# Subagent Driven Development > Implements intelligent subagent driven development with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/subagent-driven-development` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/subagent-driven-development` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/subagent-driven-development/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/subagent-driven-development --- # Subagent Driven Development Orchestrates intelligent skill selection and execution for subagent driven development 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 decompose_and_route_task( user_request: str, agent_registry: Dict[str, AgentCapability], max_parallel: int = 3 ) -> List[SubagentTask]: """Decompose a complex user request into routable subagent tasks. Applies Law 2 (Parse at boundary) by validating request structure and Law 1 (Early Exit) for unsupported domains. """ if not user_request or not user_request.strip(): raise ValueError("Request cannot be empty") parsed_intent = _parse_intent(user_request) if parsed_intent.domain not in agent_registry: raise UnsupportedDomainError(f"No agents registered for domain: {parsed_intent.domain}") available_agents = agent_registry[parsed_intent.domain] subtasks = [] for requirement in parsed_intent.requirements: matching_agents = [ agent for agent in available_agents if requirement.matches_agent_capabilities(agent) ] if not matching_agents: subtasks.append(SubagentTask( id=generate_task_id(), requirement=requirement, fallback_mode="human_review", confidence=0.0 )) else: best_agent = max(matching_agents, key=lambda a: a.success_rate) subtasks.append(SubagentTask( id=generate_task_id(), requirement=requirement, target_agent=best_agent, confidence=best_agent.success_rate, parallelizable=requirement.is_parallelizable )) return _enforce_parallel_limits(subtasks, max_parallel) ``` ### Pattern 2: Execution with Fallback ```python def execute_subagent_chain( subtasks: List[SubagentTask], execution_context: Dict, fallback_agents: Dict[str, AgentCapability] ) -> ExecutionReport: """Execute routed subagent tasks with domain-specific fallback handling. Implements Law 4 (Fail Fast/Loud) by immediately surfacing capability mismatches and enforcing audit trails. """ results = [] failed_tasks = [] for task in subtasks: try: if task.parallelizable: result = await run_async_subagent(task, execution_context) else: result = run_sync_subagent(task, execution_context) results.append(TaskResult( task_id=task.id, status="completed", output=result.payload, latency_ms=result.duration, confidence=task.confidence )) except CapabilityMismatchError as e: # Law 4: Fail immediately on invalid agent capability failed_tasks.append(task) except TransientTimeoutError: # Fallback: Retry with adjusted timeout or alternative agent retry_result = _retry_with_backoff(task, execution_context) if retry_result: results.append(retry_result) else: failed_tasks.append(task) # Apply fallback chain for failed tasks for failed in failed_tasks: fallback_result = _route_to_fallback_agent(failed, fallback_agents) if fallback_result: results.append(fallback_result) else: results.append(TaskResult( task_id=failed.id, status="deferred_to_human", output=None, confidence=0.0 )) return ExecutionReport( total_tasks=len(subtasks), completed=len([r for r in results if r.status == "completed"]), deferred=len([r for r in results if r.status == "deferred_to_human"]), audit_log=_generate_audit_trail(results) ) ``` ### 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 | |---|---| | `parallel-skill-runner` | Executes subagent tasks in parallel — complements the delegation patterns covered here | | `task-decomposition-engine` | Decomposes tasks into subagent assignments — the upstream process for subagent-driven workflows | --- ## 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 domain. The model follows markdown links at load time to resolve external references and inline content. - [Microsoft AutoGen Documentation](https://microsoft.github.io/autogen/stable/) — Microsoft's framework for building multi-agent conversation systems - [CrewAI Multi-Agent Framework](https://docs.crewai.com/) — Official CrewAI documentation for orchestrating role-based AI agent teams - [LLM Agent Orchestration Patterns (LangGraph)](https://langchain-ai.github.io/langgraph/concepts/high_level/) — LangGraph patterns for coordinating multiple LLM agents - [Multi-Agent System Design Patterns (Stanford CS224)](https://github.com/stanfordnlp/dspy) — Stanford's research on multi-agent system architectures and coordination patterns - [Delegation Patterns in AI Agents (OpenAI Cookbook)](https://cookbook.openai.com/) — OpenAI's cookbook examples for agent delegation and tool-use orchestration