azure-storage-file-datalake-py Skill
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace". Published by microsoft in skills.
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Security review, Data analysis, Includes SKILL.md, Reusable instructions
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2 months ago · MIT license
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What is azure-storage-file-datalake-py Skill?
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace". Published by microsoft in skills. This profile combines repository metadata with install, compatibility, and usage signals so developers can quickly decide whether it fits their agent workflow before opening the source repository.
Automated repository signals based on public metadata such as recency, license, installation evidence, and adoption. These are not a security audit or endorsement. See how SkillIndex evaluates profiles.
Key capabilities
- Includes SKILL.md support
- Reusable instructions support
- Security review
- Data analysis
- Security review use cases
- Data analysis use cases
Declared skill metadata
- Declared author: Microsoft
- Declared license: MIT
- Source file: .github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md
These fields retain source and confidence evidence from the indexed SKILL.md.
Compatibility and setup
- Install or run with Copy skill directory
When to use azure-storage-file-datalake-py Skill
- Use it for security review.
- Use it for data analysis.
Built with
Editorial notes
Source
- Creator: microsoft
- Repository: microsoft/skills
- Skill file: .github/plugins/azure-sdk-python/skills/azure-storage-file-datalake-py/SKILL.md
What it does
Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations. Triggers: "data lake", "DataLakeServiceClient", "FileSystemClient", "ADLS Gen2", "hierarchical namespace".
Skill instructions
Azure Data Lake Storage Gen2 SDK for Python Hierarchical file system for big data analytics workloads. Installation bash pip install azure-storage-file-datalake azure-identity Environment Variables bash AZURESTORAGEACCOUNTURL=https://<account.dfs.core.windows.net Required for all auth methods AZURETOKENCREDENTIALS=prod Required only if DefaultAzureCredential is used in production Authentication & Lifecycle 🔑 Two rules apply to every code sample below: 1. Prefer DefaultAzureCredential. It works locally (Azure CLI / VS Code / Developer CLI) and in Azure (managed identity, workload identity) with no code change. Avoid connection strings, account/API keys — they bypass Entra audit and rotation. - Local dev: DefaultAzureCredential works as-is. - Production: set AZURETOKENCREDENTIALS=prod (or AZURETOKENCREDENTIALS=<specificcredential) to constrain the credential chain to production-safe credentials. 2. Wrap every client in a context manager so HTTP transports, sockets, and token caches are
Verified compatibility and discovery
Frequently asked questions
What is azure-storage-file-datalake-py?
azure-storage-file-datalake-py is a open-source AI agent skill with Copy skill directory. Azure Data Lake Storage Gen2 SDK for Python. Use for hierarchical file systems, big data analytics, and file/directory operations.
Who is azure-storage-file-datalake-py best for?
azure-storage-file-datalake-py is best for reusing agent instructions, scripts, and references, security review workflows, data analysis workflows.
How do I install azure-storage-file-datalake-py?
Install or run azure-storage-file-datalake-py using Copy skill directory. Check azure-storage-file-datalake-py for the latest setup command.
Is azure-storage-file-datalake-py actively maintained?
azure-storage-file-datalake-py may need a closer maintenance check before production use.
Project health auto-fetched from the source repository.
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Review this source-backed profile, send a correction with evidence, or link to it from your documentation. Claims verify your relationship to the project; profile facts still require source evidence and editorial review.
