# Batching Algorithm Reference

## Topological Sort with Batch Grouping

The `/implement` skill uses a topological sort to determine execution order,
then groups tasks into batches for code review and commit. Execution within a
batch is **sequential** — no subagents, no parallelism.

## Algorithm

1. Build adjacency list from `_dependencies_table.md`
2. Compute in-degree for each task node
3. Initialize the ready set with all nodes that have in-degree 0
4. For each batch:
   a. Select up to 4 tasks from the ready set (default batch size cap)
   b. Implement the selected tasks one at a time in topological order
   c. When all tasks in the batch complete, remove them from the graph and
      decrement in-degrees of dependents
   d. Add newly zero-in-degree nodes to the ready set
5. Repeat until the graph is empty

## Ordering Inside a Batch

Tasks inside a batch are executed in topological order — a task is only
started after every task it depends on (inside the batch or in a previous
batch) is done. When two tasks have the same topological rank, prefer the
lower-numbered (more foundational) task first.

## Complexity Budget

Each batch should not exceed 20 total complexity points.
If it does, split the batch and let the user choose which tasks to include.
The budget exists to keep the per-batch code review scope reviewable.