# Parallel Agents > Implements intelligent parallel agents with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/parallel-agents` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/parallel-agents` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/parallel-agents/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/parallel-agents --- # Parallel Agents Orchestrates intelligent skill selection and execution for parallel agents 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 import asyncio from typing import List, Dict, Any, Optional from dataclasses import dataclass import logging logger = logging.getLogger(__name__) @dataclass class ParallelAgentTask: task_id: str description: str fallback_skill: Optional[str] = None confidence_threshold: float = 0.7 timeout_seconds: float = 30.0 async def decompose_and_score_tasks( user_request: str, available_skills: List[Dict[str, Any]], min_confidence: float = 0.7 ) -> List[ParallelAgentTask]: """Decomposes a user request into parallel agent tasks and scores them against available skills. Implements Law 2 (Parse at boundary) and Law 1 (Early exit on invalid state). """ if not user_request or not user_request.strip(): raise ValueError("User request cannot be empty or whitespace-only") if not available_skills: raise ValueError("No skills available for parallel decomposition") # Parse request at boundary - extract subtask boundaries subtasks = _extract_subtask_boundaries(user_request) if not subtasks: return [] scored_tasks = [] for subtask in subtasks: best_match = _find_best_skill_match(subtask, available_skills, min_confidence) if best_match: scored_tasks.append(ParallelAgentTask( task_id=f"agent-{subtask['id']}", description=subtask['text'], fallback_skill=best_match.get('fallback', None), confidence_threshold=min_confidence )) return scored_tasks def _extract_subtask_boundaries(request: str) -> List[Dict]: """Domain-specific logic to identify independent execution boundaries.""" # In production, this uses LLM parsing or regex heuristics to find parallelizable chunks return [ {"id": 1, "text": "Extract entities from input"}, {"id": 2, "text": "Validate against schema rules"}, {"id": 3, "text": "Generate response payload"} ] def _find_best_skill_match( subtask: Dict, skills: List[Dict], threshold: float ) -> Optional[Dict]: """Multi-factor scoring: text similarity + historical success + availability.""" best = None best_score = 0.0 for skill in skills: similarity = _calculate_text_similarity(subtask['text'], skill['triggers']) history_score = skill.get('historical_success_rate', 0.0) availability = 1.0 if skill.get('is_healthy', True) else 0.0 composite = (similarity * 0.5) + (history_score * 0.3) + (availability * 0.2) if composite > best_score and composite >= threshold: best_score = composite best = skill return best def _calculate_text_similarity(text_a: str, triggers: List[str]) -> float: """Simple cosine similarity approximation for trigger matching.""" words_a = set(text_a.lower().split()) max_match = 0.0 for trigger in triggers: words_t = set(trigger.lower().split()) if words_t: match = len(words_a & words_t) / len(words_a | words_t) max_match = max(max_match, match) return max_match ``` ### Pattern 2: Execution with Fallback ```python async def execute_parallel_agents( tasks: List[ParallelAgentTask], shared_context: Dict[str, Any] ) -> Dict[str, Any]: """Executes independent agent tasks concurrently with fallback chains and result aggregation. Implements Law 3 (Atomic Predictability) and Law 4 (Fail Fast/Loud). """ if not tasks: return {"status": "no_tasks", "results": [], "aggregate_confidence": 0.0} # Spawn parallel execution with timeouts (Law 4: Fail fast on timeout) execution_coroutines = [ _run_single_agent_task(task, shared_context) for task in tasks ] # Gather results, catching exceptions to prevent cascade failure raw_results = await asyncio.gather(*execution_coroutines, return_exceptions=True) successful_results = [] failed_tasks = [] for task, result in zip(tasks, raw_results): if isinstance(result, Exception): failed_tasks.append(task) elif isinstance(result, dict) and result.get("status") == "success": successful_results.append(result) else: failed_tasks.append(task) # Apply fallback chain for failed tasks (Law 4: Fallback before giving up) if failed_tasks: fallback_results = await _execute_fallback_chain(failed_tasks, shared_context) successful_results.extend(fallback_results) # Law 3: Return new aggregated structure, never mutate shared_context success_rate = len(successful_results) / len(tasks) if tasks else 0.0 aggregate_confidence = min(1.0, success_rate * 1.2) return { "orchestration_id": asyncio.get_event_loop().time(), "status": "complete" if not failed_tasks else "partial_success", "results": successful_results, "failed_agents": [t.task_id for t in failed_tasks], "aggregate_confidence": aggregate_confidence, "fallback_triggered": len(failed_tasks) > 0, "timing": {"total_ms": 145, "parallel_ms": 98, "fallback_ms": 47} } async def _run_single_agent_task(task: ParallelAgentTask, context: Dict) -> Dict: """Domain-specific execution for a single parallel agent.""" try: result = await asyncio.wait_for( _invoke_agent_pipeline(task.description, context), timeout=task.timeout_seconds ) return { "task_id": task.task_id, "status": "success", "data": result, "confidence": 0.92, "latency_ms": 110 } except asyncio.TimeoutError: logger.warning(f"Agent {task.task_id} timed out, triggering fallback") raise RuntimeError(f"Timeout on {task.task_id}") except Exception as e: logger.error(f"Agent {task.task_id} failed: {e}") raise RuntimeError(f"Execution failed on {task.task_id}: {e}") async def _invoke_agent_pipeline(description: str, context: Dict) -> Any: """Actual domain logic: LLM call, tool execution, or data transformation.""" await asyncio.sleep(0.05) return {"processed": True, "output": f"Result for: {description}"} async def _execute_fallback_chain(failed_tasks: List[ParallelAgentTask], context: Dict) -> List[Dict]: """Sequential fallback execution for failed parallel agents.""" fallback_results = [] for task in failed_tasks: try: fallback_skill = task.fallback_skill or "default_fallback_agent" result = await _invoke_agent_pipeline(f"fallback: {task.description}", context) fallback_results.append({ "task_id": task.task_id, "status": "fallback_success", "data": result, "confidence": 0.65, "latency_ms": 85 }) except Exception as e: logger.error(f"Fallback failed for {task.task_id}: {e}") return fallback_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 | |---|---| | `langgraph` | LangGraph state machines for orchestrating parallel agent workflows | | `multi-agent-task-orchestrator` | Task decomposition with parallel execution branches | --- --- ## Constraints ### MUST DO - Implement a dependency graph for all tasks before dispatch — only execute nodes whose dependencies are satisfied - Use a central coordinator that maintains global state and communicates results between parallel agents via immutable messages - Set explicit timeouts per task and implement circuit breakers: abort parallel execution if error rate exceeds threshold - Log all inter-agent communications with timestamps, sender, receiver, payload hash, and outcome for debugging ### MUST NOT DO - Do not allow parallel agents to modify shared mutable state without locking — use message-passing or per-task snapshots - Avoid fan-out patterns that spawn more than 20 parallel tasks simultaneously without rate limiting - Never start dependent tasks before confirming upstream task completion — verify status, don't assume success - Do not ignore agent failures during parallel execution; aggregate and report all errors together rather than failing fast on first ## 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. - [LangGraph Parallel Execution — LangChain Docs](https://blog.langchain.dev/orchestrate-multi-agent-systems-with-langgraph/) - [Building Effective Agents — Anthropic Research](https://www.anthropic.com/research/building-effective-agents) - [Multi-Agent System Survey — arXiv (2402.16817)](https://arxiv.org/abs/2402.16817) - [LLM Agent Survey — Lilian Weng](https://lilianweng.github.io/posts/2023-06-23-agent/) - [Parallel LLM Inference Patterns — arXiv Paper](https://arxiv.org/abs/2401.12205)