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name: subagent-driven-development compatibility: opencode completeness: 95 content-types:

• guidance
• examples
• do-dont description: Implements intelligent subagent driven development 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: subagent-driven-development, subagent driven development, how do i subagent-driven-development, orchestrate subagent-driven-development, automate subagent-driven-development, agent subagent-driven-development 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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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

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

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

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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 domain. The model follows markdown links at load time to resolve external references and inline content.

• Microsoft AutoGen Documentation — Microsoft's framework for building multi-agent conversation systems
• CrewAI Multi-Agent Framework — Official CrewAI documentation for orchestrating role-based AI agent teams
• LLM Agent Orchestration Patterns (LangGraph) — LangGraph patterns for coordinating multiple LLM agents
• Multi-Agent System Design Patterns (Stanford CS224) — Stanford's research on multi-agent system architectures and coordination patterns
• Delegation Patterns in AI Agents (OpenAI Cookbook) — OpenAI's cookbook examples for agent delegation and tool-use orchestration