Datadog Agent

Skill: datadog-agent

Expert-level guidance for installing, configuring, and extending the Datadog Agent, including Ansible orchestration and custom OpenMetrics checks.

When to Use

• When configuring, installing, or troubleshooting the Datadog Agent on a Linux host.
• To write custom Python check scripts (AgentCheck or OpenMetricsBaseCheckV2) for the agent.
• When updating Ansible playbooks that deploy the datadog.dd.agent role.

When Not to Use

• For querying Datadog APIs to read telemetry or dashboard data (use datadog-api instead).
• When managing Datadog SaaS configurations like Monitors or SLOs (use datadog-monitors).
• If you are deploying Datadog strictly via a Helm chart in Kubernetes without needing custom host-level Python checks.

Common Pitfalls

• Subprocess Hangs: Using Python's native subprocess module inside a custom check instead of get_subprocess_output(), which deadlocks the Agent's Go-runtime.
• Misnamed Files: Creating a custom check script named my_check.py but naming the configuration file custom_check.yaml, causing the Agent to silently ignore it.
• Root Level Logs: Defining - type: file at the root of a check's YAML configuration instead of properly nesting it under a logs: key.

Core Principles

• Agent Immutability: Prefer immutable configuration deployments (e.g., Ansible) over manual modifications to datadog.yaml or conf.d/.
• API Key Handling: Never hardcode datadog_api_key in playbooks; utilize vault secrets or environment variables.
• Python Compatibility: Custom Agent checks must be Python 3 compatible for modern Agent versions (v7+).
• File Matching: The custom check script name (e.g., my_check.py) must exactly match its configuration file name (e.g., my_check.yaml).

Ansible Integration & Logs Configuration

When using the datadog.dd.agent (or legacy datadog.datadog) Ansible role, ensure correct YAML hierarchy, especially for log collection.

• Requirements: Ansible v2.10+, ansible-galaxy collection install datadog.dd
• Avoid Array Root: Do not map a check directly to an array. A common mistake with logs is defining - type: file at the root check level.
• Correct Logs Structure: Logs must be encapsulated under a logs: key within the specific check dictionary.

# Correct Ansible definition for logs attached to a custom/syslog check
datadog_checks:
  syslog: # Creates /etc/datadog-agent/conf.d/syslog.d/conf.yaml
    logs:
      - type: file
        path: /var/log/syslog
        service: syslog
        source: syslog

Custom Agent Checks

Create custom Python checks by extending AgentCheck for simple metrics or OpenMetricsBaseCheckV2 for Prometheus endpoints.

• Directory Paths:
• Scripts: /etc/datadog-agent/checks.d/
• Configs: /etc/datadog-agent/conf.d/.d/conf.yaml
• Naming Convention: Prefix custom checks with custom_ (e.g., custom_postfix.py) to avoid conflicts with out-of-the-box integrations.
• Check Verification: sudo -u dd-agent -- datadog-agent check

Simple Python Check

from datadog_checks.base import AgentCheck

class CustomCheck(AgentCheck):
    def check(self, instance):
        self.gauge('custom.metric', 1, tags=instance.get('tags', []))

OpenMetrics Base Check (V2)

Advanced scraping from Prometheus endpoints. Requires openmetrics_endpoint in the check config.

from datadog_checks.base import OpenMetricsBaseCheckV2, ConfigurationError

class CustomOpenMetricsCheck(OpenMetricsBaseCheckV2):
    __NAMESPACE__ = "my_namespace"

    def __init__(self, name, init_config, instances):
        super(CustomOpenMetricsCheck, self).__init__(name, init_config, instances)
        self.metrics_map = {
            'prom_metric_name': 'datadog.metric.name',
        }

    def get_default_config(self):
        return {'metrics': self.metrics_map}

    def check(self, instance):
        endpoint = instance.get('openmetrics_endpoint')
        if not endpoint:
            raise ConfigurationError("Missing 'openmetrics_endpoint'")
        super().check(instance)

Integrations & Developer Platform

• Tiles & Dashboards: Official integrations must include out-of-the-box dashboards and telemetry logic. API integrations require OAuth 2.0.
• Platform Separation: Agent-based integrations collect from local hosts, whereas API-based integrations ingest directly via Datadog REST endpoints.

Diagnostics and Troubleshooting

• Check agent status: datadog-agent status
• Subprocess execution: Never use Python's native subprocess module due to the Agent's Go-runtime multithreading constraints. Always use get_subprocess_output() from datadog_checks.base.utils.subprocess_output.

from datadog_checks.base.utils.subprocess_output import get_subprocess_output
out, err, retcode = get_subprocess_output(["ls", "."], self.log, raise_on_empty_output=True)

Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

• Author: Cogni-AI-OU
• Source: Cogni-AI-OU/cogni-ai-agent-skills
• License: MIT

Install and usage instructions live in the source repository linked above.