arc-writing-tasks

name	arc-writing-tasks
description	Use when breaking down features into executable tasks, when preparing for implementation, or when tasks need exact code and commands
argument-hint	<feature-name>

Writing Tasks

Overview

Break features into bite-sized tasks (2-5 minutes each) with exact code and commands. Each task is independently executable.

Core Rules

1. Bite-sized - Each task 2-5 minutes
2. Exact code - Complete code, not "add validation"
3. Exact commands - Full test command with expected output
4. TDD order - Test first, then implementation
5. Persist to file - Tasks saved to docs/tasks/-tasks.md

Task Format

tasks:
  - id: 1
    description: "Write failing test for login validation"
    file: "tests/auth/test_login.py"
    action: "create"
    code: |
      def test_login_valid_credentials():
          result = login("user", "valid123")
          assert result.success is True
    test_command: "pytest tests/auth/test_login.py -v"
    expected: "FAILED - login not defined"

  - id: 2
    description: "Implement login function"
    file: "src/auth/login.py"
    depends_on: [1]
    code: |
      def login(username: str, password: str) -> LoginResult:
          # exact implementation
    test_command: "pytest tests/auth/test_login.py -v"
    expected: "PASSED"

Granularity Check

Too Vague	Just Right
"Set up auth"	"Create User dataclass in src/types.py"
"Add tests"	"Write test_login_invalid_password in tests/auth/"
"Implement login"	"Add password hash check in login()"

Workflow

digraph writing_tasks {
    "Read feature spec" -> "Identify files needed";
    "Identify files needed" -> "Break into 2-5 min tasks";
    "Break into 2-5 min tasks" -> "Each has exact code?";
    "Each has exact code?" -> "Break into 2-5 min tasks" [label="no"];
    "Each has exact code?" -> "Add test commands" [label="yes"];
    "Add test commands" -> "Output task list";
}

Output Structure

Output to docs/tasks/<feature-name>-tasks.md

Format:

# <Feature Name> Tasks

> **Goal:** [One sentence describing what this delivers]
> **Architecture:** [2-3 sentences about approach]
> **Tech Stack:** [Key technologies/libraries]

> **For Claude:** Use arc-agent-driven or arc-executing-tasks to implement.

## Context
[Scene-setting from feature spec]

## Tasks

### Task 1: [Name]
**Files:**
- Create: `exact/path/to/file.py`
- Test: `tests/exact/path/to/test.py`

**Step 1: Write failing test**
\`\`\`python
def test_specific_behavior():
    ...
\`\`\`

**Step 2: Run test**
Run: `pytest tests/path/test.py::test_name -v`
Expected: FAIL

**Step 3: Implement**
\`\`\`python
def function():
    ...
\`\`\`

**Step 4: Verify**
Run: `pytest tests/path/test.py::test_name -v`
Expected: PASS

**Step 5: Commit**
`git commit -m "feat: add specific feature"`

### Task 2: ...

Completion Format

✅ Tasks written → docs/tasks/-tasks.md

• Total: N tasks
• Ready for: arc-agent-driven or arc-executing-tasks

Blocked Format

⚠️ Writing tasks blocked

• Issue: Feature spec unclear
• Missing: Authentication method not specified
• Action: Clarify with user before breaking down

Red Flags - STOP

• "Set up X" without exact files
• "Add tests" without test code
• "Implement Y" without exact implementation
• Tasks longer than 5 minutes

Vague = break down further.

After This Skill

Hand off to one of:

• arc-agent-driven — automated execution, fresh subagent per task. Best for walk-away batch runs where each task is well-scoped.
• arc-executing-tasks — human-in-the-loop mode with checkpoint prompts between tasks. Best when the task list carries judgment calls or when you want to review each step.

Both read docs/tasks/<name>-tasks.md as input. The task file itself says which one to use; default to arc-agent-driven.