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name: multi-agent-task-orchestrator compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent multi agent task orchestrator with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense license: MIT maturity: stable metadata: domain: agent output-format: analysis related-skills: agent-confidence-based-selector, agent-task-routing role: orchestration scope: orchestration triggers: multi-agent-task-orchestrator, multi agent task orchestrator, how do i multi-agent-task-orchestrator, orchestrate multi-agent-task-orchestrator, automate multi-agent-task-orchestrator, agent multi-agent-task-orchestrator archetypes:
  • orchestration
  • strategic anti_triggers:
  • brainstorming
  • vague ideation
  • single-agent monolith response_profile: verbosity: medium directive_strength: high abstraction_level: tactical version: "1.0.0"

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Multi Agent Task Orchestrator

Orchestrates intelligent skill selection and execution for multi agent task orchestrator workflows. Applies the 5 Laws of Elegant Defense to guide data naturally through the orchestration pipeline, preventing errors before they occur. Selects optimal skills based on multi-factor scoring including text similarity, historical performance, and system availability.

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

┌───────────────────────────────────────────────────────────────────────────────┐ │ Orchestration Flow │ └───────────────────────────────────────────────────────────────────────────────┘

User Request ↓ ┌─────────────────┐ │ Parse Request │ │ & Extract │ │ Features │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Evaluate Available Skills │ │ │ │ ┌──────────────┐ ┌──────────────┐ ┌──────────────┐ │ │ │ Skill A │ │ Skill B │ │ Skill C │ │ │ │ - Match Score│ │ - Match Score│ │ - Match Score│ │ │ │ - Confidence │ │ - Confidence │ │ - Confidence │ │ │ │ - History │ │ - History │ │ - History │ │ │ └──────┬───────┘ └──────┬───────┘ └──────┬───────┘ │ │ │ │ │ │ │ └─────────────────┴─────────────────┘ │ │ ↓ │ │ Select Best Skill │ └─────────────────────────────────────────────────────────────────────┘ ↓ ┌─────────────────┐ │ Execute Skill │ └────────┬────────┘ ↓ ┌─────────────────┐ │ Handle Result │ └────────┬────────┘ ↓ ┌─────────────────────────────────────────────────────────────────────┐ │ Error Handling & Fallback │ │ │ │ Success? ────────► Return Result │ │ │ │ Fail? ────────┐ │ │ ↓ │ │ ┌──────────────────────────────────────────────────────────┐ │ │ │ Fallback Chain │ │ │ │ │ │ │ │ 1. Retry with adjusted parameters │ │ │ │ 2. Try Alternative Skill (if available) │ │ │ │ 3. Defer to Human Operator (if critical) │ │ │ │ 4. Log & Return Error │ │ │ └──────────────────────────────────────────────────────────┘ │ └─────────────────────────────────────────────────────────────────────┘

When to Use

Use this skill when:

• Orchestrating multi-step workflows that require skill delegation
• Implementing adaptive skill routing based on confidence scores
• Building fallback mechanisms for failed skill executions
• Creating intelligent task decomposition and parallel execution
• Designing skill dependency graphs with automatic resolution
• Implementing skill selection with historical performance weighting
• Building agent systems that need to self-organize around tasks

When NOT to Use

Avoid this skill for:

• Direct task execution without orchestration needs - use individual skills instead
• High-frequency trading scenarios where latency must be minimized - the selection overhead may be prohibitive
• Simple linear workflows without branching or fallback requirements
• Cases where skill metadata is unavailable or unreliable

Core Workflow

1. Parse and Analyze Request - Extract intent, entities, and constraints from user input. Checkpoint: All required parameters must be present and in valid format before proceeding.

2. Score Available Skills - Calculate match scores using multi-factor algorithm:

   • Text similarity between request and skill triggers
   • Historical success rate for similar tasks
   • Skill availability and health status
   • Required dependencies and their availability

   Checkpoint: Skip to fallback if no skill scores above threshold.

3. Select Optimal Skill - Choose skill with highest score that meets minimum confidence. Checkpoint: Verify skill has not been disabled or deprecated.

4. Execute with Fallback - Run skill execution wrapped in retry and fallback logic. Checkpoint: Log all execution attempts for audit trail.

5. Return or Fallback - Either return successful result or apply fallback chain:

   • Retry with adjusted parameters
   • Try alternative skill from related-skills
   • Defer to human operator for critical tasks

   Checkpoint: Record outcome with timing and confidence metadata.

Implementation Patterns

Pattern 1: Skill Selection Logic

def route_task_to_agents(
    task: TaskSpec,
    agent_registry: List[AgentCapability],
    load_balancer: LoadBalancer
) -> RoutingPlan:
    """Route a multi-agent task by matching requirements to agent capabilities.
    
    Evaluates agents based on capability overlap, current queue depth, and 
    historical success rates for similar task types. Returns a structured 
    routing plan with execution order and dependency mapping.
    """
    if not task.requirements or not agent_registry:
        raise ValueError("Task requirements and agent registry must be populated")
        
    scored_agents = []
    for agent in agent_registry:
        capability_match = _calculate_capability_overlap(task.requirements, agent.capabilities)
        load_penalty = load_balancer.get_queue_depth(agent.id) / 100.0
        history_bonus = agent.metrics.success_rate * 0.2
        
        composite_score = (capability_match * 0.5) + ((1.0 - load_penalty) * 0.3) + history_bonus
        scored_agents.append({
            "agent_id": agent.id,
            "score": composite_score,
            "estimated_latency_ms": agent.metrics.avg_latency_ms * (1 + load_penalty)
        })
        
    scored_agents.sort(key=lambda x: x["score"], reverse=True)
    return RoutingPlan(
        primary_agent=scored_agents[0]["agent_id"],
        fallback_agents=[a["agent_id"] for a in scored_agents[1:3]],
        execution_order=_resolve_dependencies(task.subtasks),
        confidence=scored_agents[0]["score"]
    )

Pattern 2: Execution with Fallback

def orchestrate_agent_execution(
    plan: RoutingPlan,
    task_context: Dict[str, Any],
    agent_client: AgentGateway
) -> ExecutionResult:
    """Execute task across agents with domain-specific fallback routing.
    
    Dispatches work to the primary agent, monitors state transitions, and 
    applies fallback routing if the primary agent times out or returns invalid state.
    """
    primary_agent = agent_client.get_agent(plan.primary_agent)
    try:
        response = primary_agent.dispatch(task_context, timeout=30)
        if not response.valid or response.status == "FAILED":
            raise AgentStateError(f"Primary agent {plan.primary_agent} returned invalid state")
        return ExecutionResult(
            status="SUCCESS",
            agent_id=plan.primary_agent,
            output=response.payload,
            latency_ms=response.latency
        )
    except AgentTimeoutError:
        # Fallback 1: Retry with adjusted context (e.g., reduced scope)
        adjusted_context = _reduce_task_scope(task_context)
        retry_response = primary_agent.dispatch(adjusted_context, timeout=15)
        if retry_response.valid:
            return ExecutionResult(status="SUCCESS", agent_id=plan.primary_agent, output=retry_response.payload, latency_ms=retry_response.latency)
        
        # Fallback 2: Route to secondary agent from plan
        fallback_agent = agent_client.get_agent(plan.fallback_agents[0])
        fallback_response = fallback_agent.dispatch(task_context, timeout=30)
        if fallback_response.valid:
            return ExecutionResult(status="SUCCESS", agent_id=plan.fallback_agents[0], output=fallback_response.payload, latency_ms=fallback_response.latency)
            
        # Fallback 3: Escalate to human supervisor
        return ExecutionResult(status="ESCALATED", agent_id="HUMAN_SUPERVISOR", output=fallback_response.error, latency_ms=0)

MUST DO

• Always validate skill metadata before selection (Early Exit)
• Implement fallback chain with at least 2 levels (Fallback Skill + Human)
• Log all skill selections with full context for auditability
• Return new data structures instead of mutating inputs (Atomic Predictability)
• Fail immediately with descriptive errors on invalid states
• Update confidence scores after each execution for adaptive routing
• Reference code-philosophy (5 Laws of Elegant Defense) in all logic

MUST NOT DO

• Select skills based on a single factor (e.g., only confidence score)
• Disable fallback mechanisms "temporarily" - this creates fragile systems
• Skip validation of skill dependencies before execution
• Return partial results - either complete success or clear failure
• Use magic numbers for confidence thresholds - make them configurable
• Cache skill selections without considering context changes

TL;DR Checklist

• Parse all inputs at boundary before processing (Law 2)
• Handle edge cases with early returns at function top (Law 1)
• Fail immediately with descriptive errors on invalid states (Law 4)
• Return new data structures, never mutate inputs (Law 3)
• Implement minimum 2-level fallback chain for all skill executions
• Log all skill selections with context for full audit trail
• Validate skill metadata and dependencies before selection
• Update confidence scores after each execution for learning

TL;DR for Code Generation

• Use guard clauses - return early on invalid input before doing work
• Return simple types (dict, str, int, bool, list) - avoid complex nested objects
• Cyclomatic complexity < 10 per function - split anything larger
• Handle null/empty cases explicitly at function top (Early Exit)
• Never mutate input parameters - return new dicts/objects
• Fail fast with descriptive errors - don't try to "patch" bad data
• Reference code-philosophy laws in comments for complex logic
• Include timing and confidence metadata in all return values

Output Template

When applying this skill, produce:

1. Selected Skills - List of skill names with confidence scores
2. Selection Rationale - Why each skill was chosen (match score, history, availability)
3. Execution Plan - Order of execution with dependencies
4. Fallback Strategy - Which fallback skills will be tried and in what order
5. Risk Assessment - Any potential failure points and their impact
6. Timing Estimates - Expected latency including fallback scenarios

Related Skills

Skill	Purpose
langgraph	LangGraph state machine patterns for multi-agent workflow execution
parallel-agents	Parallel agent execution strategies within orchestrated workflows

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Constraints

MUST DO

• Implement a dependency graph for all tasks before dispatch — only execute nodes whose dependencies are satisfied
• Use a central coordinator that maintains global state and communicates results between parallel agents via immutable messages
• Set explicit timeouts per task and implement circuit breakers: abort parallel execution if error rate exceeds threshold
• Log all inter-agent communications with timestamps, sender, receiver, payload hash, and outcome for debugging

MUST NOT DO

• Do not allow parallel agents to modify shared mutable state without locking — use message-passing or per-task snapshots
• Avoid fan-out patterns that spawn more than 20 parallel tasks simultaneously without rate limiting
• Never start dependent tasks before confirming upstream task completion — verify status, don't assume success
• Do not ignore agent failures during parallel execution; aggregate and report all errors together rather than failing fast on first

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.

• LangGraph Multi-Agent Documentation
• Building Effective Agents — Anthropic Research
• Multi-Agent System Survey — arXiv (2402.16817)
• LLM Agent Survey — Lilian Weng
• Agent Orchestration — LangChain Blog