# Multi Agent Task Orchestrator > Implements intelligent multi agent task orchestrator with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/multi-agent-task-orchestrator` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/multi-agent-task-orchestrator` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/multi-agent-task-orchestrator/raw - Safety review: pending - Works with: Claude Code, Claude.ai, OpenAI Codex - Category: AI & ML - License: MIT - Author: paulpas (https://skillmd.com/u/paulpas) - Updated: 2026-09-17 - Page: https://skillmd.com/skills/paulpas/multi-agent-task-orchestrator --- # 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 ```python 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 ```python 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 | --- --- ## 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](https://langchain-ai.github.io/langgraph/concepts/multi_agent/) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [Multi-Agent System Survey — arXiv (2402.16817)](https://arxiv.org/abs/2402.16817) - [LLM Agent Survey — Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/) - [Agent Orchestration — LangChain Blog](https://blog.langchain.dev/orchestrate-multi-agent-systems-with-langgraph/)