Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add benjaminasterA/antigravity-awesome-skills --skill azure-storage-file-datalake-pygit clone --depth 1 https://github.com/benjaminasterA/antigravity-awesome-skillsWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-storage-file-datalake-py)<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-storage-file-datalake-py"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-storage-file-datalake-py/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/benjaminastera/antigravity-awesome-skills/azure-storage-file-datalake-py"><img src="https://agentmods.dev/badge/skills/benjaminastera/antigravity-awesome-skills/azure-storage-file-datalake-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00000 | $0.01254 |
| Opus 5 | $0.00000 | $0.00627 |
| Sonnet 5 | $0.00000 | $0.00251 |
| Haiku 4.5 | $0.00000 | $0.00125 |
Grade A, and why
azure-storage-file-datalake-py scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 8d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 217 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Data Lake Storage Gen2 SDK for Python
Hierarchical file system for big data analytics workloads.
Installation
pip install azure-storage-file-datalake azure-identity
Environment Variables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.dfs.core.windows.net
Authentication
from azure.identity import DefaultAzureCredential
from azure.storage.filedatalake import DataLakeServiceClient
credential = DefaultAzureCredential()
account_url = "https://<account>.dfs.core.windows.net"
service_client = DataLakeServiceClient(account_url=account_url, credential=credential)
Client Hierarchy
| Client | Purpose |
|---|---|
DataLakeServiceClient |
Account-level operations |
FileSystemClient |
Container (file system) operations |
DataLakeDirectoryClient |
Directory operations |
DataLakeFileClient |
File operations |
File System Operations
# Create file system (container)
file_system_client = service_client.create_file_system("myfilesystem")
# Get existing
file_system_client = service_client.get_file_system_client("myfilesystem")
# Delete
service_client.delete_file_system("myfilesystem")
# List file systems
for fs in service_client.list_file_systems():
print(fs.name)
Directory Operations
file_system_client = service_client.get_file_system_client("myfilesystem")
# Create directory
directory_client = file_system_client.create_directory("mydir")
# Create nested directories
directory_client = file_system_client.create_directory("path/to/nested/dir")
# Get directory client
directory_client = file_system_client.get_directory_client("mydir")
# Delete directory
directory_client.delete_directory()
# Rename/move directory
directory_client.rename_directory(new_name="myfilesystem/newname")
File Operations
Upload File
# Get file client
file_client = file_system_client.get_file_client("path/to/file.txt")
# Upload from local file
with open("local-file.txt", "rb") as data:
file_client.upload_data(data, overwrite=True)
# Upload bytes
file_client.upload_data(b"Hello, Data Lake!", overwrite=True)
# Append data (for large files)
file_client.append_data(data=b"chunk1", offset=0, length=6)
file_client.append_data(data=b"chunk2", offset=6, length=6)
file_client.flush_data(12) # Commit the data
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 8d ago First seen · 217 lines · 0 tokens per session scan A af479ae07051
azure-storage-file-datalake-py is a skill published in the GitHub repository benjaminasterA/antigravity-awesome-skills (264 stars, last pushed yesterday), licensed MIT. It costs nothing until one of its globs matches a file; then it loads 1,254 tokens. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-09-03.
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