What problem does it solve? Executing a multi-task implementation plan in a single AI session pollutes context, skips quality gates, and loses progress after compaction. This Skill orchestrates plan execution by delegating each task to an isolated subagent with curated context, enforcing review checkpoints, and tracking progress in a durable ledger. ## Core Features & Use Cases - Fresh subagent per task: Dispatch implementer subagents with task briefs extracted to files, so each task runs in clean context without inheriting session history. - Two-stage review gates: Run a task-scoped reviewer (spec compliance plus code quality) after every task and a broad whole-branch review at the end, with fix-and-re-review loops for Critical and Important findings. - Durable progress ledger: Record completed tasks and commit ranges in a git-ignored ledger file so work resumes correctly after context compaction. - Use Case: You have a written implementation plan with eight mostly independent tasks. This Skill extracts each task brief, dispatches implementer and reviewer subagents with model selection per task complexity, and finishes with a whole-branch review before merge. ## Quick Start Use subagent-driven development to execute the implementation plan in docs/plans/feature-plan.md task by task with reviews after each task.