skills/codex/azure-messaging-webpubsubservice-py/SKILL.md
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: azure-messaging-webpubsubservice-py description: "Azure Web PubSub Service SDK for Python" --- # Azure Web PubSub Service SDK for Python Real-time messaging with WebSocket connections at scale. ## Installation ```bash # Service SDK (server-side) pip install azure-messaging-webpubsubservice # Client SDK (for Python WebSocket clients) pip install azure-messaging-webpubsubclient ``` ## Environment Variables ```bash AZURE_WEB
npx skillsauth add frank-luongt/faos-skills-marketplace skills/codex/azure-messaging-webpubsubservice-pyInstall this skill globally with one command. Works with Claude Code, Cursor, and Windsurf.
3 of 9 scanners reported clean
Some scanners were skipped, did not run, or reported a non-clean status. Review each row below.
Real-time messaging with WebSocket connections at scale.
# Service SDK (server-side)
pip install azure-messaging-webpubsubservice
# Client SDK (for Python WebSocket clients)
pip install azure-messaging-webpubsubclient
AZURE_WEBPUBSUB_CONNECTION_STRING=Endpoint=https://<name>.webpubsub.azure.com;AccessKey=...
AZURE_WEBPUBSUB_HUB=my-hub
from azure.messaging.webpubsubservice import WebPubSubServiceClient
# Connection string
client = WebPubSubServiceClient.from_connection_string(
connection_string=os.environ["AZURE_WEBPUBSUB_CONNECTION_STRING"],
hub="my-hub"
)
# Entra ID
from azure.identity import DefaultAzureCredential
client = WebPubSubServiceClient(
endpoint="https://<name>.webpubsub.azure.com",
hub="my-hub",
credential=DefaultAzureCredential()
)
# Token for anonymous user
token = client.get_client_access_token()
print(f"URL: {token['url']}")
# Token with user ID
token = client.get_client_access_token(
user_id="user123",
roles=["webpubsub.sendToGroup", "webpubsub.joinLeaveGroup"]
)
# Token with groups
token = client.get_client_access_token(
user_id="user123",
groups=["group1", "group2"]
)
# Send text
client.send_to_all(message="Hello everyone!", content_type="text/plain")
# Send JSON
client.send_to_all(
message={"type": "notification", "data": "Hello"},
content_type="application/json"
)
client.send_to_user(
user_id="user123",
message="Hello user!",
content_type="text/plain"
)
client.send_to_group(
group="my-group",
message="Hello group!",
content_type="text/plain"
)
client.send_to_connection(
connection_id="abc123",
message="Hello connection!",
content_type="text/plain"
)
# Add user to group
client.add_user_to_group(group="my-group", user_id="user123")
# Remove user from group
client.remove_user_from_group(group="my-group", user_id="user123")
# Add connection to group
client.add_connection_to_group(group="my-group", connection_id="abc123")
# Remove connection from group
client.remove_connection_from_group(group="my-group", connection_id="abc123")
# Check if connection exists
exists = client.connection_exists(connection_id="abc123")
# Check if user has connections
exists = client.user_exists(user_id="user123")
# Check if group has connections
exists = client.group_exists(group="my-group")
# Close connection
client.close_connection(connection_id="abc123", reason="Session ended")
# Close all connections for user
client.close_all_connections(user_id="user123")
from azure.messaging.webpubsubservice import WebPubSubServiceClient
# Grant permission
client.grant_permission(
permission="joinLeaveGroup",
connection_id="abc123",
target_name="my-group"
)
# Revoke permission
client.revoke_permission(
permission="joinLeaveGroup",
connection_id="abc123",
target_name="my-group"
)
# Check permission
has_permission = client.check_permission(
permission="joinLeaveGroup",
connection_id="abc123",
target_name="my-group"
)
from azure.messaging.webpubsubclient import WebPubSubClient
client = WebPubSubClient(credential=token["url"])
# Event handlers
@client.on("connected")
def on_connected(e):
print(f"Connected: {e.connection_id}")
@client.on("server-message")
def on_message(e):
print(f"Message: {e.data}")
@client.on("group-message")
def on_group_message(e):
print(f"Group {e.group}: {e.data}")
# Connect and send
client.open()
client.send_to_group("my-group", "Hello from Python!")
from azure.messaging.webpubsubservice.aio import WebPubSubServiceClient
from azure.identity.aio import DefaultAzureCredential
async def broadcast():
credential = DefaultAzureCredential()
client = WebPubSubServiceClient(
endpoint="https://<name>.webpubsub.azure.com",
hub="my-hub",
credential=credential
)
await client.send_to_all("Hello async!", content_type="text/plain")
await client.close()
await credential.close()
| Operation | Description |
|-----------|-------------|
| get_client_access_token | Generate WebSocket connection URL |
| send_to_all | Broadcast to all connections |
| send_to_user | Send to specific user |
| send_to_group | Send to group members |
| send_to_connection | Send to specific connection |
| add_user_to_group | Add user to group |
| remove_user_from_group | Remove user from group |
| close_connection | Disconnect client |
| connection_exists | Check connection status |
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-mlflow-evaluation --- # MLflow 3 GenAI Evaluation ## Before Writing Any Code 1. **Read GOTCHAS.md** - 15+ common mistakes that cause failures 2. **Read CRITICAL-interfaces.md** - Exact API signatures and data schemas ## End-to-End Workflows Follow these workflows based on your goal. Each step indicates which reference files to read. ### Workflow 1: First-Time Evaluation Setup For users new to MLflow GenAI evalu
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-lakebase-provisioned --- # Lakebase Provisioned Patterns and best practices for using Lakebase Provisioned (Databricks managed PostgreSQL) for OLTP workloads. ## When to Use Use this skill when: - Building applications that need a PostgreSQL database for transactional workloads - Adding persistent state to Databricks Apps - Implementing reverse ETL from Delta Lake to an operational database - Storing chat/agent m
tools
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-jobs --- # Databricks Lakeflow Jobs ## Overview Databricks Jobs orchestrate data workflows with multi-task DAGs, flexible triggers, and comprehensive monitoring. Jobs support diverse task types and can be managed via Python SDK, CLI, or Asset Bundles. ## Reference Files | Use Case | Reference File | | ----------------------
development
<!-- AUTO-GENERATED by export-skills.py — DO NOT EDIT --> --- name: databricks-genie --- # Databricks Genie Create and query Databricks Genie Spaces - natural language interfaces for SQL-based data exploration. ## Overview Genie Spaces allow users to ask natural language questions about structured data in Unity Catalog. The system translates questions into SQL queries, executes them on a SQL warehouse, and presents results conversationally. ## When to Use This Skill Use this skill when: -