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 lucaspmarie-a11y/claude-skills-vault --skill azure-mgmt-fabric-pygit clone --depth 1 https://github.com/lucaspmarie-a11y/claude-skills-vaultWrote 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/lucaspmarie-a11y/claude-skills-vault/azure-mgmt-fabric-py)<a href="https://agentmods.dev/skills/lucaspmarie-a11y/claude-skills-vault/azure-mgmt-fabric-py"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/azure-mgmt-fabric-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/lucaspmarie-a11y/claude-skills-vault/azure-mgmt-fabric-py"><img src="https://agentmods.dev/badge/skills/lucaspmarie-a11y/claude-skills-vault/azure-mgmt-fabric-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 | $0.00024 | $0.01482 |
| Opus 5.5 | $0.00010 | $0.00593 |
| Sonnet 5.5 | $0.00005 | $0.00296 |
| Haiku 4.5 | $0.00002 | $0.00148 |
Grade A, and why
azure-mgmt-fabric-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 1mo 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
86% identical to azure-mgmt-fabric-py — 5 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 — 263 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Azure Fabric Management SDK for Python
Manage Microsoft Fabric capacities and resources programmatically.
Installation
pip install azure-mgmt-fabric
pip install azure-identity
Environment Variables
AZURE_SUBSCRIPTION_ID=<your-subscription-id>
AZURE_RESOURCE_GROUP=<your-resource-group>
Authentication
from azure.identity import DefaultAzureCredential
from azure.mgmt.fabric import FabricMgmtClient
import os
credential = DefaultAzureCredential()
client = FabricMgmtClient(
credential=credential,
subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
)
Create Fabric Capacity
from azure.mgmt.fabric import FabricMgmtClient
from azure.mgmt.fabric.models import FabricCapacity, FabricCapacityProperties, CapacitySku
from azure.identity import DefaultAzureCredential
import os
credential = DefaultAzureCredential()
client = FabricMgmtClient(
credential=credential,
subscription_id=os.environ["AZURE_SUBSCRIPTION_ID"]
)
resource_group = os.environ["AZURE_RESOURCE_GROUP"]
capacity_name = "myfabriccapacity"
capacity = client.fabric_capacities.begin_create_or_update(
resource_group_name=resource_group,
capacity_name=capacity_name,
resource=FabricCapacity(
location="eastus",
sku=CapacitySku(
name="F2", # Fabric SKU
tier="Fabric"
),
properties=FabricCapacityProperties(
administration=FabricCapacityAdministration(
members=["[email protected]"]
)
)
)
).result()
print(f"Capacity created: {capacity.name}")
Get Capacity Details
capacity = client.fabric_capacities.get(
resource_group_name=resource_group,
capacity_name=capacity_name
)
print(f"Capacity: {capacity.name}")
print(f"SKU: {capacity.sku.name}")
print(f"State: {capacity.properties.state}")
print(f"Location: {capacity.location}")
List Capacities in Resource Group
capacities = client.fabric_capacities.list_by_resource_group(
resource_group_name=resource_group
)
for capacity in capacities:
print(f"Capacity: {capacity.name} - SKU: {capacity.sku.name}")
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.
- 1mo ago First seen · 263 lines · 24 tokens per session scan A 15869f7fae02
azure-mgmt-fabric-py is a skill published in the GitHub repository lucaspmarie-a11y/claude-skills-vault (5 stars, last pushed 4mo ago), licensed MIT. It adds 24 tokens to every session and 1,482 once invoked, about $0.0001 per session on Opus 5.5. A static security scan graded it A with 0 findings. It is 86% identical to azure-mgmt-fabric-py, differing in 5 lines, and is treated as a copy.
Other skills, from other repositories
matlab
Builds, reviews, migrates, and plans MATLAB or GNU Octave numerical workflows. Use for arrays, tabular/time data, tests, projects, graphics, MAT files, and explicit Python interoperability.
pennylane
Builds and differentiates PennyLane quantum circuits, hybrid PyTorch or JAX models, molecular VQE and QAOA workflows. Use for variational quantum algorithms, quantum machine learning, simulator validation, and moving validated circuits to provider plugins. For hardware-specific compilation use qiskit or cirq; for…
holoscan-install-wheel
Install Holoscan SDK 5.x CUDA 13 Python wheels in an isolated environment, choosing the full stack or version-matched components.
rocm-kernels
Provides guidance for writing and benchmarking optimized Triton kernels for AMD GPUs (MI355X, R9700) on ROCm, targeting HuggingFace diffusers (LTX-Video, SD3, FLUX) and transformers. Core kernels: RMSNorm, RoPE 3D, GEGLU, AdaLN. Includes XCD swizzle, autotune, diffusers integration patterns, and LTX-Video pipeline…
typing-exclusion-worker
Python typing exclusion worker: remove assigned mypy exclusion modules in small scoped batches, fix typing issues, run validation, and produce a structured completion summary. Use when running parallel typing-debt workers or when asked to remove modules from pyproject mypy exclusion overrides.
python
Python development with ruff, mypy, pytest - TDD and type safety.