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 DorianGallo/antigravity-awesome-skills-local --skill azure-data-tables-pygit clone --depth 1 https://github.com/DorianGallo/antigravity-awesome-skills-localWrote 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/doriangallo/antigravity-awesome-skills-local/azure-data-tables-py)<a href="https://agentmods.dev/skills/doriangallo/antigravity-awesome-skills-local/azure-data-tables-py"><img src="https://agentmods.dev/badge/skills/doriangallo/antigravity-awesome-skills-local/azure-data-tables-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/doriangallo/antigravity-awesome-skills-local/azure-data-tables-py"><img src="https://agentmods.dev/badge/skills/doriangallo/antigravity-awesome-skills-local/azure-data-tables-py.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00033 | $0.01395 |
| Opus 5 | $0.00016 | $0.00698 |
| Sonnet 5 | $0.00007 | $0.00279 |
| Haiku 4.5 | $0.00003 | $0.00139 |
Grade A, and why
azure-data-tables-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.
This is a copy
78% identical to azure-data-tables-py — 11 lines differ, which has more behind it and is treated as the original. This page carries a canonical link to it rather than competing with it.
How it starts
The opening of the file, as written. The whole thing — 252 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Tables SDK for Python
NoSQL key-value store for structured data (Azure Storage Tables or Cosmos DB Table API).
Installation
pip install azure-data-tables azure-identity
Environment Variables
# Azure Storage Tables
AZURE_STORAGE_ACCOUNT_URL=https://<account>.table.core.windows.net
# Cosmos DB Table API
COSMOS_TABLE_ENDPOINT=https://<account>.table.cosmos.azure.com
Authentication
from azure.identity import DefaultAzureCredential
from azure.data.tables import TableServiceClient, TableClient
credential = DefaultAzureCredential()
endpoint = "https://<account>.table.core.windows.net"
# Service client (manage tables)
service_client = TableServiceClient(endpoint=endpoint, credential=credential)
# Table client (work with entities)
table_client = TableClient(endpoint=endpoint, table_name="mytable", credential=credential)
Client Types
| Client | Purpose |
|---|---|
TableServiceClient |
Create/delete tables, list tables |
TableClient |
Entity CRUD, queries |
Table Operations
# Create table
service_client.create_table("mytable")
# Create if not exists
service_client.create_table_if_not_exists("mytable")
# Delete table
service_client.delete_table("mytable")
# List tables
for table in service_client.list_tables():
print(table.name)
# Get table client
table_client = service_client.get_table_client("mytable")
Entity Operations
Important: Every entity requires PartitionKey and RowKey (together form unique ID).
Create Entity
entity = {
"PartitionKey": "sales",
"RowKey": "order-001",
"product": "Widget",
"quantity": 5,
"price": 9.99,
"shipped": False
}
# Create (fails if exists)
table_client.create_entity(entity=entity)
# Upsert (create or replace)
table_client.upsert_entity(entity=entity)
Get Entity
# Get by key (fastest)
entity = table_client.get_entity(
partition_key="sales",
row_key="order-001"
)
print(f"Product: {entity['product']}")
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 · 252 lines · 33 tokens per session scan A 4ee24ac24f9d
azure-data-tables-py is a skill published in the GitHub repository DorianGallo/antigravity-awesome-skills-local (2 stars, last pushed yesterday), licensed MIT. It adds 33 tokens to every session and 1,395 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. It is 78% identical to azure-data-tables-py, differing in 11 lines, and is treated as a copy.
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