task-execution-engine

Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.

242 stars

Best use case

task-execution-engine is best used when you need a repeatable AI agent workflow instead of a one-off prompt. It is especially useful for teams working in multi. Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.

Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.

Users should expect a more consistent workflow output, faster repeated execution, and less time spent rewriting prompts from scratch.

Practical example

Example input

Use the "task-execution-engine" skill to help with this workflow task. Context: Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.

Example output

A structured workflow result with clearer steps, more consistent formatting, and an output that is easier to reuse in the next run.

When to use this skill

  • Use this skill when you want a reusable workflow rather than writing the same prompt again and again.

When not to use this skill

  • Do not use this when you only need a one-off answer and do not need a reusable workflow.
  • Do not use it if you cannot install or maintain the related files, repository context, or supporting tools.

Installation

Claude Code / Cursor / Codex

$curl -o ~/.claude/skills/task-execution-engine/SKILL.md --create-dirs "https://raw.githubusercontent.com/aiskillstore/marketplace/main/skills/notedit/task-execution-engine/SKILL.md"

Manual Installation

  1. Download SKILL.md from GitHub
  2. Place it in .claude/skills/task-execution-engine/SKILL.md inside your project
  3. Restart your AI agent — it will auto-discover the skill

How task-execution-engine Compares

Feature / Agenttask-execution-engineStandard Approach
Platform SupportNot specifiedLimited / Varies
Context Awareness High Baseline
Installation ComplexityUnknownN/A

Frequently Asked Questions

What does this skill do?

Execute implementation tasks from design documents using markdown checkboxes. Use when (1) implementing features from feature-design-assistant output, (2) resuming interrupted work, (3) batch executing tasks. Triggers on 'start implementation', 'run tasks', 'resume'.

Where can I find the source code?

You can find the source code on GitHub using the link provided at the top of the page.

SKILL.md Source

# Feature Pipeline

Execute implementation tasks directly from design documents. Tasks are managed as markdown checkboxes - no separate session files needed.

## Quick Reference

```bash
# Get next task
python3 scripts/task_manager.py next --file <design.md>

# Mark task completed
python3 scripts/task_manager.py done --file <design.md> --task "Task Title"

# Mark task failed
python3 scripts/task_manager.py fail --file <design.md> --task "Task Title" --reason "..."

# Show status
python3 scripts/task_manager.py status --file <design.md>
```

## Task Format

Tasks are written as markdown checkboxes in the design document:

```markdown
## Implementation Tasks

- [ ] **Create User model** `priority:1` `phase:model`
  - files: src/models/user.py, tests/models/test_user.py
  - [ ] User model has email and password_hash fields
  - [ ] Email validation implemented
  - [ ] Password hashing uses bcrypt

- [ ] **Implement JWT utils** `priority:2` `phase:model`
  - files: src/utils/jwt.py
  - [ ] generate_token() creates valid JWT
  - [ ] verify_token() validates JWT

- [ ] **Create auth API** `priority:3` `phase:api` `deps:Create User model,Implement JWT utils`
  - files: src/api/auth.py
  - [ ] POST /register endpoint
  - [ ] POST /login endpoint
```

See [references/task-format.md](references/task-format.md) for full format specification.

## Execution Loop

```
LOOP until no tasks remain:
  1. GET next task (task_manager.py next)
  2. READ task details (files, criteria)
  3. IMPLEMENT the task
  4. VERIFY acceptance criteria
  5. UPDATE status (task_manager.py done/fail)
  6. CONTINUE
```

### Unattended Mode Rules

- **NO stopping** for questions
- **NO asking** for clarification
- Make autonomous decisions based on codebase patterns
- If blocked, mark as failed and continue

## Status Updates

Completed task:
```markdown
- [x] **Create User model** `priority:1` `phase:model` ✅
  - files: src/models/user.py
  - [x] User model has email field
  - [x] Password hashing implemented
```

Failed task:
```markdown
- [x] **Create User model** `priority:1` `phase:model` ❌
  - files: src/models/user.py
  - [ ] User model has email field
  - reason: Missing database configuration
```

## Resume / Recovery

To resume interrupted work, simply run again with the same design file:

```
/feature-pipeline docs/designs/xxx.md
```

The task manager will find the first uncompleted task and continue from there.

## Integration

This skill is typically triggered after `/feature-analyzer` completes:

```
User: /feature-analyzer implement user auth

Claude: [designs feature, generates task list]
        Design saved to docs/designs/2026-01-02-user-auth.md
        Ready to start implementation?

User: Yes / 开始实现

Claude: [executes tasks via task-execution-engine]
```

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