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Dataverse Python Advanced Patterns

GitHubOfficialOriginally contributed by @troystaylor as prompts/dataverse-python-advanced-patterns.prompt.md; moved into a skill by pull request #761Distributed by GitHub

  • Skill
  • Tool integration
  • Software development
  • MIT
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From the author

Generate production code for Dataverse SDK using advanced patterns, error handling, and optimization techniques.
— the description by troystaylor

AutoGineer summary

Written by AutoGineer from the skill’s own files, not by the author

This skill acts as an expert assistant for the Dataverse SDK for Python. It provides guidance and code generation for implementing robust integration patterns, including error handling with exponential backoff, batch operations, and OData query optimization. It covers metadata management, configuration settings, cache handling, file operations, and integration with Pandas for data-heavy workflows.

What it does

  • Generates code for Dataverse SDK using advanced error handling and retry logic.
  • Implements bulk create, update, and delete operations with error recovery.
  • Optimizes OData queries using filtering, selection, and paging techniques.
  • Manages table metadata and custom column definitions.
  • Configures SDK settings including timeouts and retry policies.
  • Facilitates large file uploads and Pandas DataFrame integration.

When to use it

  • When developing production-grade integrations with the Dataverse SDK.
  • When implementing complex batch processing or large file handling.
  • When optimizing OData queries for specific data retrieval requirements.
  • When needing to manage custom table metadata or option sets programmatically.

Advantages

  • Provides structured patterns for transient error handling.
  • Ensures consistent use of SDK configuration and timeout settings.
  • Simplifies the implementation of complex batch and file operations.
  • Promotes clean code practices with type hints and docstrings.

Usage examples

“Generate a function to perform a bulk update on Dataverse records with retry logic.”
The agent provides a Python function using the Dataverse SDK that includes exponential backoff and error handling for transient failures during batch operations.
“How do I optimize an OData query for a large table in Dataverse?”
The agent generates code demonstrating the use of select, filter, and paging parameters to retrieve data efficiently.

Install

  1. Download the skill as a ZIP:

    https://mp-staging.autogineer.com/agent-skills/github/dataverse-python-advanced-patterns/download

  2. Unzip it into the cross-client skills folder of your project:

    mkdir -p .agents/skills && unzip dataverse-python-advanced-patterns.zip -d .agents/skills

Documentation by troystaylor

You are a Dataverse SDK for Python expert. Generate production-ready Python code that demonstrates:

  1. Error handling & retry logic — Catch DataverseError, check is_transient, implement exponential backoff.

  2. Batch operations — Bulk create/update/delete with proper error recovery.

  3. OData query optimization — Filter, select, orderby, expand, and paging with correct logical names.

  4. Table metadata — Create/inspect/delete custom tables with proper column type definitions (IntEnum for option sets).

  5. Configuration & timeouts — Use DataverseConfig for http_retries, http_backoff, http_timeout, language_code.

  6. Cache management — Flush picklist cache when metadata changes.

  7. File operations — Upload large files in chunks; handle chunked vs. simple upload.

  8. Pandas integration — Use PandasODataClient for DataFrame workflows when appropriate.

Include docstrings, type hints, and link to official API reference for each class/method used.

Files

ATTRIBUTION.md

Attribution

This folder is an unmodified copy of the Agent Skill "dataverse-python-advanced-patterns", distributed by AutoGineer (https://autogineer.com/agent-skills).

The skill's own files are unmodified copies. AutoGineer is not affiliated with or endorsed by the author or the publisher.

Files added by AutoGineer

  • ATTRIBUTION.md: this file, written by AutoGineer

  • LICENSE: copied from LICENSE

Requirements and compatibility

Written for: GitHub Copilot

Works with: Claude Code, Claude apps, OpenAI Codex, GitHub Copilot, Cursor, Gemini CLI, Kiro, OpenCode, Goose, Amp, Roo Code, JetBrains Junie, Factory Droid, Devin (ex-Windsurf), Mistral Vibe, OpenHands, Letta

“Works with” means the tool reads the open Agent Skills format; a skill written for one tool may use features others lack.

Security

No issues found by our static scan· No issues found

A static scan is not a guarantee. Skills are third-party content: review a skill before you let an agent use it.

Attribution and licence

Originally contributed by
GitHub avatar of troystaylor@troystayloras prompts/dataverse-python-advanced-patterns.prompt.mdmoved into a skill by pull request #761
Publisher
GitHub (Official)
Distributed by
GitHub
Source
github/awesome-copilot · skills/dataverse-python-advanced-patterns · commit 797337f
Licence
MIT License (redistribution allowed)
  • LICENSE (added by AutoGineer from LICENSE)
  • ATTRIBUTION.md (written by AutoGineer)
Synced
Contributors
troystaylor

AutoGineer is not affiliated with or endorsed by troystaylor or GitHub.

Download ZIP

The ZIP holds the skill’s files, its licence files and an ATTRIBUTION.md. Download dataverse-python-advanced-patterns.zip