
Dataverse Python Production Code
Generate production-grade Python against Microsoft Dataverse with retries, OData filters, and typed error handling.
Overview
Dataverse Python Production Code is an agent skill for the Build phase that generates production-ready Python using the Dataverse SDK with retries, OData optimization, and structured error handling.
Install
npx skills add https://github.com/github/awesome-copilot --skill dataverse-python-production-codeWhat is this skill?
- DataverseError hierarchy with ValidationError, MetadataError, and HttpError handling
- Retry loop with exponential backoff for HTTP 429 and timeout failures
- Singleton client pattern for connection management
- OData server-side filter and column select optimization
- Type hints, docstrings, and structured logging for audit trails
- max_retries default of 3 with exponential backoff in the retry template
Adoption & trust: 9.3k installs on skills.sh; 34.6k GitHub stars; 3/3 security scanners passed (skills.sh audits).
What problem does it solve?
You need Dataverse CRUD or query code but generic Python examples skip retries, OData tuning, and the SDK’s error types.
Who is it for?
Solo builders shipping Power Platform or Dynamics-adjacent backends who want SDK code that survives throttling and validation failures.
Skip if: Teams that only need one-off scripts with no logging, no retries, or no Microsoft Dataverse dependency.
When should I use this skill?
You need production-ready Python against Dataverse with error handling, optimization, and Microsoft SDK patterns.
What do I get? / Deliverables
You get copy-paste modules with singleton client usage, exponential backoff, logging, and type-hinted functions ready to drop into your app or agent tool.
- Python modules with retry wrappers and DataverseError handling
- OData-optimized query and mutation functions with type hints and docstrings
Recommended Skills
Journey fit
Canonical shelf is Build because the skill outputs SDK integration code for Power Platform Dataverse, not deployment or growth work. Integrations is the right subphase: it wires agents to an external CRM/data API via the official client SDK.
How it compares
Use instead of asking the model for generic REST samples that ignore DataverseError and OData best practices.
Common Questions / FAQ
Who is dataverse-python-production-code for?
Indie developers and agent users integrating Microsoft Dataverse or Power Platform data into Python services, automations, or agent tools.
When should I use dataverse-python-production-code?
During Build integrations when you are wiring agents or APIs to Dataverse and need retry logic, selective OData queries, and proper exception handling in one pass.
Is dataverse-python-production-code safe to install?
Review the Security Audits panel on this Prism page for install risk and file integrity; the skill instructs generated code to call live Dataverse APIs when you run it.
SKILL.md
READMESKILL.md - Dataverse Python Production Code
# 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 ```python 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 ```python 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 ```python 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