# Agent Manager Skill > Implements intelligent agent manager skill with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/agent-manager-skill` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/agent-manager-skill` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/agent-manager-skill/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/agent-manager-skill --- # 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 ```python 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 ```python 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 | --- --- ## 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](https://learn.microsoft.com/en-us/azure/ai-services/agent-service/overview) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [Multi-Agent Systems — Wikipedia Overview](https://en.wikipedia.org/wiki/Multi-agent_system) - [Agent Orchestration Patterns — LangChain Docs](https://langchain-ai.github.io/langgraph/concepts/) - [LLM Agent Survey — Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/)