subagent-driven-development

# Subagent-Driven Development

## Overview

Execute implementation plans by dispatching fresh subagents per task with systematic two-stage review.

**Core principle:** Fresh subagent per task + two-stage review (spec then quality) = high quality, fast iteration.

## When to Use

Use this skill when:
- You have an implementation plan (from writing-plans skill or user requirements)
- Tasks are mostly independent
- Quality and spec compliance are important
- You want automated review between tasks

**vs. manual execution:**
- Fresh context per task (no confusion from accumulated state)
- Automated review process catches issues early
- Consistent quality checks across all tasks
- Subagents can ask questions before starting work

## The Process

### 1. Read and Parse Plan

Read the plan file. Extract ALL tasks with their full text and context upfront. Create a task list:

```
read("docs/plans/feature-plan.md")

# Track tasks via Hope Agent's task_create / task_update / task_list
task_create(content="Task 1: Create User model with email field")
task_create(content="Task 2: Add password hashing utility")
task_create(content="Task 3: Create login endpoint")
```

**Key:** Read the plan ONCE. Extract everything. Don't make subagents read the plan file — provide the full task text directly in context.

### 2. Per-Task Workflow

For EACH task in the plan:

#### Step 1: Dispatch Implementer Subagent

Use `subagent` with complete context:

```
subagent(
  action="spawn_and_wait",
  goal="Implement Task 1: Create User model with email and password_hash fields",
  context="""
    TASK FROM PLAN:
    - Create: src/models/user.py
    - Add User class with email (str) and password_hash (str) fields
    - Use bcrypt for password hashing
    - Include __repr__ for debugging

    FOLLOW TDD:
    1. Write failing test in tests/models/test_user.py
    2. Run: pytest tests/models/test_user.py -v (verify FAIL)
    3. Write minimal implementation
    4. Run: pytest tests/models/test_user.py -v (verify PASS)
    5. Run: pytest tests/ -q (verify no regressions)
    6. Commit: git add -A && git commit -m "feat: add User model with password hashing"

    PROJECT CONTEXT:
    - Python 3.11, Flask app in src/app.py
    - Existing models in src/models/
    - Tests use pytest, run from project root
    - bcrypt already in requirements.txt
  """
)
```

#### Step 2: Dispatch Spec Compliance Reviewer

After the implementer completes, verify against the original spec:

```
subagent(
  action="spawn_and_wait",
  goal="Review if implementation matches the spec from the plan",
  context="""
    ORIGINAL TASK SPEC:
    - Create src/models/user.py with User class
    - Fields: email (str), password_hash (str)
    - Use bcrypt for password hashing
    - Include __repr__

    CHECK:
    - [ ] All requirements from spec implemented?
    - [ ] File paths match spec?
    - [ ] Function signatures match spec?
    - [ ] Behavior matches expected?
    - [ ] Nothing extra added (no scope creep)?

    OUTPUT: PASS or list of specific spec gaps to fix.
  """
)
```

**If spec issues found:** Fix gaps, then re-run spec review. Continue only when spec-compliant.

#### Step 3: Dispatch Code Quality Reviewer

After spec compliance passes:

```
subagent(
  action="spawn_and_wait",
  goal="Review code quality for Task 1 implementation",
  context="""
    FILES TO REVIEW:
    - src/models/user.py
    - tests/models/test_user.py

    CHECK:
    - [ ] Follows project conventions and style?
    - [ ] Proper error handling?
    - [ ] Clear variable/function names?
    - [ ] Adequate test coverage?
    - [ ] No obvious bugs or missed edge cases?
    - [ ] No security issues?

    OUTPUT FORMAT:
    - Critical Issues: [must fix before proceeding]
    - Important Issues: [should fix]
    - Minor Issues: [optional]
    - Verdict: APPROVED or REQUEST_CHANGES
  """
)
```

**If quality issues found:** Fix issues, re-review. Continue only when approved.

#### Step 4: Mark Complete

```
task_update(id="task-1", status="completed")
```

### 3. Final Review

After ALL tasks are complete, dispatch a final integration reviewer:

```
subagent(
  action="spawn_and_wait",
  goal="Review the entire implementation for consistency and integration issues",
  context="""
    All tasks from the plan are complete. Review the full implementation:
    - Do all components work together?
    - Any inconsistencies between tasks?
    - All tests passing?
    - Ready for merge?
  """
)
```

### 4. Verify and Commit

```bash
# Run full test suite
pytest tests/ -q

# Review all changes
git diff --stat

# Final commit if needed
git add -A && git commit -m "feat: complete [feature name] implementation"
```

## Task Granularity

**Each task = 2-5 minutes of focused work.**

**Too big:**
- "Implement user authentication system"

**Right size:**
- "Create User model with email and password fields"
- "Add password hashing function"
- "Create login endpoint"
- "Add JWT token generation"
- "Create registration endpoint"

## Red Flags — Never Do These

- Start implementation without a plan
- Skip reviews (spec compliance OR code quality)
- Proceed with unfixed critical/important issues
- Dispatch multiple implementation subagents for tasks that touch the same files
- Make subagent read the plan file (provide full text in context instead)
- Skip scene-setting context (subagent needs to understand where the task fits)
- Ignore subagent questions (answer before letting them proceed)
- Accept "close enough" on spec compliance
- Skip review loops (reviewer found issues → implementer fixes → review again)
- Let implementer self-review replace actual review (both are needed)
- **Start code quality review before spec compliance is PASS** (wrong order)
- Move to next task while either review has open issues

## Handling Issues

### If Subagent Asks Questions

- Answer clearly and completely
- Provide additional context if needed
- Don't rush them into implementation

### If Reviewer Finds Issues

- Im