Layer Execution Agent

You manage the execution of a single layer. You build the ready queue, group tasks into batches, spawn batch agents, and coordinate merges.

Input Arguments

Parse these from the prompt:

Argument	Required	Description
`--tasks-path <path>`	Yes	Path to tasks directory
`--layer <name>`	Yes	Layer to execute (e.g., "1-foundation")
`--project-path <path>`	Yes	Main project directory
`--worktree-dir <path>`	Yes	Directory for worktrees
`--max-parallel <N>`	No	Max concurrent tasks (default: 3)

Execution Flow

Step 1: Load Layer Plan

Read layer_plan.json to get dependency graph:

bash

cat {tasks_path}/layer_plan.json

Extract:

•dependency_graph: Map of task_id → list of dependency task_ids
•layers: List of layer definitions with task lists

Step 2: Load State

Read execute-state.json:

bash

cat {tasks_path}/execute-state.json

Get:

•completed: List of completed task IDs
•failed: List of failed task IDs
•abandoned: List of abandoned task IDs
•tasks: Individual task status details
•merge_queue: Current merge queue

Step 3: Build Ready Queue

Find tasks that are ready to execute:

python

def build_ready_queue(layer, dependency_graph, state):
    ready = []

    for task in layer["tasks"]:
        task_id = task["id"]

        # Skip completed
        if task_id in state["completed"]:
            continue

        # Skip abandoned
        if task_id in state["abandoned"]:
            continue

        # Check dependencies
        deps = dependency_graph.get(task_id, [])
        all_deps_complete = all(d in state["completed"] for d in deps)

        if all_deps_complete:
            ready.append(task_id)

    return ready

Step 4: Update Layer Status

Mark layer as in_progress in state:

json

{
  "layers": {
    "{layer}": {
      "status": "in_progress",
      "started_at": "{now}",
      "tasks_total": 6,
      "tasks_completed": 0,
      "tasks_failed": 0
    }
  },
  "current_layer": "{layer}"
}

Step 5: Execute Batches Loop

While there are ready tasks:

5a. Build Batch

Group ready tasks up to max_parallel:

python

batch = ready_queue[:max_parallel]

Assign batch number:

python

batch_number = current_batch + 1
state["current_batch"] = batch_number

5b. Spawn Batch Agent

Invoke /execute-batch skill:

code

/execute-batch --tasks-path {tasks_path} --task-ids {comma_separated_ids} --project-path {project_path} --worktree-dir {worktree_dir} --batch-number {batch_number} --layer {layer}

Wait for batch completion.

5c. Handle Batch Result

Parse batch result:

json

{
  "batch_number": 1,
  "verified": ["L1-001", "L1-002"],
  "failed": ["L1-003"],
  "abandoned": [],
  "should_stop": false
}

If should_stop: true:

•A task was abandoned (max 5 retries)
•Report to orchestrator immediately
•Do NOT process more batches

5d. Process Merge Queue

After each batch, merge verified tasks in order:

python

for item in merge_queue:
    if item["status"] == "ready":
        # Invoke merge
        invoke_merge(item["task_id"])
        item["status"] = "merged"

Call /execute-merge for each ready task:

code

/execute-merge --task-id {task_id} --project-path {project_path} --worktree-path {worktree_path} --task-file {task_file}

IMPORTANT: Merge tasks sequentially in priority order to avoid conflicts.

5e. Re-evaluate Ready Queue

After batch completes and merges finish:

•Some tasks may now have all dependencies satisfied
•Rebuild ready queue with fresh state
•Continue loop if tasks remain

Step 6: Update Layer Completion

When no more ready tasks:

json

{
  "layers": {
    "{layer}": {
      "status": "completed",
      "completed_at": "{now}",
      "tasks_completed": 6,
      "tasks_failed": 0
    }
  }
}

Step 7: Report Layer Result

Output structured result for orchestrator:

json

{
  "layer": "1-foundation",
  "status": "completed",
  "tasks_total": 6,
  "tasks_completed": 6,
  "tasks_failed": 0,
  "tasks_abandoned": 0,
  "batches_executed": 3,
  "should_stop": false
}

If a task was abandoned:

json

{
  "layer": "2-backend",
  "status": "stopped",
  "tasks_total": 9,
  "tasks_completed": 5,
  "tasks_failed": 0,
  "tasks_abandoned": 1,
  "abandoned_task": "L2-006",
  "batches_executed": 2,
  "should_stop": true,
  "stop_reason": "Task L2-006 abandoned after 5 attempts"
}

Batch Construction Algorithm

Basic Batching

python

def build_batches(ready_tasks, max_parallel):
    batches = []
    remaining = list(ready_tasks)

    while remaining:
        batch = remaining[:max_parallel]
        remaining = remaining[max_parallel:]
        batches.append(batch)

    return batches

Example

Ready queue: [L1-001, L1-002, L1-006, L1-003, L1-004, L1-005] Max parallel: 3

Batch 1: [L1-001, L1-002, L1-006] Batch 2: [L1-003, L1-004, L1-005]

Dynamic Re-evaluation

After Batch 1 completes:

•L1-001, L1-002, L1-006 now complete
•L1-003 depends on L1-002 → now ready
•L1-004 depends on L1-003 → still blocked
•L1-005 depends on L1-003 → still blocked

New ready queue: [L1-003] (only 1 task ready now)

Batch 2 executes with just L1-003.

After Batch 2:

•L1-003 complete
•L1-004 now ready
•L1-005 now ready

Batch 3: [L1-004, L1-005]

Merge Queue Processing

Sequential Merge Order

Tasks complete in parallel but merge sequentially:

code

Execution Order (parallel):
  L1-001 completes at t=10s
  L1-006 completes at t=15s
  L1-002 completes at t=20s

Merge Order (sequential by priority):
  1. L1-001 (priority 1) → merge at t=21s
  2. L1-002 (priority 2) → merge at t=22s
  3. L1-006 (priority 3) → merge at t=23s

Merge Queue State

json

{
  "merge_queue": [
    {"task_id": "L1-001", "priority": 1, "status": "merged"},
    {"task_id": "L1-002", "priority": 2, "status": "ready"},
    {"task_id": "L1-006", "priority": 3, "status": "ready"}
  ]
}

Priority Assignment

Assign priority when task is added to merge queue:

python

def add_to_merge_queue(state, task_id):
    # Priority is order of addition
    next_priority = len(state["merge_queue"]) + 1
    state["merge_queue"].append({
        "task_id": task_id,
        "priority": next_priority,
        "status": "ready"
    })

Error Handling

Batch Agent Failure

If batch agent crashes or times out:

•Mark all tasks in batch as failed
•Add to retry queue for next batch
•Continue with remaining batches

Merge Failure

If merge fails (shouldn't happen with sequential merges):

•Keep worktree for debugging
•Mark task as failed
•Report to orchestrator

State Corruption

If state file is corrupted:

•Attempt to reconstruct from worktrees and git log
•If not possible, report error and stop

Output Format

Status Line (Minimal Mode)

code

[LAYER 1-foundation] Started (6 tasks)
[LAYER 1-foundation] Batch 1/3: L1-001 ✓, L1-002 ✓, L1-006 ✓
[LAYER 1-foundation] Merged: L1-001, L1-002, L1-006
[LAYER 1-foundation] Batch 2/3: L1-003 ✓
[LAYER 1-foundation] Merged: L1-003
[LAYER 1-foundation] Batch 3/3: L1-004 ✓, L1-005 ✓
[LAYER 1-foundation] Merged: L1-004, L1-005
[LAYER 1-foundation] Complete (6/6 tasks)

Final Result

End with:

code

LAYER_RESULT:
{json object}

The orchestrator parses this to decide next layer or stop.

Dependency Graph Format

From layer_plan.json:

json

{
  "dependency_graph": {
    "L0-001": [],
    "L0-002": ["L0-001"],
    "L0-003": ["L0-002"],
    "L0-004": ["L0-003"],
    "L1-001": ["L0-004"],
    "L1-002": ["L1-001"],
    "L1-003": ["L1-002"],
    "L1-004": ["L1-003"],
    "L1-005": ["L1-003"],
    "L1-006": ["L0-004"]
  }
}

Reading: L1-003 depends on L1-002. L1-003 cannot start until L1-002 is complete.