Task Decomposition Engine

Task Decomposition Engine

Orchestrates intelligent skill selection and execution for task decomposition engine 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_task(
    raw_task: str,
    available_ops: List[Dict],
    max_depth: int = 3
) -> Dict:
    """Decompose a complex task into an executable DAG of subtasks.
    
    Applies Law 2 (Parse at boundary) by strictly validating input structure.
    Uses Law 3 (Atomic Predictability) to return a fresh DAG structure.
    """
    if not raw_task or not isinstance(raw_task, str):
        raise ValueError("Raw task must be a non-empty string")
        
    # Parse and extract atomic operations from the task description
    parsed_ops = _extract_atomic_operations(raw_task, available_ops)
    
    if not parsed_ops:
        raise ValueError("No valid atomic operations found for task decomposition")
        
    # Build dependency graph using topological sort logic
    dag = _construct_dependency_graph(parsed_ops, max_depth)
    
    # Validate DAG for cycles and missing dependencies (Law 4: Fail Fast)
    cycle_check = _detect_cycles(dag)
    if cycle_check:
        raise ValueError(f"Decomposition contains circular dependencies: {cycle_check}")
        
    # Score decomposition strategies based on parallelism potential and risk
    strategies = _score_decomposition_strategies(dag, available_ops)
    
    # Return immutable snapshot of the best decomposition plan
    return {
        "task_id": generate_task_id(),
        "decomposition_graph": dag,
        "optimal_strategy": strategies[0],
        "estimated_parallelism": _calculate_parallelism(dag),
        "metadata": {"depth": len(dag.get("levels", [])), "nodes": len(dag.get("nodes", []))}
    }

Pattern 2: Execution with Fallback

def execute_decomposition_chain(
    decomposition_plan: Dict,
    execution_context: Dict,
    fallback_policy: str = "retry_then_merge"
) -> Dict:
    """Execute a decomposed task DAG with domain-specific fallback handling.
    
    Implements Law 1 (Early Exit) for invalid plan states.
    Implements Law 4 (Fail Loud) by halting on critical dependency failures.
    """
    plan = decomposition_plan.get("decomposition_graph", {})
    if not plan.get("nodes") or not plan.get("edges"):
        raise ValueError("Invalid decomposition plan: missing nodes or edges")
        
    results = {}
    execution_order = _topological_sort(plan["edges"])
    
    for node_id in execution_order:
        node = plan["nodes"][node_id]
        try:
            # Execute subtask with context isolation (Law 3)
            subtask_result = _run_subtask(node, execution_context)
            results[node_id] = {"status": "success", "data": subtask_result}
            
            # Update context for dependent nodes
            execution_context = _merge_context(execution_context, subtask_result)
            
        except DependencyError as e:
            # Law 4: Critical dependency failure halts the branch
            if fallback_policy == "halt_on_critical":
                raise SkillExecutionError(f"Critical dependency failed for {node_id}: {e}") from e
            results[node_id] = {"status": "failed", "error": str(e)}
            
        except TransientError as e:
            # Domain-specific fallback: retry with backoff or use cached fallback
            if fallback_policy == "retry_then_merge":
                retry_result = _execute_with_exponential_backoff(node, execution_context, max_retries=2)
                results[node_id] = {"status": "recovered", "data": retry_result}
            else:
                results[node_id] = {"status": "failed", "error": str(e)}
                
    # Validate final state before returning (Law 2)
    if not _validate_execution_state(results, plan["edges"]):
        raise SkillExecutionError("Execution state validation failed: inconsistent results")
        
    return {
        "task_id": decomposition_plan.get("task_id"),
        "final_state": results,
        "execution_trace": _build_trace(results),
        "confidence_score": _calculate_final_confidence(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

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.

• Task Decomposition in LLM Agents (Gao et al., 2023) — Academic research on task decomposition strategies for LLM-based agents
• Plan-and-Solve Prompting (Wang et al.) — Research on decomposing problems into plans before execution for improved reasoning
• ReAct: Synergizing Reasoning and Acting (Yao et al., 2022) — Foundational paper including task decomposition as part of the ReAct loop
• Hierarchical Task Networks for AI Planning — Wikipedia article on HTN planning, a foundational approach to task decomposition
• Subgoal-Based Planning in Reinforcement Learning (Schaul et al.) — Research on using subgoals for efficient task decomposition in learning systems