
dev-orchestration
by topstolenname
Mind-link
SKILL.md
name: dev-orchestration description: Coordinate a team of AI agents (Architect-PM, Developer, QA-Critic, Tech-Writer) to build complex software projects iteratively. Use when the user says "build me a [project]", "implement [feature] using the dev team approach", "create a [system] with proper testing and documentation", or requests a multi-component system that needs planning, implementation, testing, and documentation. Prevents "codebase drift" through component-level iteration with QA feedback loops.
Dev Team Orchestration Skill
Transform complex software requests into production-ready code through coordinated multi-agent development.
Overview
This skill orchestrates a 4-agent development team that builds software iteratively:
- Architect-PM - Requirements gathering and system design
- Developer - Component implementation
- QA-Critic - Testing and quality assurance
- Tech-Writer - Documentation and usability
The key innovation is component-level iteration with tight QA feedback loops, preventing the "500 lines of broken code" problem common in AI-generated projects.
When to Use
Trigger this skill for:
- Multi-component projects: "Build me a Python CLI for managing TODOs with SQLite"
- Quality-critical systems: "Create a web API with full test coverage"
- Production-ready code: "Implement a data pipeline with proper error handling and docs"
- Team-style development: "Use the dev team approach to build this"
Do NOT use for:
- Simple scripts (<100 lines, single file)
- Quick prototypes without testing requirements
- Exploratory coding or experiments
Core Workflow
┌─────────────────────┐
│ PLANNING PHASE │ Architect-PM creates spec.md, design.md
└──────────┬──────────┘
↓
┌─────────────────────────────────────┐
│ IMPLEMENTATION PHASE (per component)│
│ ┌──────────────┐ │
│ │ Developer │ Implement │
│ └───────┬──────┘ │
│ ↓ │
│ ┌──────────────┐ │
│ │ QA-Critic │ Test │
│ └───────┬──────┘ │
│ ↓ │
│ PASS? ──NO──→ Back to Developer │
│ │ │
│ YES │
│ ↓ │
│ Next component? │
└──────────┬──────────────────────────┘
↓
┌─────────────────────┐
│ DOCUMENTATION PHASE │ Tech-Writer creates README
└─────────────────────┘
How to Use
Option 1: Manual Orchestration (Recommended for Learning)
Run the orchestrator script manually and interact with each agent:
cd your-project-directory
python /path/to/.skills/dev-orchestration/scripts/orchestrator.py "my-project"
The script will:
- Load or create
.dev-team-state.json(persistent state) - Determine which agent should work next
- Display the agent's system prompt and task
- Wait for you to complete the task
- Advance to the next agent
Example session:
📝 Project name: todo-cli
====================================================
🤖 AGENT: ARCHITECT-PM
====================================================
📋 TASK:
Review the user request. Create:
1. spec.md - User stories, acceptance criteria
2. design.md - Architecture, component list
3. schema.sql (if database needed)
====================================================
[You create the planning documents]
Press Enter when agent has completed this task...
====================================================
🤖 AGENT: DEVELOPER
====================================================
📋 TASK:
Implement component: database-layer
Refer to spec.md and design.md for requirements.
====================================================
[You implement the component]
Press Enter when agent has completed this task...
====================================================
🤖 AGENT: QA-CRITIC
====================================================
📋 TASK:
Test component: database-layer
1. Write test file: tests/test_database-layer.py
2. Execute tests using pytest
3. Analyze results
====================================================
[Tests run automatically if pytest is installed]
✅ database-layer PASSED quality checks
▶ Continue to next agent? (y/n/quit):
Option 2: Automated Integration (For Production Use)
Integrate with your local LLM by modifying orchestrator.py:
# In run_turn() method, replace the manual input:
response = call_your_llm(
system_prompt=system_prompt,
user_prompt=instructions,
model="qwen2.5:7b" # or your preferred model
)
This enables fully autonomous multi-agent development.
Agent Roles & Responsibilities
Architect-PM
Files Created: spec.md, design.md, optionally schema.sql
Responsibilities:
- Clarify ambiguous requirements with questions
- Write user stories and acceptance criteria
- Design system architecture and component breakdown
- Define database schemas if needed
Success Criteria:
design.mdcontains a "## Components" section listing 3-7 components- Each component has clear responsibilities
- Dependencies between components are documented
Developer
Files Modified/Created: Source code files (.py, .js, etc.)
Responsibilities:
- Implement ONE component at a time
- Write clean, testable code with docstrings
- Follow specifications from
spec.mdanddesign.md - Focus on correctness, not perfection
Success Criteria:
- Code runs without syntax errors
- Component implements required functionality
- Code is well-structured for testing
QA-Critic
Files Created: Test files (tests/test_*.py)
Responsibilities:
- Write comprehensive tests for each component
- Test happy paths, edge cases, and error conditions
- Execute tests and analyze failures
- Report failures with actionable details
Success Criteria:
- Test file exists with multiple test cases
- Tests cover core functionality and edge cases
- All tests pass, or failures are clearly reported
Tech-Writer
Files Created/Modified: README.md, docstrings in source files
Responsibilities:
- Create comprehensive README with installation and usage
- Ensure all functions have docstrings
- Write code examples that work
- Document project structure
Success Criteria:
- README includes installation, usage examples, and project structure
- All public functions have docstrings
- Documentation is clear and copy-pasteable
State Management
The orchestrator maintains .dev-team-state.json:
{
"project_name": "todo-cli",
"phase": "implementation",
"current_component": "cli-interface",
"completed_components": ["database-layer"],
"qa_failures": 1,
"discovered_components": ["database-layer", "cli-interface", "integration"],
"history": [
{"role": "architect-pm", "task": "Create planning documents"},
{"role": "developer", "task": "Implement database-layer"},
{"role": "qa-critic", "task": "Test database-layer"}
]
}
Recovery: If the process crashes or is interrupted, simply run the orchestrator again. It will resume from the last saved state.
Reset: To start over: python orchestrator.py --reset
Advanced Features
Auto-Component Discovery
The orchestrator can parse design.md to automatically extract the component list:
## Components
1. **database-layer** - SQLite operations and schema management
2. **cli-interface** - Argument parsing and command handling
3. **integration** - Glue code connecting components
Components are then processed in order automatically.
Automatic QA Result Detection
If pytest is installed, the orchestrator automatically runs tests and detects pass/fail:
# In orchestrator.py
status, error = self.check_test_result(component)
if status == "PASS":
# Advance to next component
elif status == "FAIL":
# Kick back to developer with error details
Iteration Tracking
The orchestrator tracks how many times a component has failed QA:
self.state["qa_failures"] += 1
# After 3 failures, suggest redesigning the component
if self.state["qa_failures"] > 3:
print("⚠️ Component has failed 3+ times. Consider:")
print(" 1. Breaking into smaller sub-components")
print(" 2. Revisiting the design")
print(" 3. Re-engaging Architect-PM")
Project Templates
Pre-built templates for common project types:
Python CLI Template
cp -r assets/templates/python-cli/ ./my-cli-project/
Includes:
src/directory with CLI, core logic, and utilitiestests/directory with test templatessetup.pyfor packagingrequirements.txtwith common dependencies
Python Library Template
cp -r assets/templates/python-lib/ ./my-library/
Includes:
- Public API structure
- Test suite
- Documentation templates
Web App Template
cp -r assets/templates/web-app/ ./my-web-app/
Includes:
- MVC structure
- Route handlers
- Template system
Workflow Customization
Different project types require different workflows. See references/workflow-patterns.md for:
- CLI Tool Pattern (3-5 components)
- Library/Package Pattern (4-8 components)
- Web Application Pattern (5-10 components)
- Data Pipeline Pattern (4-7 components)
Each pattern includes:
- Typical component breakdown
- Testing focus areas
- Special considerations
Best Practices
Component Sizing
- Too small (<50 lines): Consider merging related components
- Good size (50-300 lines): Easy to test, single responsibility
- Too large (>500 lines): Split into sub-components
QA Iteration Limits
- 1-2 iterations: Normal - minor bugs, edge cases
- 3 iterations: Warning - may indicate design issues
- 4+ iterations: Stop - redesign component or split into smaller pieces
Agent Handoff Protocol
Each agent should clearly signal completion:
- Architect-PM: "PLANNING COMPLETE"
- Developer: "Implementation complete for [component]"
- QA-Critic: "QA RESULT: PASS" or "QA RESULT: FAIL - [reason]"
- Tech-Writer: "DOCUMENTATION COMPLETE"
Troubleshooting
"Component keeps failing QA"
Solution:
- Review the test failures carefully
- Check if component scope is too large
- Consider splitting into sub-components
- Re-engage Architect-PM if design is flawed
"Orchestrator asks for manual component selection"
Solution:
- Ensure
design.mdhas a "## Components" section - List components as numbered or bulleted items
- Or manually input component names when prompted
"Tests don't auto-run"
Solution:
- Install pytest:
pip install pytest - Ensure test files follow naming:
tests/test_*.py - Check that test file paths match component names
Integration Examples
With Open Interpreter
from interpreter import interpreter
# In orchestrator.py run_turn()
interpreter.system_message = system_prompt
result = interpreter.chat(instructions)
With Ollama
import ollama
response = ollama.chat(
model='qwen2.5:7b',
messages=[
{'role': 'system', 'content': system_prompt},
{'role': 'user', 'content': instructions}
]
)
With OpenAI-Compatible API
import openai
response = openai.ChatCompletion.create(
model="gpt-4",
messages=[
{"role": "system", "content": system_prompt},
{"role": "user", "content": instructions}
]
)
References
- Agent Personas: See
references/agent-personas.mdfor detailed system prompts - Workflow Patterns: See
references/workflow-patterns.mdfor project type guides - Templates: See
assets/templates/for starter projects
Credits
Architecture inspired by Claude's Agent Skills pattern with progressive disclosure and stateful orchestration for long-running development workflows.
スコア
総合スコア
リポジトリの品質指標に基づく評価
SKILL.mdファイルが含まれている
ライセンスが設定されている
100文字以上の説明がある
GitHub Stars 100以上
3ヶ月以内に更新がある
10回以上フォークされている
オープンIssueが50未満
プログラミング言語が設定されている
1つ以上のタグが設定されている
レビュー
レビュー機能は近日公開予定です