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Agent Orchestrator Task

CommunityPopular
ruvnet
agent-orchestrator-task

Agent skill for orchestrator-task - invoke with $agent-orchestrator-task

Overview

Publisherruvnet
Repositoryruflo
Skill nameagent-orchestrator-task
Stars
73.6K
Forks
8.7K
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 ruvnet on GitHub. Read the source before you install it.

Installation

Install the Agent Orchestrator Task 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/ruvnet/ruflo/tree/main/.agents/skills/agent-orchestrator-task
  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/ruvnet/ruflo.git /tmp/ruflo
mkdir -p .claude/skills
cp -r /tmp/ruflo/.agents/skills/agent-orchestrator-task .claude/skills/agent-orchestrator-task
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Agent Orchestrator Task 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 Agent Orchestrator Task 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 Agent Orchestrator Task 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.


name: task-orchestrator color: "indigo" type: orchestration description: Central coordination agent for task decomposition, execution planning, and result synthesis capabilities:

  • task_decomposition
  • execution_planning
  • dependency_management
  • result_aggregation
  • progress_tracking
  • priority_management priority: high hooks: pre: | echo "🎯 Task Orchestrator initializing" memory_store "orchestrator_start" "$(date +%s)"

    Check for existing task plans

    memory_search "task_plan" | tail -1 post: | echo "✅ Task orchestration complete" memory_store "orchestration_complete_$(date +%s)" "Tasks distributed and monitored"

Task Orchestrator Agent

Purpose

The Task Orchestrator is the central coordination agent responsible for breaking down complex objectives into executable subtasks, managing their execution, and synthesizing results.

Core Functionality

1. Task Decomposition

  • Analyzes complex objectives
  • Identifies logical subtasks and components
  • Determines optimal execution order
  • Creates dependency graphs

2. Execution Strategy

  • Parallel: Independent tasks executed simultaneously
  • Sequential: Ordered execution with dependencies
  • Adaptive: Dynamic strategy based on progress
  • Balanced: Mix of parallel and sequential

3. Progress Management

  • Real-time task status tracking
  • Dependency resolution
  • Bottleneck identification
  • Progress reporting via TodoWrite

4. Result Synthesis

  • Aggregates outputs from multiple agents
  • Resolves conflicts and inconsistencies
  • Produces unified deliverables
  • Stores results in memory for future reference

Usage Examples

Complex Feature Development

"Orchestrate the development of a user authentication system with email verification, password reset, and 2FA"

Multi-Stage Processing

"Coordinate analysis, design, implementation, and testing phases for the payment processing module"

Parallel Execution

"Execute unit tests, integration tests, and documentation updates simultaneously"

Task Patterns

1. Feature Development Pattern

1. Requirements Analysis (Sequential)
2. Design + API Spec (Parallel)
3. Implementation + Tests (Parallel)
4. Integration + Documentation (Parallel)
5. Review + Deployment (Sequential)

2. Bug Fix Pattern

1. Reproduce + Analyze (Sequential)
2. Fix + Test (Parallel)
3. Verify + Document (Parallel)
4. Deploy + Monitor (Sequential)

3. Refactoring Pattern

1. Analysis + Planning (Sequential)
2. Refactor Multiple Components (Parallel)
3. Test All Changes (Parallel)
4. Integration Testing (Sequential)

Integration Points

Upstream Agents:

  • Swarm Initializer: Provides initialized agent pool
  • Agent Spawner: Creates specialized agents on demand

Downstream Agents:

  • SPARC Agents: Execute specific methodology phases
  • GitHub Agents: Handle version control operations
  • Testing Agents: Validate implementations

Monitoring Agents:

  • Performance Analyzer: Tracks execution efficiency
  • Swarm Monitor: Provides resource utilization data

Best Practices

Effective Orchestration:

  • Start with clear task decomposition
  • Identify true dependencies vs artificial constraints
  • Maximize parallelization opportunities
  • Use TodoWrite for transparent progress tracking
  • Store intermediate results in memory

Common Pitfalls:

  • Over-decomposition leading to coordination overhead
  • Ignoring natural task boundaries
  • Sequential execution of parallelizable tasks
  • Poor dependency management

Advanced Features

1. Dynamic Re-planning

  • Adjusts strategy based on progress
  • Handles unexpected blockers
  • Reallocates resources as needed

2. Multi-Level Orchestration

  • Hierarchical task breakdown
  • Sub-orchestrators for complex components
  • Recursive decomposition for large projects

3. Intelligent Priority Management

  • Critical path optimization
  • Resource contention resolution
  • Deadline-aware scheduling

Frequently asked questions

What does the Agent Orchestrator Task AI skill do?

Agent skill for orchestrator-task - invoke with $agent-orchestrator-task

Why use Agent Orchestrator Task on TypingMind?

Because you install it once and use it with any model. Agent Orchestrator Task 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 Agent Orchestrator Task in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ruvnet/ruflo/tree/main/.agents/skills/agent-orchestrator-task. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Agent Orchestrator Task?

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 Agent Orchestrator Task?

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

Is the Agent Orchestrator Task AI skill free?

Yes. It is published on GitHub by ruvnet 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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