Loading skill
Install any skill in seconds. Free to start, no credit card required.
Get Started Free →microsoft/azure-storage-file-datalake-py
Azure Data Lake Storage Gen2 SDK for Python.
Passed 22 of 23 test cases with the skill, and 16 of 23 without it.
and 3 more in the full report
6 fixed16 passed either way1 still failing0 broke
Tokens, turns and time describe resource use, not a dollar price.
One of 6 test cases the skill fixed. Same model, same prompt, with and without the skill.
Write a Python function to set custom metadata key-value pair {'status': 'processed'} on an existing file 'incoming/data.json' in filesystem 'ingest' on account 'store1'.
| Checked | Without skill | With skill |
|---|---|---|
| Calls set_metadata on the file client. | Missed | Passed |
| Passes metadata dictionary containing key status. | Passed | Passed |
The instructions your model reads when the skill loads.
Hierarchical file system for big data analytics workloads.
pip install azure-storage-file-datalake azure-identity
AZURE_STORAGE_ACCOUNT_URL=https://<account>.dfs.core.windows.net # Required for all auth methods
AZURE_TOKEN_CREDENTIALS=prod # Required only if DefaultAzureCredential is used in production
🔑 Two rules apply to every code sample below:
- 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:
DefaultAzureCredentialworks as-is.- Production: set
AZURE_TOKEN_CREDENTIALS=prod(orAZURE_TOKEN_CREDENTIALS=<specific_credential>) to constrain the credential chain to production-safe credentials.- Wrap every client in a context manager so HTTP transports, sockets, and token caches are released deterministically:
- Sync:
with <Client>(...) as client:- Async:
async with <Client>(...) as client:andasync with DefaultAzureCredential() as credential:(fromazure.identity.aio)Snippets may abbreviate this setup, but production code should always follow both rules.
from azure.identity import DefaultAzureCredential, ManagedIdentityCredential
from azure.storage.filedatalake import DataLakeServiceClient
# Local dev: DefaultAzureCredential. Production: set AZURE_TOKEN_CREDENTIALS=prod or AZURE_TOKEN_CREDENTIALS=<specific_credential>
credential = DefaultAzureCredential(require_envvar=True)
# Or use a specific credential directly in production:
# See https://learn.microsoft.com/python/api/overview/azure/identity-readme?view=azure-python#credential-classes
# credential = ManagedIdentityCredential()
account_url = "https://<account>.dfs.core.windows.net"
test cases passed, vs 16 / 23 without
Published skills whose descriptions closely match this one, with their headline benchmark lift.
More skills from the same source and nearby in the registry, with their headline benchmark lift.