:
   - Approach name (1 sentence)
   - Exact file paths and code changes
   - Pros and cons
   - Why your approach is better than the others (or acknowledge if another is better)
8. Use TaskUpdate to mark your task as "completed"
9. Go idle and wait — the team lead will shut you down when done

## Communication Format

When messaging teammates:
- SendMessage type: "message"
- recipient: "<teammate-name>" (e.g., "analyst-b")
- content: your full analysis
- summary: "Analysis from <your-name>: <1-line summary>"

When messaging team lead:
- SendMessage type: "message"
- recipient: "team-lead" (or the lead's name from team config)
- content: your FINAL refined report
- summary: "Final report from <your-name>"

## Important
- You ONLY analyze and propose. Do NOT edit any files or implement changes.
- You MUST message your teammates, not just the lead. Cross-agent discussion is the whole point.
- After sending your analysis, WAIT for teammate messages before submitting your final report.
- The Context Brief is your primary source of truth. Only explore further if something is missing.

Step 6: Monitor and Facilitate

As team lead, your role is coordination:

1. Wait for idle notifications — teammates go idle after each turn, this is normal. They'll send you messages automatically when they have results.
2. Monitor cross-agent discussion — idle notifications include summaries of peer DMs. Verify agents are actually messaging each other.
3. Nudge if needed — if an agent hasn't messaged teammates after completing their analysis, send them a reminder:

   SendMessage type: "message", recipient: "analyst-a"
   content: "Please share your analysis with analyst-b and analyst-c before submitting your final report."
   

4. Collect final reports — wait for all 3 agents to send their final refined reports to you.
5. Update tasks — mark tasks as completed as agents submit final reports (if they haven't already).

Step 7: Present Comparison

After all 3 agents submit final reports:

1. Create a comparison table:

| Dimension | Analyst A (Conservative) | Analyst B (Best Practice) | Analyst C (Creative) |
|-----------|--------------------------|---------------------------|----------------------|
| Approach  | ...                      | ...                       | ...                  |
| Complexity| ...                      | ...                       | ...                  |
| Risk      | ...                      | ...                       | ...                  |
| Files changed | ...                  | ...                       | ...                  |
| Pros      | ...                      | ...                       | ...                  |
| Cons      | ...                      | ...                       | ...                  |

2. Add a recommendation with reasoning (but let user decide)
3. Present to user: "Which approach would you like to implement?"

Step 8: Cleanup

1. Send shutdown_request to all 3 agents (they will approve and exit)
2. Wait for all agents to shut down
3. Use TeamDelete to clean up team resources

SendMessage type: "shutdown_request", recipient: "analyst-a"
SendMessage type: "shutdown_request", recipient: "analyst-b"
SendMessage type: "shutdown_request", recipient: "analyst-c"
# Wait for all to shut down, then:
TeamDelete

Important Rules

• Agents ONLY analyze and propose. They do NOT implement code changes.
• All agents MUST communicate with each other via SendMessage (not just with team-lead).
• Agents must WAIT for teammate messages before submitting final reports.
• Always present the user with all 3 proposals before implementing anything.
• The user makes the final decision on which approach to use.
• Keep agents focused: each prompt should include the specific files to read.
• NEVER use run_in_background: true — this creates subagents that cannot communicate.
• Teammates go idle after each turn — this is NORMAL, not an error. Send them a message to wake them up.

Example Invocation

User: "Use multi-agent analysis to figure out why the modal doesn't close properly on desktop"

Response flow:

1. AskUserQuestion → "Which platforms? What triggers the bug? Any constraints?"
2. Explore agent → deep-dive into modal/desktop code, produce Context Brief
3. TeamCreate → create team "analysis-modal-close"
4. TaskCreate × 3 → one task per analyst
5. Task × 3 (in parallel, with team_name and name) → spawn 3 team members with full Context Brief
6. Wait for cross-agent discussion (agents message each other)
7. Collect final proposals from all 3 agents
8. Present comparison table to user
9. User picks → implement the chosen approach
10. Shutdown all agents → TeamDelete