Skill: openlark/task-orchestrator (version 1.0.0)

--- 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 → Task Parsing → Task Decomposition → Dependency Analysis → Priority Sorting → Agent Matching → Execution → Monitoring → 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: ```bash python3 scripts/task_decomposer.py --goal "User Goal" --output tasks.json ``` Output structure: ```json { "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](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 → Identify 3 parallel tasks 2. Decompose Tasks → Estimate total effort 8h 3. Match Agents → - PPT: doc-writing-skill + ppt-parser-local - Demo: Code generation agent - Poster: image_generation 4. Suggest Execution Order → PPT outline → demo development → poster design → 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) → Data analysis (T2) → Chart generation (T3) → Report writing (T4) 2. Dependency Chain: T1→T2→T3→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](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


Security scan verdicts — ClawScan: clean | static analysis: clean | VirusTotal: clean | SkillSpector: clean (LOW)