feat: implement tdd workflow (#1309)

Co-authored-by: Claude <noreply@anthropic.com>
Co-authored-by: github-actions[bot] <github-actions[bot]@users.noreply.github.com>
This commit is contained in:
Ralph Khreish
2025-10-18 16:29:03 +02:00
committed by GitHub
parent b8830d9508
commit ccb87a516a
106 changed files with 25097 additions and 1541 deletions

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{
"mode": "pre",
"tag": "rc",
"initialVersions": {
"task-master-ai": "0.29.0",
"@tm/cli": "",
"docs": "0.0.6",
"extension": "0.25.6",
"@tm/mcp": "0.28.0-rc.2",
"@tm/ai-sdk-provider-grok-cli": "",
"@tm/build-config": "",
"@tm/claude-code-plugin": "0.0.2",
"@tm/core": ""
},
"changesets": [
"dirty-hairs-know",
"fix-parent-directory-traversal",
"fix-warning-box-alignment",
"light-owls-stay",
"metal-rocks-help"
]
}

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---
"task-master-ai": minor
---
Add autonomous TDD workflow automation system with new `tm autopilot` commands and MCP tools for AI-driven test-driven development.
**New CLI Commands:**
- `tm autopilot start <taskId>` - Initialize TDD workflow
- `tm autopilot next` - Get next action in workflow
- `tm autopilot status` - Check workflow progress
- `tm autopilot complete` - Advance phase with test results
- `tm autopilot commit` - Save progress with metadata
- `tm autopilot resume` - Continue from checkpoint
- `tm autopilot abort` - Cancel workflow
**New MCP Tools:**
Seven new autopilot tools for programmatic control: `autopilot_start`, `autopilot_next`, `autopilot_status`, `autopilot_complete_phase`, `autopilot_commit`, `autopilot_resume`, `autopilot_abort`
**Features:**
- Complete RED → GREEN → COMMIT cycle enforcement
- Intelligent commit message generation with metadata
- Activity logging and state persistence
- Configurable workflow settings via `.taskmaster/config.json`
- Comprehensive AI agent integration documentation
**Documentation:**
- AI Agent Integration Guide (2,800+ lines)
- TDD Quick Start Guide
- Example prompts and integration patterns
> **Learn more:** [TDD Workflow Quickstart Guide](https://dev.task-master.dev/tdd-workflow/quickstart)
This release enables AI agents to autonomously execute test-driven development workflows with full state management and recovery capabilities.