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Coordinator Mode

CommunityPopular
vudovn
coordinator-mode

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.

Overview

Publishervudovn
Repositoryag-kit
Skill namecoordinator-mode
Stars
8.2K
Forks
1.5K
Bundled files
Instructions only
LicenseMIT
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by vudovn on GitHub. Read the source before you install it.

Installation

Install the Coordinator Mode AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/vudovn/ag-kit/tree/main/.agents/skills/coordinator-mode
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/vudovn/ag-kit.git /tmp/ag-kit
mkdir -p .claude/skills
cp -r /tmp/ag-kit/.agents/skills/coordinator-mode .claude/skills/coordinator-mode
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Coordinator Mode in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Coordinator Mode on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Coordinator Mode is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

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.


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

Phase-Based Workflow

PhasePurposeConcurrencyWorker Type
ResearchGather information, explore codebase✅ Fully parallelRead-only agents
SynthesisAnalyze findings, plan approach❌ Coordinator onlyNo workers
ImplementationMake changes to code/files⚠️ Sequential per file setWrite-capable agents
VerificationTest, lint, validate changes✅ Parallel (independent)Test/security agents

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


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

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.

Fork Semantics

When to Fork (vs. Spawn Fresh Agent)

ScenarioActionWhy
Open-ended research questionFork (omit agent type)Fork inherits context, shorter prompt
Independent parallel researchFork multiple in one messageEach fork shares cache
Specialized domain workSpawn (specify agent type)Fresh specialist starts clean
Second opinion on your workSpawnIndependent 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).


Anti-Patterns

Anti-PatternWhy It's BadFix
"Look at the code and fix it"Zero context = workers make wrong assumptionsProvide specific file paths, line numbers, and what to change
Skipping Research phaseImplementation without understanding = reworkAlways research first, even briefly
Launching 10+ workers at onceOverwhelms synthesis, diminishing returns2-5 workers per round, synthesize, then more if needed
Fabricating worker resultsUser gets wrong informationWait for actual notification, say "still running" if asked
Coordinator writes code directlyLoses orchestration benefitsAlways delegate to appropriate specialist

Synthesis Protocol

After all workers complete:

markdown
## 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]

Continue vs. Spawn Decision

SituationAction
Worker completed, need follow-up on same topicContinue (send message to worker ID)
Worker completed, need different domain workSpawn new specialist
Worker failed, need retry with same contextContinue with corrected instructions
Worker failed, need fundamentally different approachSpawn new

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

Frequently asked questions

What does the Coordinator Mode AI skill do?

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.

Why use Coordinator Mode on TypingMind?

Because you install it once and use it with any model. Coordinator Mode is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Coordinator Mode in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/vudovn/ag-kit/tree/main/.agents/skills/coordinator-mode. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Coordinator Mode?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Coordinator Mode?

As many as you like. As long as a model supports skills, you can use Coordinator Mode with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Coordinator Mode AI skill free?

Yes. It is published on GitHub by vudovn under the MIT license. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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