What problem does it solve? Sequential LLM calls waste time when a task can be split into independent units, and single-pass scoring lacks the reliability of ensemble judgment. This Skill provides a structured pattern for splitting work, running branches concurrently, and merging outputs deterministically. ## Core Features & Use Cases - Sectioning: Split a task into independent slices (document chunks, test cases, tool queries) and process each in its own branch. - Voting and Ensemble Scoring: Run multiple judges on the same candidate answer in parallel and aggregate scores by voting or ranking. - Failure and Cost Guardrails: Checklists covering timeouts, retries, partial-failure policies, rate-limit fit, and cost caps per branch. - Use Case: You need to summarize a 50-page report. Split it into per-section branches, summarize each concurrently, then merge the section summaries into one final output with deterministic join logic. ## Quick Start Apply the parallelization pattern to split my task into independent subtasks, run them concurrently, and merge the results.