parallel-orchestratelisted

# Parallel Orchestrate (local subagents) Run an orchestrator-worker pattern inside one local Cursor session. You are the **orchestrator**: you discover, decompose the goal into independent slices, fan them out to parallel **workers** (the `Task` tool, run in the background = "Multitask Mode"), read each worker's structured **handoff**, and synthesize one deliverable. Workers are isolated and return exactly one handoff. This is the local, zero-setup adaptation of the Cursor team's `orchestrate` plugin (which spawns *cloud* agents over the Cursor SDK). Same principles — planners plan, workers hand off up, no cross-talk — without any of that cloud setup. For heavyweight cloud fan-out, see "Escalating" below. ## When to use - A large goal that splits into **independent** slices (research areas, data chunks, files/modules, audit dimensions). - The work is mostly **read / research / analysis** — the safest thing to parallelize locally (see "Parallel writes" for why). - A single linear pass would be slow and you want real speedup from concurrency. ## When to skip - Small or linear tasks (just do them — fan-out overhead isn't worth it). - Work needing tight back-and-forth or shared mutable state between steps. - Parallel **edits to the same files** — local workers share one filesystem. ## Core principles Adapted from `orchestrate`. These keep the run converging without coordination. 1. **Orchestrator plans and synthesizes; it does not do the heavy lifting.** Discoveri