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Provides expert guidance on Azure Open Datasets limits and quotas, including non-Spark download handling, throttling, retry logic, and rate-limit workarounds.
How this skill is triggered — by the user, by Claude, or both
Slash command
/azure-agent-skills:azure-open-datasetsThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
This skill provides expert guidance for Azure Open Datasets. Covers limits & quotas. It combines local quick-reference content with remote documentation fetching capabilities.
This skill provides expert guidance for Azure Open Datasets. Covers limits & quotas. It combines local quick-reference content with remote documentation fetching capabilities.
IMPORTANT for Agent: Use the Category Index below to locate relevant sections. For categories with line ranges (e.g.,
L35-L120), useread_filewith the specified lines. For categories with file links (e.g.,[security.md](security.md)), useread_fileon the linked reference file
IMPORTANT for Agent: If
metadata.generated_atis more than 3 months old, suggest the user pull the latest version from the repository. Ifmcp_microsoftdocstools are not available, suggest the user install it: Installation Guide
This skill requires network access to fetch documentation content:
mcp_microsoftdocs:microsoft_docs_fetch with query string from=learn-agent-skill. Returns Markdown.fetch_webpage with query string from=learn-agent-skill&accept=text/markdown. Returns Markdown.| Category | Lines | Description |
|---|---|---|
| Limits & Quotas | L29-L32 | Managing and troubleshooting non-Spark download limits for Azure Open Datasets, including throttling behavior, quotas, and strategies to avoid or handle rate limits |
| Topic | URL |
|---|---|
| Handle Azure Open Datasets non-Spark download limits | https://learn.microsoft.com/en-us/azure/open-datasets/samples |
npx claudepluginhub microsoftdocs/agent-skills --plugin azure-agent-skillsGuides Azure Data Share development: troubleshooting invitations, estimating costs, configuring RBAC/security, cross-region deployment, dataset mapping, and automation with PowerShell/ARM/Bicep.
Guides on Azure Data Factory validation rules including activity nesting limitations, ForEach restrictions, pipeline validation, linked service authentication, resource limits, Set Variable rules, and Data Flow constraints.
Analyze lakehouse data interactively via Fabric Lakehouse Livy API sessions using PySpark/Spark SQL for DataFrames, cross-lakehouse joins, Delta time-travel, and unstructured/JSON data.