# Parallel Skill Runner > Implements intelligent parallel skill runner with multi-factor skill selection, fallback chains, and adherence to the 5 Laws of Elegant Defense - Skill: `paulpas/parallel-skill-runner` (Agent Skill) - Install (CLI): `npx skillmds@latest add paulpas/parallel-skill-runner` - Raw SKILL.md: https://api.skillmd.com/api/skills/paulpas/parallel-skill-runner/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-skill-runner --- # Parallel Skill Runner Orchestrates intelligent skill selection and execution for parallel skill runner 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 evaluate_parallel_candidates( task: str, candidate_skills: List[SkillMetadata], historical_registry: Dict[str, HistoricalRecord] ) -> List[RankedSkill]: """Score and rank skills for parallel dispatch based on multi-factor metrics. Applies Law 2 (Parse at boundary) by validating inputs upfront. Returns immutable ranked list for deterministic routing. """ if not task or not candidate_skills: raise ValueError("Task and candidate_skills must be non-empty") parsed_task = _parse_intent_and_entities(task) ranked = [] for skill in candidate_skills: if not _is_skill_available(skill): continue text_match = _cosine_similarity(parsed_task.terms, skill.trigger_terms) history = historical_registry.get(skill.id, HistoricalRecord()) success_rate = history.success_count / max(history.total_runs, 1) availability_score = _get_current_load_score(skill.endpoint) composite_score = (0.4 * text_match) + (0.4 * success_rate) + (0.2 * availability_score) ranked.append(RankedSkill( skill=skill, confidence=composite_score, expected_latency_ms=_estimate_latency(skill, parsed_task) )) ranked.sort(key=lambda x: x.confidence, reverse=True) return ranked ``` ### Pattern 2: Execution with Fallback ```python async def run_parallel_with_fallback( ranked_skills: List[RankedSkill], task_context: Dict, fallback_map: Dict[str, List[str]], timeout_seconds: float = 30.0 ) -> ParallelExecutionResult: """Execute top N skills in parallel with per-skill fallback chains. Implements Law 4 (Fail Fast/Loud) by isolating failures and applying fallbacks. Returns aggregated results without mutating original context. """ if not ranked_skills: raise ParallelExecutionError("No skills available for parallel execution") selected = ranked_skills[:3] # Execute top 3 in parallel tasks = [] for ranked in selected: tasks.append(_execute_single_skill_with_fallback( skill=ranked.skill, context=task_context, fallback_chain=fallback_map.get(ranked.skill.id, []), timeout=timeout_seconds )) results = await asyncio.gather(*tasks, return_exceptions=True) aggregated = ParallelExecutionResult( success_count=0, failed_count=0, results=[], fallbacks_triggered=[] ) for ranked, result in zip(selected, results): if isinstance(result, Exception): aggregated.failed_count += 1 aggregated.fallbacks_triggered.append(ranked.skill.id) else: aggregated.success_count += 1 aggregated.results.append(result) return aggregated ``` ### 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 | |---|---| | `agent-confidence-based-selector` | Provides the confidence-based selection layer that this parallel runner orchestrates across multiple candidates | | `agent-task-routing` | Handles sequential task routing — use this when tasks must be ordered rather than parallelized | --- ## 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. - [OpenAI Agent Design Patterns (Microsoft)](https://microsoft.github.io/autogen/stable/user-guide/agent-design/design-patterns.html) — Standard patterns for agent orchestration including parallel execution - [LangGraph Parallel Branches](https://langchain-ai.github.io/langgraph/concepts/high_level/#parallel-processing) — Documentation on implementing parallel processing workflows in LangGraph - [Multi-Agent Orchestration with CrewAI](https://docs.crewai.com/how-to/Parallel-Processing/) — Guide to parallel task execution across multiple AI agents - [CAMEL Communication Framework](https://www.camel-ai.org/) — Research on multi-agent communication and coordination patterns - [LLM Powered Autonomous Agents (Liao et al.)](https://arxiv.org/abs/2307.11765) — Foundational paper on LLM-based autonomous agent architectures