What problem does it solve? Coordinating large backlogs of pending development tasks often leads to merge conflicts, lost agent state on crashes, polluted git history, and wasted CI/CD credits from rapid polling. This Skill orchestrates a disciplined multi-agent execution loop that processes every pending task in .ai-memory/plans/pending/ without triggering releases or running tests locally. ## Core Features & Use Cases - Batched 3-Agent Execution: Spawns up to 3 concurrent sub-agents, each assigned small micro-task chunks with disjoint files tracked in a locking matrix to prevent merge collisions. - Crash Recovery & State Tracking: Logs agent state to .ai-memory/temp/ and .ai-memory/temp-agents/, applies a 3-strike rollback policy with git checkout, and records failures for diagnosis. - Strict Git Hygiene: Enforces single atomic commits (never per-file commits), artifact sanitization before staging, immediate push, and a total ban on local test/build execution deferred to CI/CD monitored via GitMap Pipeline-AI with ETA-based waiting. - Use Case: A repository accumulates 20 pending task files in .ai-memory/plans/pending/. Invoke this Skill to self-loop through all of them—grouping into execution waves, committing consolidated work, consolidating completed subtasks, and reporting done/pending statistics every loop—until the queue is empty. ## Quick Start Ask the AI to execute all pending tasks from the plans folder using the batched loop workflow without triggering a release, and say "continue" to resume the loop. ## Quick Start Run the batched loop to complete every pending task in .ai-memory/plans/pending/ without releases, then say go or continue to keep looping.