Datadog

Datadog API Skill

Overview

The Datadog API allows you to submit metrics, events, and manage dashboards programmatically. This skill focuses on the @datadog/datadog-api-client library.

Installation

npm install @datadog/datadog-api-client
pip install datadog-api-client

Authentication

Requires two headers: DD-API-KEY (for submitting data) and DD-APPLICATION-KEY (for reading data or managing configuration).

Core Concepts

• Metric: Time-series data points.
• Tags: Key:Value pairs attached to metrics (crucial for filtering).
• Monitor: An alert configured to trigger on thresholds.

Common Workflows

1. Instantiate the API client.
2. Construct a Series object with points and tags.
3. Call metricsApi.submitMetrics.

Error Handling

Catch HTTP 403 (Invalid Keys) and HTTP 429. If submitting metrics fails, log the response.body.errors array.

Security

API keys have agent-level permissions. Application keys should be scoped strictly (e.g., dashboards_read).

Rate Limits

Submit Metrics endpoint allows 100 requests per 10 seconds. Use batching heavily.

Best Practices

Batch metrics before sending. Never submit a single data point per API call in high-throughput environments.

Troubleshooting

If metrics don't appear, ensure the UNIX timestamp attached to your points is in seconds, not milliseconds.

References

• API Reference

Why use this skill

Use this when your agent works with datadog — structured patterns beat pasted docs and prevent common hallucinations.

AI pitfalls

• Using outdated SDK or API versions from training data
• Inventing environment variable names
• Omitting error handling and retry logic

Production checklist

• Secrets in environment variables, not source code
• Error handling and logging in place
• Rate limits and timeouts configured

Related skills

• No graph relationships yet — see the knowledge graph in the docs site.

Last Verified: 2026-07-02