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A structured AI-assisted task workflow system with approval gates. Plan complex tasks with AI, then execute them step-by-step with verification and human approval.

Package Exports

  • taskgate
  • taskgate/lib/init.js

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Readme

TaskGate 🚀

A structured AI-assisted task workflow system with approval gates.

Plan complex tasks with AI, then execute them step-by-step with verification and human approval between each change.

Why TaskGate?

Working with AI coding assistants on complex tasks can be chaotic. TaskGate brings structure:

  • Two-phase workflow - Separate planning from execution
  • Atomic tasks - Small, focused changes (~1-3 minutes each)
  • Verification gates - Build/lint/test after every change
  • Human approval - You control when to proceed, pause, redo, or stop
  • Full audit trail - Complete documentation of what was done and why

Installation

# Install globally
npm install -g taskgate

# Or as a dev dependency
npm install --save-dev taskgate

Quick Setup

Run the init command in your project root:

npx taskgate init

This creates:

  • .github/skills/ - AI skill definitions
  • docs/templates/ - Task document templates
  • docs/tasks/ - Where your task documents live
  • docs/ATOMIC-TASK-WORKFLOW.md - Core workflow reference

Usage

1. Planning Phase

Start by telling your AI assistant to create a task:

create task: Refactor the authentication module to use JWT tokens

The AI will:

  1. Run an intake Q&A to understand requirements
  2. Analyze your codebase for context
  3. Create a detailed plan with atomic tasks
  4. Refine the plan based on your feedback
  5. Hand off with: "To execute, use: execute task: AUTH-JWT-REFACTOR"

2. Execution Phase

When ready to implement:

execute task: AUTH-JWT-REFACTOR

The AI will:

  1. Read the plan and find the next task
  2. Execute ONE atomic task
  3. Verify the change (build, lint, tests)
  4. Report what was done
  5. Wait for your approval

You respond with:

  • continue - Proceed to next task
  • wait - Pause while you review
  • redo - Try again with adjustments
  • stop - End execution

The Workflow

create task: [description]
         ↓
    [Planning]
         ↓
    Plan approved
         ↓
execute task: [TASK-NAME]
         ↓
    [Execute atomic task → Verify → Update docs → Get approval]
         ↓
    continue/wait/redo/stop
         ↓
    [Repeat until done]

Folder Structure After Init

your-project/
├── .github/
│   └── skills/
│       ├── new-task/
│       │   └── SKILL.md
│       └── execute-task/
│           └── SKILL.md
├── docs/
│   ├── templates/
│   │   ├── TASK-INTAKE-TEMPLATE.md
│   │   ├── TASK-PLAN-TEMPLATE.md
│   │   └── TASK-SESSION-TEMPLATE.md
│   ├── tasks/
│   │   └── .gitkeep
│   └── ATOMIC-TASK-WORKFLOW.md

IDE Integration

GitHub Copilot (VS Code / JetBrains)

Add to your .github/copilot-instructions.md:

## Task Workflow Skills

This project uses TaskGate for structured AI-assisted task execution.

<skills>
  <skill>
    <name>new-task</name>
    <description>Use for planning complex tasks. Trigger with "create task:" command.</description>
    <file>.github/skills/new-task/SKILL.md</file>
  </skill>
  <skill>
    <name>execute-task</name>
    <description>Use to execute approved task plans. Trigger with "execute task:" command.</description>
    <file>.github/skills/execute-task/SKILL.md</file>
  </skill>
</skills>

Cursor

Skills are automatically detected from .github/skills/.

Claude (via CLAUDE.md or system prompt)

Reference the skill files in your project instructions.

Commands

Command Description
create task: [description] Start planning a new task
execute task: [TASK-NAME] Execute an approved plan
resume task: [TASK-NAME] Resume interrupted planning
continue Proceed to next atomic task
wait Pause for manual review
redo Retry last task with changes
stop End execution session

Documentation

Why "TaskGate"?

Task = Structured work broken into atomic pieces Gate = Human approval checkpoints between each step

You stay in control. The AI does the heavy lifting. Every change is verified and approved.

Contributing

Contributions welcome! Please read our contributing guidelines first.

License

MIT © Konrad Czerwinski