dataverse-python-production-code

Generate production-ready Python code for Dataverse SDK with error handling and best practices.

github/awesome-copilotUpdated Apr 8, 2026

Works with

Claude CodeCursorClineWindsurfCodexGooseGitHub CopilotZed

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Install Skill

Run in your terminal

$npx skills add https://github.com/github/awesome-copilot --skill dataverse-python-production-code

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this week

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What it does

  • Implements comprehensive error handling using DataverseError hierarchy with retry logic and exponential backoff for transient failures

  • Enforces singleton client pattern for connection management and includes structured logging for audit trails and debugging

  • Applies OData optimization techniques: server-side filtering, column selection, and pagination to reduce data transfer

  • Provides typ

Category

Backend

Last updated

Apr 8, 2026

Installation Guide

How to use dataverse-python-production-code on Cursor

AI-first code editor with Composer

1

Prerequisites

Before installing skills in Cursor, ensure your development environment meets these requirements:

  • Cursor installed and configured on your machine
  • Node.js 16+ with npm — verify with node --version
  • Active project directory where you want to add dataverse-python-production-code
2

Run the install command

Execute the skills CLI command in your project's root directory to begin installation:

$npx skills add https://github.com/github/awesome-copilot --skill dataverse-python-production-code

Fetches dataverse-python-production-code from github/awesome-copilot and configures it for Cursor.

3

Select Cursor when prompted

The CLI shows a list of agents. Use arrow keys and space to select Cursor:

◆ Which agents do you want to install to?
│ ── Universal (.agents/skills) ────────────────
│ · Cline · Codex · Goose · Windsurf
│ ●Cursor(selected)
│ · Cursor · Aider · Continue
4

Verify installation

Confirm successful installation by checking the skill directory location:

.cursor/skills/dataverse-python-production-code

Restart Cursor to activate dataverse-python-production-code. Access via /dataverse-python-production-code in your agent's command palette.

Security Notice

We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.

Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.

Documentation

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

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Use Cases

Task Automation & Efficiency

Automate repetitive workflows and reduce manual effort

Example

Generate reports, summarize documents, draft communications

Save 3-5 hours per week on routine tasks

Knowledge Enhancement

Learn new skills, understand complex topics, get expert guidance

Example

Explain concepts, provide examples, suggest learning resources

Accelerate learning and skill development by 2x

Quality Improvement

Enhance output quality through reviews, suggestions, and refinements

Example

Review drafts, suggest improvements, catch errors

Improve work quality by 30-40% with less effort

Implementation Guide

Prerequisites

  • Claude Desktop or compatible AI client with skill support
  • Clear understanding of task or problem to solve
  • Willingness to iterate and refine outputs

Time Estimate

15-45 minutes depending on use case complexity

Steps

  1. 1Install skill using provided installation command
  2. 2Test with simple use case relevant to your work
  3. 3Evaluate output quality and relevance
  4. 4Iterate on prompts to improve results
  5. 5Integrate into regular workflow if valuable

Common Pitfalls

  • Expecting perfect results without iteration
  • Not providing enough context in prompts
  • Using skill for tasks outside its intended scope
  • Accepting outputs without review and validation

Best Practices

✓ Do

  • +Start with clear, specific prompts
  • +Provide relevant context and constraints
  • +Review and refine all outputs before using
  • +Iterate to improve output quality
  • +Document successful prompt patterns

✗ Don't

  • Don't use without understanding skill limitations
  • Don't skip validation of outputs
  • Don't share sensitive information in prompts
  • Don't expect skill to replace human judgment

💡 Pro Tips

  • Be specific about desired format and style
  • Ask for multiple options to choose from
  • Request explanations to understand reasoning
  • Combine AI efficiency with human expertise

When to Use This

✓ Use when

Use when skill capabilities match your task, clear ROI on time saved, and you can validate outputs. Best for repetitive tasks, learning, and quality improvement.

✗ Avoid when

Avoid when task requires deep expertise you can't validate, involves sensitive decisions, or when learning process is more valuable than speed of completion.

Learning Path

  1. 1Familiarize yourself with skill capabilities and limitations
  2. 2Start with low-risk, non-critical tasks
  3. 3Progress to more complex and valuable use cases
  4. 4Build expertise through regular use and experimentation

Related Skills

Reviews

4.647 reviews
  • M
    Michael SharmaDec 12, 2024

    Keeps context tight: dataverse-python-production-code is the kind of skill you can hand to a new teammate without a long onboarding doc.

  • M
    Mia SanchezDec 8, 2024

    dataverse-python-production-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • C
    Chinedu MensahDec 8, 2024

    Useful defaults in dataverse-python-production-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.

  • K
    Kaira HuangNov 27, 2024

    I recommend dataverse-python-production-code for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.

  • M
    Mia RamirezNov 23, 2024

    dataverse-python-production-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.

  • H
    Hassan TandonNov 3, 2024

    dataverse-python-production-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

  • H
    Hassan GuptaOct 22, 2024

    dataverse-python-production-code reduced setup friction for our internal harness; good balance of opinion and flexibility.

  • C
    Chinedu KimOct 18, 2024

    Solid pick for teams standardizing on skills: dataverse-python-production-code is focused, and the summary matches what you get after install.

  • M
    Mia MenonOct 14, 2024

    dataverse-python-production-code has been reliable in day-to-day use. Documentation quality is above average for community skills.

  • A
    Aarav LiuSep 25, 2024

    dataverse-python-production-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.

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