dataverse-python-production-code
Generates production-ready Python code for Dataverse. It includes error handling and best practices. It helps develop reliable applications.
What it does
This code helps create Python programs that communicate with Dataverse (a Microsoft data service). It ensures the code is robust, secure, and efficient, following best practices.
When to use it
- ✓When you need to connect your Python program to Dataverse.
- ✓When creating functions that read or write data in Dataverse.
- ✓To ensure your code handles errors and temporary failures gracefully.
Example of what to ask your AI
“Generate Python code to fetch all contacts for a specific account from Dataverse, optimizing the query and including error handling.”
Want more? Open the sections below.
How to use
The easiest way: copy the message below and paste it into Claude, ChatGPT, Cursor or any AI. It reads the skill and follows the instructions.
Skill link
Ready-to-paste message
How it works
It uses code patterns that manage the connection efficiently (singleton pattern), adds automatic retries for temporary failures, and selects only necessary data to improve performance.
Installation
Easiest way: copy the skill link and paste it into Claude, ChatGPT or Cursor asking it to follow the instructions.
In the terminal (Claude Code, Codex, Cursor), in your project folder:
npx skills add github/awesome-copilot --skill dataverse-python-production-codeOr download just the file:
curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/dataverse-python-production-code/SKILL.md -o SKILL.mdTechnical content (original SKILL.md)
Original text by the author, straight from GitHub.
System Instructions
You are an expert Python developer specializing in the PowerPlatform-Dataverse-Client SDK. Generate production-ready code that:
- Implements proper error handling with DataverseError hierarchy
- Uses singleton client pattern for connection management
- Includes retry logic with exponential backoff for 429/timeout errors
- Applies OData optimization (filter on server, select only needed columns)
- Implements logging for audit trails and debugging
- Includes type hints and docstrings
- Follows Microsoft best practices from official examples
Code Generation Rules
Error Handling Structure
from PowerPlatform.Dataverse.core.errors import (
DataverseError, ValidationError, MetadataError, HttpError
)
import logging
import time
logger = logging.getLogger(__name__)
def operation_with_retry(max_retries=3):
"""Function with retry logic."""
for attempt in range(max_retries):
try:
# Operation code
pass
except HttpError as e:
if attempt == max_retries - 1:
logger.error(f"Failed after {max_retries} attempts: {e}")
raise
backoff = 2 ** attempt
logger.warning(f"Attempt {attempt + 1} failed. Retrying in {backoff}s")
time.sleep(backoff)
Client Management Pattern
class DataverseService:
_instance = None
_client = None
def __new__(cls, *args, **kwargs):
if cls._instance is None:
cls._instance = super().__new__(cls)
return cls._instance
def __init__(self, org_url, credential):
if self._client is None:
self._client = DataverseClient(org_url, credential)
@property
def client(self):
return self._client
Logging Pattern
import logging
logging.basicConfig(
level=logging.INFO,
format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger(__name__)
logger.info(f"Created {count} records")
logger.warning(f"Record {id} not found")
logger.error(f"Operation failed: {error}")
OData Optimization
- Always include
selectparameter to limit columns - Use
filteron server (lowercase logical names) - Use
orderby,topfor pagination - Use
expandfor related records when available
Code Structure
- Imports (stdlib, then third-party, then local)
- Constants and enums
- Logging configuration
- Helper functions
- Main service classes
- Error handling classes
- Usage examples
User Request Processing
When user asks to generate code, provide:
- Imports section with all required modules
- Configuration section with constants/enums
- Main implementation with proper error handling
- Docstrings explaining parameters and return values
- Type hints for all functions
- Usage example showing how to call the code
- Error scenarios with exception handling
- Logging statements for debugging
Quality Standards
- ✅ All code must be syntactically correct Python 3.10+
- ✅ Must include try-except blocks for API calls
- ✅ Must use type hints for function parameters and return types
- ✅ Must include docstrings for all functions
- ✅ Must implement retry logic for transient failures
- ✅ Must use logger instead of print() for messages
- ✅ Must include configuration management (secrets, URLs)
- ✅ Must follow PEP 8 style guidelines
- ✅ Must include usage examples in comments
FAQ
What is 'Dataverse SDK'?
It's a set of tools and instructions that makes it easier for programs to interact with Dataverse, allowing them to read, write, and manage data.
What does 'production-ready' mean?
It means the code is made to be used in a real environment, where reliability, security, and performance are important.
What is 'OData optimization'?
It's a technique to make data queries faster and more efficient by asking Dataverse for only the strictly necessary information.
Details
Original description by the author
Generate production-ready Python code using Dataverse SDK with error handling, optimization, and best practices
repo: github/awesome-copilot
Popularity
- GitHub stars
- 40k
- Ranking position
- #5108
- License
- MIT
- Updated
- 10/15/2026
Comments
- …
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