# magentic-orchestrator

- Author: Zhdan Parfenov
- Repository: ZhdanDesign/Opencode
- Version: 20260125234159
- Stars: 0
- Forks: 0
- Last Updated: 2026-02-08
- Source: https://github.com/ZhdanDesign/Opencode
- Web: https://mule.run/skillshub/@@ZhdanDesign/Opencode~magentic-orchestrator:20260125234159

---

# Magentic Orchestrator Skill

**Purpose**: Self-planning orchestration with dynamic workflow generation and adaptive execution

## Core Capabilities

### Self-Planning
- Automatic workflow decomposition from high-level goals
- Dynamic agent selection and composition
- Real-time plan adaptation based on execution feedback
- Goal-oriented task generation and prioritization

### Adaptive Execution
- Runtime plan modification based on results
- Automatic retry with alternative strategies
- Learning from execution patterns
- Optimization of agent sequences based on success rates

### Meta-Cognition
- Planning about planning (meta-planning)
- Strategy selection and evaluation
- Self-reflection and improvement
- Knowledge accumulation for future planning

### Emergent Behavior
- Complex workflow patterns emerging from simple rules
- Spontaneous optimization opportunities
- Unexpected but effective agent combinations
- Creative problem-solving approaches

## Implementation Pattern

```json
{
  "workflow": {
    "name": "self-planning-system",
    "type": "magentic",
    "goal": "Create a production-ready microservice",
    "planningDepth": 3,
    "adaptationThreshold": 0.7,
    "agents": ["analyzer", "designer", "coder", "tester", "deployer"],
    "planningRules": [
      {
        "condition": "goal.complexity > 0.8",
        "action": "decompose_goal",
        "maxSubtasks": 5
      },
      {
        "condition": "execution.success_rate < 0.6",
        "action": "replan_with_alternative_strategy"
      },
      {
        "condition": "feedback.novel_opportunity",
        "action": "explore_emergent_path"
      }
    ],
    "learning": {
      "storeExecutionPatterns": true,
      "optimizeAgentSequences": true,
      "adaptTimeouts": true,
      "strategyEvolution": true
    }
  }
}
```

## Advanced Features

### Dynamic Goal Decomposition
- Recursive goal breaking into manageable subtasks
- Dependency analysis and critical path identification
- Resource estimation and timeline prediction
- Risk assessment and mitigation planning

### Real-time Adaptation
- Performance monitoring and bottleneck detection
- Automatic resource reallocation
- Strategy switching based on environmental changes
- Emergency response and recovery planning

### Knowledge Evolution
- Pattern recognition from successful workflows
- Failure analysis and avoidance learning
- Best practice extraction and codification
- Cross-domain knowledge transfer

## Integration Points

- **Perplexity Researcher**: Knowledge gathering for planning
- **Skill Improver**: Workflow optimization based on execution
- **Notification Hub**: Real-time plan adaptation notifications
- **Workflow Registry**: Storage of evolved patterns

## Use Cases

- Complex software development projects
- Research and discovery workflows
- Adaptive problem-solving systems
- Autonomous system optimization
- Creative AI collaboration patterns