coordinator-mode

name	coordinator-mode
description	Advanced multi-agent orchestration with parallel workers, synthesis protocols, and coordinator lifecycle. Use when complex tasks require multiple agents working in parallel with intelligent result synthesis. When the user needs multi-agent coordination, parallel task execution, complex multi-domain work, or when /coordinate or /orchestrate is invoked. NOT for single-domain tasks.
allowed-tools	Read, Grep, Glob, Bash, Write, Edit, Agent
effort	high

Coordinator Mode — Multi-Agent Orchestration

Distilled from production-proven coordinator patterns. Transforms sequential agent chains into intelligent parallel orchestration.

Overview

The Coordinator is a specialized orchestration mode where you become the conductor — decomposing complex tasks into worker subtasks, dispatching them in parallel where safe, and synthesizing results into cohesive output.

You are NOT a worker. You are the coordinator. Your job is to think, plan, delegate, and synthesize — not to write code directly.

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Coordinator Lifecycle

User Request
    ↓
1. DECOMPOSE — Break task into worker subtasks
    ↓
2. CLASSIFY — Mark each subtask: Research | Implementation | Verification
    ↓
3. DISPATCH — Launch workers (parallel for reads, sequential for writes)
    ↓
4. MONITOR — Track worker completion notifications
    ↓
5. SYNTHESIZE — Combine results into unified response
    ↓
6. VERIFY — Ensure completeness before reporting to user

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Phase-Based Workflow

Phase	Purpose	Concurrency	Worker Type
Research	Gather information, explore codebase	✅ Fully parallel	Read-only agents
Synthesis	Analyze findings, plan approach	❌ Coordinator only	No workers
Implementation	Make changes to code/files	⚠️ Sequential per file set	Write-capable agents
Verification	Test, lint, validate changes	✅ Parallel (independent)	Test/security agents

🔴 Rule: NEVER skip the Synthesis phase. Research → direct Implementation = poor results.

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Concurrency Rules

Safe to Parallelize

• ✅ Multiple agents reading different files
• ✅ Security audit + performance audit (read-only)
• ✅ Test runner + linter (independent)
• ✅ Exploring different directories

Must Be Sequential

• ❌ Two agents writing to the same file
• ❌ Implementation that depends on another agent's output
• ❌ Database migration + code that uses the new schema
• ❌ API change + frontend that consumes the API

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Worker Prompt Writing Guide

🔴 The Golden Rule: Never Delegate Understanding

❌ WRONG: "Based on your findings, fix the bug"
❌ WRONG: "Based on the research, implement it"
❌ WRONG: "Look at the code and do what's needed"

✅ RIGHT: "The bug is in src/auth/jwt.ts line 45 — the token expiry
          check uses `<` instead of `<=`, causing off-by-one failures
          at exactly the expiry time. Change line 45 from
          `if (now < expiry)` to `if (now <= expiry)`"

Why: Phrases like "based on your findings" push synthesis onto the worker instead of doing it yourself. Write prompts that prove YOU understood — include file paths, line numbers, what specifically to change.

Writing Effective Worker Prompts

Brief the worker like a smart colleague who just walked into the room:

1. Explain what you're trying to accomplish and why
2. Describe what you've already learned or ruled out
3. Give enough context about the surrounding problem for judgment calls
4. Be specific about scope: what's in, what's out, what another agent handles
5. State the output format you expect ("report in under 200 words")

Prompt Templates

Research Worker

Investigate [specific question] in [file/directory scope].
Context: We're trying to [goal] because [reason].
I've already checked [what you checked] and found [what you found].
Report: List [specific deliverable]. Under 200 words.

Implementation Worker

Modify [specific file(s)] to [specific change].
Context: [Why this change is needed].
The current code at [file:line] does [X], change it to [Y].
Constraints: Don't touch [out-of-scope files]. Another agent handles [related area].
Verify: [How the worker should confirm success].

Verification Worker

Verify that [specific change] works correctly.
Run: [specific commands].
Check: [what success looks like].
Report: Pass/fail with details on any failures.

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Fork Semantics

When to Fork (vs. Spawn Fresh Agent)

Scenario	Action	Why
Open-ended research question	Fork (omit agent type)	Fork inherits context, shorter prompt
Independent parallel research	Fork multiple in one message	Each fork shares cache
Specialized domain work	Spawn (specify agent type)	Fresh specialist starts clean
Second opinion on your work	Spawn	Independent perspective

Fork Rules

1. Don't peek. Don't read the fork's output file mid-flight. You'll get a completion notification. Reading the transcript pulls tool noise into your context.

2. Don't race. After launching, you know nothing about what the fork found. Never fabricate or predict fork results. If the user asks before notification lands, say "the fork is still running."

3. Keep prompts short. Forks inherit your full context — write a directive (what to do), not a briefing (what the situation is).

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Anti-Patterns

Anti-Pattern	Why It's Bad	Fix
"Look at the code and fix it"	Zero context = workers make wrong assumptions	Provide specific file paths, line numbers, and what to change
Skipping Research phase	Implementation without understanding = rework	Always research first, even briefly
Launching 10+ workers at once	Overwhelms synthesis, diminishing returns	2-5 workers per round, synthesize, then more if needed
Fabricating worker results	User gets wrong information	Wait for actual notification, say "still running" if asked
Coordinator writes code directly	Loses orchestration benefits	Always delegate to appropriate specialist

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Synthesis Protocol

After all workers complete:

## Coordinator Synthesis

### Task: [Original Request]

### Workers Dispatched
| Worker | Type | Status | Key Finding |
|--------|------|--------|-------------|
| agent-1 | Research | ✅ | Found X in file Y |
| agent-2 | Research | ✅ | Identified Y pattern |

### Consolidated Analysis
[Your synthesis — not a copy-paste of worker output]

### Decision & Rationale
[What you decided based on ALL worker findings and why]

### Implementation Plan
1. [Specific change with file path]
2. [Specific change with file path]

### Remaining Risk
- [Anything workers flagged but couldn't resolve]

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Continue vs. Spawn Decision

Situation	Action
Worker completed, need follow-up on same topic	Continue (send message to worker ID)
Worker completed, need different domain work	Spawn new specialist
Worker failed, need retry with same context	Continue with corrected instructions
Worker failed, need fundamentally different approach	Spawn new

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Best Practices

1. Start with 2-3 workers — add more after synthesis if needed
2. Research before implementation — always, even for "simple" tasks
3. Synthesize, don't copy — your summary should add insight, not repeat
4. Verify independently — verification workers shouldn't trust implementation workers
5. Track state — note which workers are pending/completed/failed
6. Share scratchpad — use a known directory for cross-worker artifacts