Separate read and write models with command and query buses for scalable, event-driven architectures.
Works with
Provides command and query handler infrastructure with bus patterns for dispatching operations to appropriate handlers
Includes templates for command validation, event persistence, read model projections, and FastAPI integration
Supports eventual consistency patterns with checkpoint-based synchronization and read-your-writes consistency helpers
Covers event sourcing fundamentals,
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Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versioncqrs-implementationExecute the skills CLI command in your project's root directory to begin installation:
Fetches cqrs-implementation from wshobson/agents and configures it for Cursor.
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Confirm successful installation by checking the skill directory location:
Restart Cursor to activate cqrs-implementation. Access via /cqrs-implementation in your agent's command palette.
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Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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Comprehensive guide to implementing CQRS (Command Query Responsibility Segregation) patterns.
┌─────────────┐
│ Client │
└──────┬──────┘
│
┌────────────┴────────────┐
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Commands │ │ Queries │
│ API │ │ API │
└──────┬──────┘ └──────┬──────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Command │ │ Query │
│ Handlers │ │ Handlers │
└──────┬──────┘ └──────┬──────┘
│ │
▼ ▼
┌─────────────┐ ┌─────────────┐
│ Write │─────────►│ Read │
│ Model │ Events │ Model │
└─────────────┘ └─────────────┘
| Component | Responsibility |
|---|---|
| Command | Intent to change state |
| Command Handler | Validates and executes commands |
| Event | Record of state change |
| Query | Request for data |
| Query Handler | Retrieves data from read model |
| Projector | Updates read model from events |
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TypeVar, Generic, Dict, Any, Type
from datetime import datetime
import uuid
# Command base
@dataclass
class Command:
command_id: str = None
timestamp: datetime = None
def __post_init__(self):
self.command_id = self.command_id or str(uuid.uuid4())
self.timestamp = self.timestamp or datetime.utcnow()
# Concrete commands
@dataclass
class CreateOrder(Command):
customer_id: str
items: list
shipping_address: dict
@dataclass
class AddOrderItem(Command):
order_id: str
product_id: str
quantity: int
price: float
@dataclass
class CancelOrder(Command):
order_id: str
reason: str
# Command handler base
T = TypeVar('T', bound=Command)
class CommandHandler(ABC, Generic[T]):
@abstractmethod
async def handle(self, command: T) -> Any:
pass
# Command bus
class CommandBus:
def __init__(self):
self._handlers: Dict[Type[Command], CommandHandler] = {}
def register(self, command_type: Type[Command], handler: CommandHandler):
self._handlers[command_type] = handler
async def dispatch(self, command: Command) -> Any:
handler = self._handlers.get(type(command))
if not handler:
raise ValueError(f"No handler for {type(command).__name__}")
return await handler.handle(command)
# Command handler implementation
class CreateOrderHandler(CommandHandler[CreateOrder]):
def __init__(self, order_repository, event_store):
self.order_repository = order_repository
self.event_store = event_store
async def handle(self, command: CreateOrder) -> str:
# Validate
if not command.items:
raise ValueError("Order must have at least one item")
# Create aggregate
order = Order.create(
customer_id=command.customer_id,
items=command.items,
shipping_address=command.shipping_address
)
# Persist events
await self.event_store.append_events(
stream_id=f"Order-{order.id}",
stream_type="Order",
events=order.uncommitted_events
)
return order.id
from abc import ABC, abstractmethod
from dataclasses import dataclass
from typing import TypeVar, Generic, List, Optional
# Query base
@dataclass
class Query:
pass
# Concrete queries
@dataclass
class GetOrderById(Query):
order_id: str
@dataclass
class GetCustomerOrders(Query):
customer_id: str
status: Optional[str] = None
page: int = 1
page_size: int = 20
@dataclass
class SearchOrders(Query):
query: str
filters: dict = None
sort_by: str = "created_at"
sort_order: str = "desc"
# Query result types
@dataclass
class OrderView:
order_id: str
customer_id: str
status: str
total_amount: float
item_count: int
created_at: datetime
shipped_at: Optional[datetime] = None
@dataclass
class PaginatedResult(Generic[T])✓Make data-driven prioritization decisions faster
Stakeholder Communication
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
✓Save 3-5 hours/week on communication overhead
Implementation Guide
Prerequisites
- ›Claude Desktop or compatible AI client
- ›Access to product documentation and roadmap tools (Jira, Notion, etc.)
- ›Understanding of product management frameworks (RICE, Jobs-to-be-Done, etc.)
- ›Stakeholder contact information and communication channels
Time Estimate
30-60 minutes to see productivity improvements
Steps
- 1Install product management skill
- 2Start with user story generation for known feature
- 3Progress to competitive analysis: research 2-3 competitors
- 4Use for roadmap prioritization: apply RICE/ICE scoring
- 5Draft stakeholder communications and refine based on feedback
- 6Build template library for recurring PM tasks
- 7Share effective prompts with product team
Common Pitfalls
- ⚠Not validating competitive research—verify facts before sharing
- ⚠Accepting user stories without involving engineering team
- ⚠Over-relying on frameworks without qualitative judgment
- ⚠Not customizing outputs to company culture and communication style
- ⚠Skipping stakeholder validation of generated requirements
Best Practices
✓ Do
- +Validate research and competitive analysis with real data
- +Collaborate with engineering when generating technical requirements
- +Customize frameworks and templates to your company context
- +Use skill for first drafts, refine with stakeholder input
- +Document successful prompt patterns for PM tasks
- +Combine AI efficiency with human judgment and intuition
✗ Don't
- −Don't publish competitive analysis without fact-checking
- −Don't finalize user stories without engineering review
- −Don't make prioritization decisions solely on AI scoring
- −Don't skip customer validation of generated requirements
- −Don't ignore company-specific context and culture
💡 Pro Tips
- ★Provide context: company goals, constraints, customer feedback
- ★Ask for alternatives: 'Show 3 ways to prioritize this roadmap'
- ★Request stakeholder-specific formatting: 'Executive summary vs. engineering spec'
- ★Use skill for 70% generation + 30% customization to company needs
When to Use This
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
Learning Path
- 1Basic: user stories, feature specs, status updates
- 2Intermediate: competitive analysis, prioritization frameworks, PRDs
- 3Advanced: product strategy, go-to-market planning, OKR setting
- 4Expert: product vision, market positioning, business model innovation
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4.4★★★★★60 reviews- PPratham Ware★★★★★Dec 28, 2024
Solid pick for teams standardizing on skills: cqrs-implementation is focused, and the summary matches what you get after install.
- BBenjamin Okafor★★★★★Dec 24, 2024
cqrs-implementation is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- MMin Huang★★★★★Dec 20, 2024
cqrs-implementation has been reliable in day-to-day use. Documentation quality is above average for community skills.
- CCamila Iyer★★★★★Dec 16, 2024
Registry listing for cqrs-implementation matched our evaluation — installs cleanly and behaves as described in the markdown.
- NNia Gonzalez★★★★★Dec 4, 2024
Solid pick for teams standardizing on skills: cqrs-implementation is focused, and the summary matches what you get after install.
- CCarlos Khan★★★★★Nov 23, 2024
We added cqrs-implementation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- YYash Thakker★★★★★Nov 19, 2024
We added cqrs-implementation from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
- MMaya Menon★★★★★Nov 11, 2024
cqrs-implementation fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- SSakura Bhatia★★★★★Nov 7, 2024
Useful defaults in cqrs-implementation — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- HHana Martinez★★★★★Oct 26, 2024
I recommend cqrs-implementation for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
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