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APIs & servers (backend)40k · #5108

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

https://github.com/github/awesome-copilot/blob/main/skills/dataverse-python-production-code/SKILL.md

Ready-to-paste message

Use this skill to help me: https://github.com/github/awesome-copilot/blob/main/skills/dataverse-python-production-code/SKILL.md Read the SKILL.md file at that link and follow its instructions for my task.
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-code

Or download just the file:

curl -L https://raw.githubusercontent.com/github/awesome-copilot/main/skills/dataverse-python-production-code/SKILL.md -o SKILL.md
Technical 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 select parameter to limit columns
  • Use filter on server (lowercase logical names)
  • Use orderby, top for pagination
  • Use expand for related records when available

Code Structure

  1. Imports (stdlib, then third-party, then local)
  2. Constants and enums
  3. Logging configuration
  4. Helper functions
  5. Main service classes
  6. Error handling classes
  7. Usage examples

User Request Processing

When user asks to generate code, provide:

  1. Imports section with all required modules
  2. Configuration section with constants/enums
  3. Main implementation with proper error handling
  4. Docstrings explaining parameters and return values
  5. Type hints for all functions
  6. Usage example showing how to call the code
  7. Error scenarios with exception handling
  8. 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

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