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BytesAgain β€Ί Skills β€Ί Task Orchestrator
πŸ¦€ ClawHub

Task Orchestrator

by @openlark

Intelligent task management and execution coordination officer. Automatically generates task lists, intelligently decomposes complex tasks, matches AI agents...

Versionv1.0.0
Downloads618
#legal#automation#productivity
TERMINAL
clawhub install task-orchestrator

πŸ“– About This Skill

name: task-orchestrator description: Intelligent task management and execution coordination officer. Automatically generates task lists, intelligently decomposes complex tasks, matches AI agents, makes priority decisions, and monitors progress.

Task Orchestrator

End-to-end automated task management: from goals to execution, intelligent decomposition, agent matching, and progress monitoring.

Use Cases

  • User mentions keywords such as "task management," "task planning," "task decomposition," "multi-task parallelism," "task orchestration"
  • User needs to decompose complex objectives into executable steps
  • User needs multiple Agents to collaborate on work
  • User needs to track task progress and resource allocation
  • User needs intelligent decision-making for execution order and dependencies.

    • Core Capabilities

    1. Task Parsing and Decomposition

    Automatically decompose natural language objectives into a structured task tree:
  • Goal Decomposition: Break complex objectives into atomic tasks
  • Dependency Identification: Establish dependency relationships between tasks
  • Effort Estimation: Estimate execution time based on task complexity

    • 2. Intelligent Agent Matching

    Match the most suitable execution agent based on task characteristics:
  • Capability Matching: Select specialized agents based on task type
  • Load Balancing: Avoid agent overload
  • Cost Optimization: Balance quality and cost

    • 3. Priority Decision-Making

    Autonomously decide task execution order:
  • Urgency Assessment: Based on time constraints and impact scope
  • Value Assessment: Based on business value and user expectations
  • Dependency Priority: Ensure dependency chains execute correctly

    • 4. Progress Monitoring

    Track task execution status in real time:
  • Status Tracking: Pending, In Progress, Completed, Blocked
  • Anomaly Detection: Identify timed-out, failed, and blocked tasks
  • Automatic Retry: Intelligent retry strategy for failed tasks

    • Workflow

    User Goal β†’ Task Parsing β†’ Task Decomposition β†’ Dependency Analysis β†’ Priority Sorting β†’ Agent Matching β†’ Execution β†’ Monitoring β†’ Summary

    Step 1: Receive and Parse Goal

    Understand user intent and identify core objectives:

  • Clarify task boundaries and expected outputs
  • Identify time constraints and priority hints
  • Confirm available resources and constraints

    • Example Dialogue: 

    User: "Help me complete a product launch, including documentation, testing, and promotional materials"
Orchestrator: Parse goal into 3 main tasks:
  1. Product documentation writing (parallelizable)
  2. Test case design and execution (depends on partial completion of 1)
  3. Promotional material production (parallelizable)

    Step 2: Task Decomposition

    Use a script to generate a structured task tree:

    python3 scripts/task_decomposer.py --goal "User Goal" --output tasks.json

    Output structure: 

    {
  "main_goal": "Product Launch",
  "tasks": [
    {
      "id": "T1",
      "title": "Write Product Documentation",
      "description": "Includes feature descriptions, user guides, and API documentation",
      "priority": "high",
      "estimated_time": "2h",
      "dependencies": [],
      "subtasks": [
        {"id": "T1.1", "title": "Feature Description Document"},
        {"id": "T1.2", "title": "User Guide"},
        {"id": "T1.3", "title": "API Interface Documentation"}
      ],
      "required_skills": ["doc-writing-skill"],
      "status": "pending"
    }
  ]
}

    Step 3: Agent Matching and Resource Allocation

    Select execution agents based on task characteristics. See references/agent_matching.md for details.

    Step 4: Execution and Monitoring

    Initiate task execution and continuously monitor:

  • Execute tasks without dependencies in parallel
  • Execute tasks with dependencies serially
  • Update task status in real time
  • Automatically adjust plans upon anomalies

    • Step 5: Result Integration and Feedback

    After task completion:

  • Integrate execution results from each agent
  • Generate an execution report
  • Collect feedback to optimize subsequent tasks

    • Quick Start

    Scenario 1: Complex Task Decomposition

    User: "Help me prepare for next week's tech sharing session; I need a PPT, demo code, and a promotional poster"
    Orchestrator: 
1. Parse Goal β†’ Identify 3 parallel tasks
2. Decompose Tasks β†’ Estimate total effort 8h
3. Match Agents β†’ 
   - PPT: doc-writing-skill + ppt-parser-local
   - Demo: Code generation agent
   - Poster: image_generation
4. Suggest Execution Order β†’ PPT outline β†’ demo development β†’ poster design β†’ PPT refinement

    Scenario 2: Multi-Agent Collaboration

    User: "Complete a competitive analysis report; need data scraping, chart generation, and report writing"
    Orchestrator:
1. Task Decomposition: Data scraping (T1) β†’ Data analysis (T2) β†’ Chart generation (T3) β†’ Report writing (T4)
2. Dependency Chain: T1β†’T2β†’T3β†’T4
3. Agent Matching:
   - T1: web-search + deep-search-skill
   - T2: Data analysis agent
   - T3: image_generation
   - T4: doc-writing-skill
4. Execution Plan: Serial execution, estimated total duration 6h

    Decision Framework

    Priority Decision Matrix

    | Dimension | Weight | Scoring Criteria | |-----------|--------|------------------| | Urgency | 30% | Deadline, blocking impact | | Value | 40% | Business value, user expectations | | Cost | 20% | Time cost, resource consumption | | Risk | 10% | Failure risk, dependency risk |

    Agent Selection Strategy

    See references/agent_matching.md for details.

    Resource Files

    scripts/

  • task_decomposer.py - Task decomposition script, generates structured task tree
  • priority_calculator.py - Priority calculation script, supports custom weights
  • progress_monitor.py - Progress monitoring script, tracks task status in real time

    • references/

  • agent_matching.md - Agent matching strategies and capability matrix
  • workflow_patterns.md - Common workflow patterns and best practices
  • task_templates.md - Common task template library

    • assets/

  • task_plan_template.md - Task planning document template
  • execution_report_template.md - Execution report template

    • ⚑ When to Use

    TriggerAction
    - User needs to decompose complex objectives into executable steps
    - User needs multiple Agents to collaborate on work
    - User needs to track task progress and resource allocation
    - User needs intelligent decision-making for execution order and dependencies.

    πŸ’‘ Examples

    Scenario 1: Complex Task Decomposition

    User: "Help me prepare for next week's tech sharing session; I need a PPT, demo code, and a promotional poster"
    Orchestrator: 
1. Parse Goal β†’ Identify 3 parallel tasks
2. Decompose Tasks β†’ Estimate total effort 8h
3. Match Agents β†’ 
   - PPT: doc-writing-skill + ppt-parser-local
   - Demo: Code generation agent
   - Poster: image_generation
4. Suggest Execution Order β†’ PPT outline β†’ demo development β†’ poster design β†’ PPT refinement

    Scenario 2: Multi-Agent Collaboration

    User: "Complete a competitive analysis report; need data scraping, chart generation, and report writing"
    Orchestrator:
1. Task Decomposition: Data scraping (T1) β†’ Data analysis (T2) β†’ Chart generation (T3) β†’ Report writing (T4)
2. Dependency Chain: T1β†’T2β†’T3β†’T4
3. Agent Matching:
   - T1: web-search + deep-search-skill
   - T2: Data analysis agent
   - T3: image_generation
   - T4: doc-writing-skill
4. Execution Plan: Serial execution, estimated total duration 6h