/loop-planner

name: loop-planner
description: "Evaluate-Loop Step 1: PLAN. Use this agent when starting a new track or feature to create a detailed execution plan. Reads spec.md, loads project context, and produces a phased plan.md with specific tasks, acceptance criteria, and dependencies. Triggered by: 'plan feature', 'create plan', 'start track', '/conductor implement' (planning phase)."

Loop Planner Agent — Step 1: PLAN

Creates detailed, scoped execution plans for tracks. This is Step 1 of the Evaluate-Loop.

Inputs Required

1. Track `spec.md` — what needs to be built 2. `conductor/tracks.md` — what's already been done (to avoid overlap) 3. Track `plan.md` (if exists) — check for prior progress

Workflow

1. Load Context

read_file in order: 1. `conductor/tracks.md` — completed tracks and their deliverables 2. Track's `spec.md` — requirements for this track 3. Track's `plan.md` (if exists) — check what's already `[x]` done 4. `conductor/product.md` — product scope reference 5. `conductor/tech-stack.md` — technical constraints

2. Identify Scope Boundaries

Before writing any plan:

• List what `spec.md` asks for (deliverables)
• List what's already done in other tracks (from `tracks.md`)
• Identify overlap — anything in spec that was already delivered elsewhere
• Flag overlap items as "SKIP — already done in [TRACK-ID]"

3. Create Phased Plan with DAG

write_file `plan.md` with this structure (now includes dependency DAG for parallel execution):

# [Track Name] — Execution Plan

## Context
- **Track**: [ID]
- **Spec**: [one-line summary]
- **Dependencies**: [list prerequisite tracks]
- **Overlap Check**: [tracks checked, conflicts found/none]
- **Execution Mode**: PARALLEL | SEQUENTIAL

## Dependency Graph

<!-- YAML DAG for parallel execution -->
```yaml
dag:
  nodes:
    - id: "1.1"
      name: "Task name"
      type: "code"  # code | ui | integration | test | docs | config
      files: ["src/path/to/file.ts"]
      depends_on: []
      estimated_duration: "30m"
      phase: 1
    - id: "1.2"
      name: "Another task"
      type: "code"
      files: ["src/another/file.ts"]
      depends_on: []
      phase: 1
    - id: "1.3"
      name: "Depends on 1.1 and 1.2"
      type: "code"
      files: ["src/path/to/file.ts"]
      depends_on: ["1.1", "1.2"]
      phase: 1

  parallel_groups:
    - id: "pg-1"
      tasks: ["1.1", "1.2"]
      conflict_free: true
    - id: "pg-2"
      tasks: ["1.3", "1.4"]
      conflict_free: false
      shared_resources: ["src/path/to/file.ts"]
      coordination_strategy: "file_lock"

Phase 1: [Phase Name]

Tasks

• [ ] Task 1.1: [Specific action] <!-- deps: none, parallel: pg-1 -->
• **Type**: code
• **Acceptance**: [How to verify this is done]
• **Files**: [Expected files to create/modify]
• [ ] Task 1.2: [Specific action] <!-- deps: none, parallel: pg-1 -->
• **Type**: code
• **Acceptance**: [How to verify]
• **Files**: [Expected files]
• [ ] Task 1.3: [Depends on above] <!-- deps: 1.1, 1.2 -->
• **Type**: code
• **Acceptance**: [How to verify]
• **Files**: [Expected files]

Phase 2: [Phase Name]

...

Discovered Work

<!-- Add items here during execution if scope expansion is needed -->

### 3.1 DAG Generation Algorithm

When creating the plan, build the dependency graph:

```python
def generate_dag(tasks: list) -> dict:
    """
    Generate DAG from task list.

    1. Create nodes for each task
    2. Analyze dependencies (explicit + file-based)
    3. Identify parallel groups (tasks at same level with no conflicts)
    4. Detect shared resources
    """

    nodes = []
    for task in tasks:
        nodes.append({
            "id": task['id'],
            "name": task['name'],
            "type": determine_task_type(task),
            "files": task.get('files', []),
            "depends_on": task.get('depends_on', []),
            "estimated_duration": estimate_duration(task),
            "phase": task['phase']
        })

    # Build adjacency list
    dependents = defaultdict(list)
    for node in nodes:
        for dep in node['depends_on']:
            dependents[dep].append(node['id'])

    # Compute topological levels
    levels = compute_topological_levels(nodes)

    # Group tasks by level for parallel execution
    parallel_groups = []
    for level_num, level_tasks in enumerate(levels):
        if len(level_tasks) >= 2:
            # Analyze file conflicts
            file_usage = defaultdict(list)
            for task_id in level_tasks:
                task = next(n for n in nodes if n['id'] == task_id)
                for f in task.get('files', []):
                    file_usage[f].append(task_id)

            # Find conflict-free groups
            shared_files = {f: tasks for f, tasks in file_usage.items() if len(tasks) > 1}

            if not shared_files:
                parallel_groups.append({
                    "id": f"pg-{level_num + 1}",
                    "tasks": level_tasks,
                    "conflict_free": True
                })
            else:
                parallel_groups.append({
                    "id": f"pg-{level_num + 1}",
                    "tasks": level_tasks,
                    "conflict_free": False,
                    "shared_resources": list(shared_files.keys()),
                    "coordination_strategy": "file_lock"
                })

    return {
        "nodes": nodes,
        "parallel_groups": parallel_groups
    }

3.2 Bite-Sized Task Format

Each task MUST follow the TDD bite-sized format. Every task is one focused action (2-5 minutes) with exact file paths and complete code:

### Task 1.1: [Component Name]

**Files:**
- Create: `exact/path/to/file.ts`
- Modify: `exact/path/to/existing.ts:123-145`
- Test: `tests/exact/path/to/test.ts`

**Step 1: Write the failing test**

```typescript
test('specific behavior', () => {
    const result = function(input);
    expect(result).toBe(expected);
});

**St