By name: datadog-api-alerts description: Implements alert management using the Datadog API, focusing on creating effective alerts that respond to service conditions with best practices. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: datadog alerts, create alerts, manage alerts, alert conditions, datadog API alerts role: implementation scope: implementation output-format: code related-skills: datadog-api-monitors, datadog-api-dashboards, datadog-api-logs archetypes: [monitoring, implementation] anti_triggers: [alert fatigue, generic monitoring] response_profile: verbosity: medium directive_strength: high
Datadog Alerts Management
Implements alert creation and management leveraging the Datadog API. This skill ensures alerts are configured properly to monitor critical service health metrics and respond appropriately to incidents.
TL;DR Checklist
- Configure alerts based on specific metrics and thresholds that reflect critical user events.
- Utilize notification chains in alerts for proper escalations and communications.
- Implement tests to ensure alerts engage as expected under various scenarios.
Core Workflow
Define Alert Parameters: Create a clear specification of what conditions trigger alerts (e.g., error rates, latency) Checkpoint: All alert conditions must meet the defined thresholds based on service metrics.
Implement Notification Settings: Configure who should receive alerts based on urgency, including integrations like Slack and PagerDuty. Checkpoint: Every notification is tracked to verify effectiveness.
Create Alerts via API: Leverage the API to create alerts with validated condition parameters. Checkpoint: Confirm successful creation and handle any errors appropriately.
Implementation Patterns
Pattern 1: Creating an Alert
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.alerts_api import AlertsApi
from datadog_api_client.v2.model.alert import Alert
import os
class AlertsManager:
def __init__(self):
self.configuration = Configuration(api_key={"apiKeyAuth": os.environ["DD_API_KEY"]},
server_variables={"site": os.environ.get("DD_SITE", "datadoghq.com")})
self.api_client = ApiClient(self.configuration)
self.alerts_api = AlertsApi(self.api_client)
def create_alert(self, name: str, query: str, message: str) -> int:
alert = Alert(name=name, query=query, message=message)
response = self.alerts_api.create_alert(alert)
return response.id
Pattern 2: Updating an Existing Alert
def update_alert(alert_id: int, new_message: str):
alert = self.alerts_api.get_alert(alert_id)
alert.message = new_message
self.alerts_api.update_alert(alert_id, alert)
Constraints
MUST DO
- Implement clear and actionable alert messages to avoid confusion during incidents.
- Ensure all alerts are validated after creation to confirm they engage correctly under defined conditions.
MUST NOT DO
- Avoid vague alert conditions that could lead to alert fatigue.
- Never ignore the need for historical performance metrics in defining alert thresholds.
Live References
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