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github/awesome-copilot · Official

dataverse-python-advanced-patterns

Generate production code for Dataverse SDK using advanced patterns, error handling, and optimization techniques.

All-time #1656 First seen Feb 25, 2026
8-week activity · all time api

Installation

$ npx skills add github/awesome-copilot --skill dataverse-python-advanced-patterns
View on skills.sh ↗ GitHub repository ↗

Summary

  • Production-ready Dataverse SDK patterns with error handling, batch operations, and optimization techniques.
  • Demonstrates exponential backoff retry logic for transient errors, batch CRUD operations with error recovery, and OData query optimization using filters, selects, expands, and paging with correct logical names Covers table metadata creation and inspection, custom column definitions with IntEnum option sets, and cache flushing strategies when schema changes Includes configuration best practices via DataverseConfig (http_retries, http_backoff, http_timeout, language_code) and chunked file upload handling for large payloads Provides PandasODataClient integration for DataFrame-based workflows and includes docstrings with type hints linking to official API references

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More details

Agent compatibility

Declared targets from SKILL.md / docs. Unmarked agents are not listed — the skill may still install via the CLI.

Claude Code Not declared
Cursor Not declared
Codex Not declared
GitHub Copilot Declared
Windsurf Not declared
Gemini CLI Not declared
Cline Not declared
OpenCode Not declared

Repository health

Stars 38.8K
License LICENSE
Default branch main
Open issues 21
Status Active

Skill metadata

Parsed from SKILL.md frontmatter.

Declared agents github-copilot

Package contents

Files included with this skill beyond the listing page.

  • skill md SKILL.md 1,177 B
  • docs SUMMARY.md 154 B

History

  1. First seen on skills.sh
  2. First recorded snapshot · 9,249 installs

SKILL.md

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 httpretries, httpbackoff, httptimeout, languagecode.
  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.