dataverse-python-production-code▌
github/awesome-copilot · updated Apr 8, 2026
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Generate production-ready Python code for Dataverse SDK with error handling and best practices.
- ›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
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
How to use dataverse-python-production-code on Cursor
AI-first code editor with Composer
Prerequisites
Before installing skills in Cursor, ensure your development environment meets these requirements:
- ›Cursor installed and configured on your development machine
- ›Node.js version 16.0+ with npm package manager (verify with
node --version) - ›Active project directory or workspace where you want to add dataverse-python-production-code
Execute installation command
Execute the skills CLI command in your project's root directory to begin installation:
The skills CLI fetches dataverse-python-production-code from GitHub repository github/awesome-copilot and configures it for Cursor.
Select Cursor when prompted
The CLI will show a list of available agents. Use arrow keys to navigate and space to select Cursor:
Verify installation
Confirm successful installation by checking the skill directory location:
Reload or restart Cursor to activate dataverse-python-production-code. Access the skill through slash commands (e.g., /dataverse-python-production-code) or your agent's skill management interface.
Security & Verification 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 development environment. Always verify the publisher's identity, review recent commits, and test in isolated environments before production deployment.
List & Monetize Your Skill
Submit your Claude Code skill and start earning
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
Installation Steps
- 1.Install skill using provided installation command
- 2.Test with simple use case relevant to your work
- 3.Evaluate output quality and relevance
- 4.Iterate on prompts to improve results
- 5.Integrate 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▌
- 1Familiarize yourself with skill capabilities and limitations
- 2Start with low-risk, non-critical tasks
- 3Progress to more complex and valuable use cases
- 4Build expertise through regular use and experimentation
Discussion
Product Hunt–style comments (not star reviews)- No comments yet — start the thread.
Ratings
4.6★★★★★47 reviews- ★★★★★Michael Sharma· Dec 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.
- ★★★★★Mia Sanchez· Dec 8, 2024
dataverse-python-production-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Chinedu Mensah· Dec 8, 2024
Useful defaults in dataverse-python-production-code — fewer surprises than typical one-off scripts, and it plays nicely with `npx skills` flows.
- ★★★★★Kaira Huang· Nov 27, 2024
I recommend dataverse-python-production-code for anyone iterating fast on agent tooling; clear intent and a small, reviewable surface area.
- ★★★★★Mia Ramirez· Nov 23, 2024
dataverse-python-production-code fits our agent workflows well — practical, well scoped, and easy to wire into existing repos.
- ★★★★★Hassan Tandon· Nov 3, 2024
dataverse-python-production-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
- ★★★★★Hassan Gupta· Oct 22, 2024
dataverse-python-production-code reduced setup friction for our internal harness; good balance of opinion and flexibility.
- ★★★★★Chinedu Kim· Oct 18, 2024
Solid pick for teams standardizing on skills: dataverse-python-production-code is focused, and the summary matches what you get after install.
- ★★★★★Mia Menon· Oct 14, 2024
dataverse-python-production-code has been reliable in day-to-day use. Documentation quality is above average for community skills.
- ★★★★★Aarav Liu· Sep 25, 2024
dataverse-python-production-code is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
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