# Task Decomposition Engine > Implements intelligent task decomposition engine with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/task-decomposition-engine` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/task-decomposition-engine` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/task-decomposition-engine/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/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 ```python 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 ```python 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 | Skill | Purpose | |---|---| | `parallel-skill-runner` | Executes decomposed sub-tasks in parallel after the engine splits a complex task | | `subagent-driven-development` | Delegates decomposed sub-tasks to subagents for parallel execution | --- ## 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)](https://arxiv.org/abs/2305.08291) — Academic research on task decomposition strategies for LLM-based agents - [Plan-and-Solve Prompting (Wang et al.)](https://arxiv.org/abs/2205.04616) — Research on decomposing problems into plans before execution for improved reasoning - [ReAct: Synergizing Reasoning and Acting (Yao et al., 2022)](https://arxiv.org/abs/2210.03629) — Foundational paper including task decomposition as part of the ReAct loop - [Hierarchical Task Networks for AI Planning](https://en.wikipedia.org/wiki/Hierarchical_task_analysis) — Wikipedia article on HTN planning, a foundational approach to task decomposition - [Subgoal-Based Planning in Reinforcement Learning (Schaul et al.)](https://arxiv.org/abs/2006.15473) — Research on using subgoals for efficient task decomposition in learning systems