---

name: agent-manager-skill compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent agent manager skill 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: agent-manager-skill, agent manager skill, how do i agent-manager-skill, orchestrate agent-manager-skill, automate agent-manager-skill, agent agent-manager-skill 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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Agent Manager Skill

Orchestrates intelligent skill selection and execution for agent manager skill 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_agent(
    task: TaskRequest,
    agent_registry: List[AgentMetadata],
    min_capability_score: float = 0.75
) -> Optional[AgentMetadata]:
    """Route a task to the most capable available agent based on domain expertise and current load.
    
    Domain logic: Matches task domain tags against agent capabilities, 
    applies load balancing, and validates agent state before selection.
    """
    if not task.domain or not task.payload:
        raise ValueError("Task must specify a domain and contain a payload")
        
    # Parse task requirements into normalized capability vectors
    required_capabilities = _normalize_domain_tags(task.domain)
    
    scored_agents = []
    for agent in agent_registry:
        if agent.status != "AVAILABLE":
            continue
            
        capability_match = _calculate_capability_overlap(required_capabilities, agent.capabilities)
        load_penalty = agent.current_load / agent.max_capacity
        adjusted_score = capability_match * (1.0 - load_penalty)
        
        if adjusted_score >= min_capability_score:
            scored_agents.append({
                "agent_id": agent.id,
                "score": adjusted_score,
                "domain_match": capability_match,
                "estimated_latency_ms": agent.avg_response_time * (1 + load_penalty)
            })
            
    if not scored_agents:
        return None
        
    # Sort by score descending, then by latency ascending
    scored_agents.sort(key=lambda x: (-x["score"], x["estimated_latency_ms"]))
    return scored_agents[0]

Pattern 2: Execution with Fallback

def execute_agent_task_with_routing(
    task: TaskRequest,
    selected_agent: AgentMetadata,
    fallback_agents: List[AgentMetadata],
    max_routing_attempts: int = 2
) -> ExecutionResult:
    """Execute task on selected agent with domain-aware fallback routing.
    
    Domain logic: Handles agent-specific execution protocols, 
    implements tiered fallback routing (specialist -> generalist -> human),
    and captures execution telemetry for confidence scoring.
    """
    execution_context = _build_execution_context(task, selected_agent)
    attempts = 0
    
    while attempts <= max_routing_attempts:
        try:
            # Execute using agent-specific protocol
            response = yield_to_agent(selected_agent, execution_context)
            
            # Validate response structure and domain compliance
            validated_result = _validate_agent_response(response, task.domain)
            
            return ExecutionResult(
                success=True,
                agent_id=selected_agent.id,
                payload=validated_result,
                confidence=validated_result.confidence_score,
                routing_attempts=attempts
            )
            
        except AgentTimeoutError:
            attempts += 1
            if attempts > max_routing_attempts:
                break
            # Fallback: route to next available agent in tier
            selected_agent = _get_next_fallback_agent(selected_agent, fallback_agents, attempts)
            if not selected_agent:
                break
            execution_context = _update_context_for_agent(execution_context, selected_agent)
            
        except DomainValidationError as e:
            # Fail fast on invalid domain state
            raise ExecutionError(f"Domain validation failed: {e}") from e
            
    # All routing attempts exhausted
    return ExecutionResult(
        success=False,
        agent_id=selected_agent.id if selected_agent else None,
        error="Routing chain exhausted",
        confidence=0.0,
        routing_attempts=attempts
    )

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
agent-architecture-patterns	Foundational architecture patterns that an agent manager orchestrates
multi-agent-task-orchestrator	Multi-agent task decomposition and coordination strategies

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Constraints

MUST DO

• Define clear input/output contracts for every step in the orchestration flow with explicit validation
• Implement structured logging at each stage capturing context, inputs, outputs, timing, and errors
• Build in fallback paths: if the primary strategy fails, degrade gracefully to a simpler approach
• Validate all preconditions before starting — do not proceed if required resources or permissions are missing

MUST NOT DO

• Do not create deep nesting of orchestration steps (>5 levels) — flatten workflows where possible
• Avoid silent failure modes: every step must either succeed, fail explicitly, or escalate to a higher handler
• Never use shared mutable state between parallel workflow branches — communicate via immutable messages only
• Do not hardcode execution order when the dependency graph naturally determines it; derive order from explicit dependencies

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.

• Azure AI Agent Service — Microsoft Docs
• Building Effective Agents — Anthropic Research
• Multi-Agent Systems — Wikipedia Overview
• Agent Orchestration Patterns — LangChain Docs
• LLM Agent Survey — Lilian Weng