AutoCraft - AI-Powered Project Execution Platform

🚀 Empower non-technical product managers to drive complex software development with 3-6x efficiency

Version: v2.1.0 Updated: 2026-05-20 Changes: Complete skill package with installation automation

📦 System Installation

One-Click Install & Deploy

# 1. Install skill from ClawHub
clawhub install autocraft

# 2. Navigate to autocraft directory
cd autocraft

# 3. Run one-click installation script
bash install.sh

# 4. Access the system
# Frontend UI: http://localhost:8080
# API Docs:    http://localhost:9001/docs

install.sh automatically:

• Downloads complete system code from GitHub/Gitee (~5.6MB)
• Installs backend dependencies (Python + FastAPI)
• Installs frontend dependencies (Node.js + Vue3)
• Starts backend service (port 9001)
• Starts frontend service (port 8080)

Manual Installation (Optional)

# Clone complete system code
git clone https://github.com/Robin-Chen2025/autocraft-opensource.git
cd autocraft-opensource

# Backend setup
cd backend
pip install -r requirements.txt
python3 -m uvicorn main:app --host 0.0.0.0 --port 9001

# Frontend setup (new terminal)
cd ..
npm install
npm run dev

📊 Real Case: Complete Education Platform in 10 Days

📈 Project Scale:
   Plans: 19
   Tasks: 71
   Success: 70 (99% success rate)

🔧 Quality Metrics:
   Bugs Found: 7 (all auto-fixed)
   Test Coverage: 100% (L1+L2+L3)
   Manual Interventions: Only 3 key decisions

⏱️ Efficiency Comparison:
   Traditional Estimate: 1-2 months
   AutoCraft: 10 days (3-6x improvement)

🎯 Your Role

You are the project manager - make decisions, break down tasks, verify deliverables. Don't write code.

Two-Phase Model

Phase 1: Design Phase (Without AutoCraft)
    │
    │  PRD → Feature Design → Tech Solution → API/DB/UI Design
    │  → Test Plan → Development Plan (Overview + Work Plans)
    │  → Overall Verification
    │
    ▼  Development Plan Finalized
Phase 2: Execution Phase (Enter AutoCraft)
    │
    │  Break down task tickets → Import via API
    │  → Execution engine runs tasks (AI agents)
    │  → Auto verification → Manager approval → Status cascade
    │
    ▼  Project Complete

Phase 1: Design Phase

Design Document System

Follow references/design-specs/设计阶段文档规范-总纲.md.

Required Documents:

Optional Documents:

Document Generation Flow

Use sub-agents to generate documents:

1. Prepare input materials (prior docs + spec files)
2. Sub-agent generates first version
3. Sub-agent reviews against checklist
4. Fix based on issues found
5. Re-review to confirm
6. Finalize (Boss approval)

Sub-agent Invocation:

Design phase uses multi-turn session mode:

# Create persistent session (supports multiple review-fix cycles)
openclaw agent --session-id explicit:doc_session_{project_name}_{timestamp} \
  --agent-id ac-glm5 \
  --model glm-5 \
  --message "Read the following spec files and input materials, generate {document_type} document..."

# Review in same session
openclaw agent --session-id explicit:doc_session_{project_name}_{timestamp} \
  --message "Review the generated document against checklist..."

# Fix in same session
openclaw agent --session-id explicit:doc_session_{project_name}_{timestamp} \
  --message "Fix the document based on review report..."

Key Requirements:

• Design phase document generation uses multi-turn session mode
• Sub-agent must read corresponding spec files and review checklists before generating
• Immediately self-review after generation using checklist
• Score ≥80 to submit for Boss review, \x3C70 regenerate
• Use GLM-5 model for design phase work

Test Plan

Generate according to references/design-specs/质量检测方案/:

Development Plan

Generate according to references/design-specs/开发计划方案/:

Two-layer structure:

• Development Plan Overview: Batch order, module dependencies, feature overview
• Work Plan List: Task ticket structure for each work plan

Phase 2: Execution Phase

Import to AutoCraft

After development plan is finalized, main agent breaks down task tickets and imports via API:

# 1. Create project profile
curl -X POST http://localhost:9001/api/profiles \
  -H "Content-Type: application/json" \
  -d '{"profile_id":"...", "profile_name":"...", ...}'

# 2. Create phases and workflows
curl -X POST http://localhost:9001/api/profiles/{id}/phases -d '...'
curl -X POST http://localhost:9001/api/profiles/{id}/workflows -d '...'

# 3. Create work plans
curl -X POST http://localhost:9001/plans -d '...'

# 4. Create task tickets one by one
curl -X POST http://localhost:9001/tasks -d '...'

Task Creation

⚠️ Follow references/task-creator/SKILL.md, including:

• Standard input_data format and required fields
• workflow_type to task_type mapping
• input_files configuration guide
• Batch creation example code
• Common errors and checklists

Core Rules:

1. All paths must be absolute paths
2. project_path must point to correct project root
3. input_files must contain at least 1 design document
4. requirements must be detailed and specific
5. expected_output_files must list all expected files
6. input_data must be converted to JSON string via json.dumps()

Simple FlowTicket Execution

Invocation:

# 1. Submit execution request (async)
curl -X POST http://localhost:9001/api/v2/tasks/execute \
  -H "Content-Type: application/json" \
  -d '{
    "task_id": \x3Ctask_id>,
    "model": "glm-5.1",
    "label": "Execute task XXX",
    "timeout": 1800
  }'

# 2. Query task status (polling)
curl -X GET "http://localhost:9001/api/v2/tasks/{task_id}/status"

# 3. Check execution result
# - Status "completed" or "verified" = success
# - Status "failed" or "verification_failed" = failure
# - execution_log and verification_log contain details

Task Status Flow:

pending → in_progress → completed → verifying → verified
                     ↓
                   failed → (can re-execute)
                                  ↓
                          verification_failed

Model Configuration:

Verification Dimensions (7)

Approval Rules

1. Don't trust agent's "completed" - must check output files
2. Verification is automatic - PASS/FAIL per dimension, all PASS to pass
3. Output path must match - check files are in deliverables locations
4. Architecture check enforced - new dimension 6 "Architecture", SRP violation = FAIL
5. Design compliance enforced - new dimension 7 "Design Compliance", check implementation matches design docs

AutoCraft System Info

Service Management:

sudo systemctl restart autocraft-backend autocraft-frontend
sudo systemctl status autocraft-backend autocraft-frontend
curl http://localhost:9001/health  # Health check

Iron Rules

Sub-Agent Guide

Comprehensive guide for ac-glm5 (execution agent) and ac-validator (verification agent):

• ac-agent-guide: references/ac-agent-guide/SKILL.md
  • Role identification (auto-detect execution/verification by Agent ID)
  • Execution agent: 5 task types (BUILD-CODE/BUILD-TEST/BUILD-ENV/DOC/DESIGN)
  • Verification agent: 3 task types + PASS/FAIL judgment + verification dimensions
  • Auxiliary skill index: auto-select skill by tech stack
  • JSON result file format spec
  • Shared rules (file operations, output directory, common commands)

When sub-agent is invoked by AutoCraft execution engine, it should read this skill for role definition and behavior specs.

Architecture Check & Template Library

Architecture Check Tool

New verification dimension 6: Architecture, checks if code follows design principles:

Architecture check script: scripts/architecture-check/architecture_check.py

• Check file responsibility singularity (SRP principle)
• Check code structure rationality (layered architecture)
• Check functional boundary clarity (coupling)
• Generate architecture health report

Usage:

python3 scripts/architecture-check/architecture_check.py --path /path/to/code --report architecture_report.json

Standardized Template Library

Provides standard code templates to ensure architecture consistency:

Reference Document Index