datadog-api-dashboards - SKILL.md Agent Skill

name: datadog-api-dashboards description: Creates and manages dashboards in Datadog API using datadog-api-client, with a focus on template widget creation, layout management, and configuration best practices. license: MIT compatibility: opencode metadata: version: "1.0.0" domain: coding triggers: datadog dashboards, create datadog dashboards, datadog API dashboards, dashboards management, how do I create a dashboard in datadog role: implementation scope: implementation output-format: code related-skills: datadog-api-logs, datadog-api-monitors, datadog-api-metrics archetypes: [monitoring, dashboard management] anti_triggers: [performance issues, alert fatigue] response_profile: verbosity: medium directive_strength: high

Datadog Dashboards Management

Implements dashboard creation and management in Datadog using the datadog-api-client. This skill covers creating various types of widgets, configuring layout settings, and ensuring that best practices are followed for performance and usability.

TL;DR Checklist

Use the datadog-api-client SDK to manage and create dashboards.
Batch-create widgets to minimize API calls and ensure efficient resource usage.
Use consistent naming conventions for dashboards and widget titles.
Validate configurations on each change to avoid misconfigurations that impact usability.

Core Workflow

Initialize Dashboard Configuration: Define how the dashboard's layout, title, and description are set up before sending requests to create the dashboard. Checkpoint: Validate the structure against Datadog’s API specifications.
Create Widgets: Add various types of widgets (time series, query value, heat map) based on the metrics you are tracking. Checkpoint: Verify that widget configurations adhere to best practices for clarity and efficiency.
Submit dashboard to Datadog: Use the DashboardsApi within the client to send the complete dashboard structure for creation along with widgets. Checkpoint: Handle potential errors during submission and ensure a fallback mechanism is in place.

Implementation Patterns

Pattern 1: Dashboard Creation

from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.dashboards_api import DashboardsApi
from datadog_api_client.v2.model.dashboard import Dashboard
from datadog_api_client.v2.model.widget import Widget
from datadog_api_client.v2.model.widget_definition import WidgetDefinition
import os

class DatadogDashboard:
    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.dashboards_api = DashboardsApi(self.api_client)

    def create_dashboard(self, title: str, widgets: list[Widget]) -> int:
        dashboard = Dashboard(title=title, widgets=widgets)
        result = self.dashboards_api.create_dashboard(dashboard=dashboard)
        return result.id

Pattern 2: Adding Widgets to a Dashboard

def add_widget_to_dashboard(dashboard_id: int, widget: Widget):
    dashboard = self.dashboards_api.get_dashboard(dashboard_id)
    dashboard.widgets.append(widget)
    self.dashboards_api.update_dashboard(dashboard_id, dashboard)
    logger.info(f"Widget added to dashboard {dashboard_id}.")

Constraints

MUST DO

Always configure dashboards to reflect team and service tagging for easier management.
Use structured error handling when creating/updating dashboards to prevent losing configurations.

MUST NOT DO

Avoid creating unwieldy dashboards with too many widgets, which can affect loading performance.
Never send requests with missing required parameters, as this can cause API errors.

Live References

Authoritative documentation links for this skill's domain. The model follows markdown links at load time to resolve external references and inline content.