Code Generation

Datadog Integration Skill

This skill helps users interact with Datadog through two complementary approaches:

1. Immediate execution using the pup CLI tool
2. Code generation for application integration using Datadog API clients

When to Use This Skill

Use this skill when the user:

• Wants to query Datadog data (logs, traces, metrics, etc.)
• Needs to configure Datadog (monitors, dashboards, SLOs, etc.)
• Asks to "generate code" for a Datadog operation
• Wants to integrate Datadog operations into their application
• Needs examples of using Datadog API clients in a specific language

Pup CLI Tool

The pup CLI is a Go-based command-line wrapper for Datadog APIs. It provides:

• OAuth2 authentication (preferred) or API key authentication
• 28 command groups covering 33+ API domains
• JSON, YAML, and table output formats
• 200+ subcommands for comprehensive Datadog operations

Pup Authentication

# OAuth2 (preferred)
pup auth login

# API Keys (fallback)
export DD_API_KEY="your-api-key"
export DD_APP_KEY="your-app-key"
export DD_SITE="datadoghq.com"

Pup Command Structure

pup <domain> <action> [options]
pup <domain> <subgroup> <action> [options]

# Examples
pup monitors list --tag="env:prod"
pup logs search --query="status:error" --from="1h"
pup metrics query --query="avg:system.cpu.user{*}" --from="1h"

Supported Operations

Core Observability

• Metrics: Query, list, search, submit metrics
• Logs: Search and aggregate log data
• Traces: Query APM traces and spans
• Events: List and search events
• RUM: Real user monitoring data

Monitoring & Alerting

• Monitors: Full CRUD operations
• Dashboards: Create, list, get, delete
• SLOs: Service level objectives management
• Synthetics: Synthetic test management
• Downtimes: Monitor downtime management
• Notebooks: Investigation notebooks

Security & Compliance

• Security Monitoring: Rules, signals, findings
• Vulnerabilities: Security vulnerability scanning
• Static Analysis: Code security analysis
• Audit Logs: Organizational audit trail
• Data Governance: Sensitive data scanning

Infrastructure & Cloud

• Infrastructure: Host inventory and metrics
• Tags: Resource tagging
• Cloud Integrations: AWS, GCP, Azure

Incident & Operations

• Incidents: Incident management
• On-Call: On-call team management
• Error Tracking: Application error tracking
• Service Catalog: Service registry
• Scorecards: Service quality metrics

Organization & Access

• Users: User and role management
• Organizations: Org settings
• API Keys: API key management

See pup --help for complete command reference.

Usage Patterns

Pattern 1: Quick Query (Use Pup Directly)

When users want immediate results, execute pup commands:

# Query metrics
pup metrics query --query="avg:system.cpu.user{*}" --from="1h" --to="now"

# Search logs
pup logs search --query="status:error service:api" --from="30m"

# List monitors
pup monitors list --tag="team:backend"

# Get dashboard
pup dashboards get abc-123-def

Pattern 2: Code Generation (For Application Integration)

When users want to integrate into their application, provide code examples using official Datadog API clients.

TypeScript Example (using @datadog/datadog-api-client)

import { client, v2 } from '@datadog/datadog-api-client';

// Configure authentication
const configuration = client.createConfiguration({
  authMethods: {
    apiKeyAuth: process.env.DD_API_KEY || '',
    appKeyAuth: process.env.DD_APP_KEY || '',
  },
});

// Query metrics
async function queryMetrics() {
  const apiInstance = new v2.MetricsApi(configuration);

  try {
    const params: v2.MetricsApiQueryTimeseriesDataRequest = {
      body: {
        data: {
          type: 'timeseries_request',
          attributes: {
            formulas: [{
              formula: 'query1'
            }],
            queries: [{
              name: 'query1',
              dataSource: 'metrics',
              query: 'avg:system.cpu.user{*}'
            }],
            from: Date.now() - 3600000, // 1 hour ago
            to: Date.now()
          }
        }
      }
    };

    const result = await apiInstance.queryTimeseriesData(params);
    console.log(JSON.stringify(result, null, 2));
  } catch (error) {
    console.error('Error:', error);
  }
}

queryMetrics();

Installation: npm install @datadog/datadog-api-client

Python Example (using datadog-api-client)

#!/usr/bin/env python3
import os
from datetime import datetime, timedelta
from datadog_api_client import ApiClient, Configuration
from datadog_api_client.v2.api.metrics_api import MetricsApi
from datadog_api_client.v2.model.timeseries_formula_request import TimeseriesFormulaRequest
from datadog_api_client.v2.model.timeseries_formula_query_request import TimeseriesFormulaQueryRequest
from datadog_api_client.v2.model.timeseries_formula_request_attributes import TimeseriesFormulaRequestAttributes
from datadog_api_client.v2.model.timeseries_formula_request_type import TimeseriesFormulaRequestType

def configure_datadog():
    configuration = Configuration()
    configuration.api_key['apiKeyAuth'] = os.getenv('DD_API_KEY')
    configuration.api_key['appKeyAuth'] = os.getenv('DD_APP_KEY')
    configuration.server_variables['site'] = os.getenv('DD_SITE', 'datadoghq.com')
    return configuration

def query_metrics():
    configuration = configure_datadog()

    with ApiClient(configuration) as api_client:
        api_instance = MetricsApi(api_client)

        # Query parameters
        now = int(datetime.now().timestamp())
        - timedelta(hours=1)).timestamp())

        body = TimeseriesFormulaRequest(
            data=TimeseriesFormulaQueryRequest(
                type=TimeseriesFormulaRequestType.TIMESERIES_REQUEST,
                attributes=TimeseriesFormulaRequestAttributes(
                    formulas=[{"formula": "query1"}],
                    queries=[{
                        "name": "query1",
                        "data_source": "metrics",
                        "query": "avg:system.cpu.user{*}"
                    }],
                    _from=one_hour_ago,
                    to=now
                )
            )
        )

        try:
            result = api_instance.query_timeseries_data(body=body)
            print(result)
        except Exception as e:
            print(f"Error: {e}")

if __name__ == "__main__":
    query_metrics()

Installation: pip install datadog-api-client

Java Example (using com.datadoghq:datadog-api-client)

package com.datadog.api.example;

import com.datadog.api.client.ApiClient;
import com.datadog.api.client.ApiException;
import com.datadog.api.client.v2.api.MetricsApi;
import com.datadog.api.client.v2.model.*;
import java.time.Instant;
import java.time.temporal.ChronoUnit;
import java.util.Collections;

public class MetricsQueryExample {
    public static void main(String[] args) {
        // Validate environment variables
        String apiKey = System.getenv("DD_API_KEY");
        String appKey = System.getenv("DD_APP_KEY");
        String site = System.getenv().getOrDefault("DD_SITE", "datadoghq.com");

        if (apiKey == null || appKey == null) {
            System.err.println("Error: DD_API_KEY and DD_APP_KEY must be set");
            System.exit(1);
        }

        // Configure API client
        ApiClient apiClient = ApiClient.getDefaultApiClient();
        apiClient.setServerVariableValue("site", site);
        apiClient.configureApiKeys(Collections.singletonMap("apiKeyAuth", apiKey));
        apiClient.configureApiKeys(Collections.singletonMap("appKeyAuth", appKey));

        try {
            queryMetrics(apiClient);
        } catch (ApiException e) {
            System.err.println("API Error: " + e.getMessage());
            e.printStackTrace();
        }
    }

    private static void queryMetrics(ApiClient apiClient) throws ApiException {
        MetricsApi apiInstance = new MetricsApi(apiClient);

        // Time range: last hour
        long now = Instant.now().getEpochSecond();
        long ChronoUnit.HOURS).getEpochSecond();

        // Build query
        TimeseriesFormulaQueryRequest query = new TimeseriesFormulaQueryRequest()
            .type(TimeseriesFormulaRequestType.TIMESERIES_REQUEST)
            .attributes(new TimeseriesFormulaRequestAttributes()
                .formulas(Collections.singletonList(new QueryFormula().formula("query1")))
                .queries(Collections.singletonList(
                    new MetricsTimeseriesQuery()
                        .name("query1")
                        .dataSource(MetricsDataSource.METRICS)
                        .query("avg:system.cpu.user{*}")
                ))
                .from(oneHourAgo)
                .to(now)
            );

        TimeseriesFormulaRequest body = new TimeseriesFormulaRequest().data(query);

        // Execute query
        TimeseriesFormulaResponse result = apiInstance.queryTimeseriesData(body);
        System.out.println(result);
    }
}

Installation: Add to pom.xml:

<dependency>
    <groupId>com.datadoghq</groupId>
    <artifactId>datadog-api-client</artifactId>
    <version>2.30.0</version>
</dependency>

Go Example (using github.com/DataDog/datadog-api-client-go)

package main

import (
    "context"
    "encoding/json"
    "fmt"
    "os"
    "time"

    datadog "github.com/DataDog/datadog-api-client-go/v2/api/datadog"
    "github.com/DataDog/datadog-api-client-go/v2/api/datadogV2"
)

func main() {
    // Validate environment variables
    apiKey := os.Getenv("DD_API_KEY")
    appKey := os.Getenv("DD_APP_KEY")

    if apiKey == "" || appKey == "" {
        fmt.Println("Error: DD_API_KEY and DD_APP_KEY must be set")
        os.Exit(1)
    }

    // Configure API client
    ctx := context.WithValue(
        context.Background(),
        datadog.ContextAPIKeys,
        map[string]datadog.APIKey{
            "apiKeyAuth": {Key: apiKey},
            "appKeyAuth": {Key: appKey},
        },
    )

    configuration := datadog.NewConfiguration()
    apiClient := datadog.NewAPIClient(configuration)
    api := datadogV2.NewMetricsApi(apiClient)

    // Time range: last hour
    now := time.Now().Unix()
    oneHourAgo := time.Now().Add(-1 * time.Hour).Unix()

    // Build query
    body := datadogV2.TimeseriesFormulaRequest{
        Data: datadogV2.TimeseriesFormulaQueryRequest{
            Type: datadogV2.TIMESERIESFORMULAREQUESTTYPE_TIMESERIES_REQUEST,
            Attributes: datadogV2.TimeseriesFormulaRequestAttributes{
                Formulas: []datadogV2.QueryFormula{
                    {Formula: "query1"},
                },
                Queries: []datadogV2.TimeseriesQuery{
                    datadogV2.MetricsTimeseriesQuery{
                        Name:       datadog.PtrString("query1"),
                        DataSource: datadogV2.METRICSDATASOURCE_METRICS,
                        Query:      "avg:system.cpu.user{*}",
                    },
                },
                From: oneHourAgo,
                To:   now,
            },
        },
    }

    // Execute query
    result, _, err := api.QueryTimeseriesData(ctx, body)
    if err != nil {
        fmt.Printf("Error: %v\n", err)
        os.Exit(1)
    }

    jsonData, _ := json.MarshalIndent(result, "", "  ")
    fmt.Println(string(jsonData))
}

Installation: go get github.com/DataDog/datadog-api-client-go/v2

Rust Example (using datadog-api-client)

use datadog_api_client::datadog;
use datadog_api_client::datadogV2::api_metrics::MetricsAPI;
use datadog_api_client::datadogV2::model::*;
use std::collections::HashMap;

#[tokio::main]
async fn main() {
    // Validate environment variables
    let api_key = std::env::var("DD_API_KEY")
        .expect("DD_API_KEY must be set");
    let app_key = std::env::var("DD_APP_KEY")
        .expect("DD_APP_KEY must be set");

    // Configure API client
    let mut configuration = datadog::Configuration::new();
    configuration.api_key = Some(HashMap::from([
        ("apiKeyAuth".to_string(), api_key),
        ("appKeyAuth".to_string(), app_key),
    ]));

    let api = MetricsAPI::with_config(configuration);

    // Time range: last hour
    let now = chrono::Utc::now().timestamp();
    let - chrono::Duration::hours(1)).timestamp();

    // Build query
    let body = TimeseriesFormulaRequest::new(
        TimeseriesFormulaQueryRequest::new(
            TimeseriesFormulaRequestAttributes::new(
                vec![QueryFormula::new("query1".to_string())],
                one_hour_ago,
                vec![
                    TimeseriesQuery::MetricsTimeseriesQuery(Box::new(
                        MetricsTimeseriesQuery::new(
                            MetricsDataSource::METRICS,
                            "avg:system.cpu.user{*}".to_string(),
                        )
                        .name("query1".to_string())
                    ))
                ],
                now,
            ),
            TimeseriesFormulaRequestType::TIMESERIES_REQUEST,
        )
    );

    // Execute query
    match api.query_timeseries_data(body).await {
        Ok(result) => {
            println!("{:#?}", result);
        }
        Err(err) => {
            eprintln!("Error: {}", err);
        }
    }
}

Installation: Add to Cargo.toml:

[dependencies]
datadog-api-client = "0.3"
tokio = { version = "1", features = ["full"] }
chrono = "0.4"

Example Interactions

Example 1: Quick Metrics Query

User: "Show me CPU usage for the last hour"

Response:

# Execute pup command for immediate results
pup metrics query --query="avg:system.cpu.user{*}" --from="1h" --to="now" --output=table

Example 2: Code Generation for Application

User: "Generate Python code to query error logs"

Response:

Here's Python code to query error logs using the Datadog API client:

[Provide Python code example using datadog-api-client]

To use this code:
1. Save to a file: `query_logs.py`
2. Install: `pip install datadog-api-client`
3. Set environment variables: `export DD_API_KEY="..." DD_APP_KEY="..."`
4. Run: `python query_logs.py`

For immediate results without writing code, you can also use:
pup logs search --query="status:error" --from="1h"

Example 3: Monitor Management

User: "List all production monitors"

Response:

# Using pup CLI
pup monitors list --tag="env:production" --output=table

# Or generate code for your application (specify language: typescript, python, java, go, rust)

When to Use Each Approach

Use Pup CLI When:

• User wants immediate results
• Exploring/experimenting with Datadog
• One-off queries or operations
• Quick troubleshooting
• Testing queries before coding

Generate Code When:

• User asks to "generate code" or "create a script"
• Integrating into an application
• Automating recurring operations
• Building custom tools or dashboards
• User specifies a programming language

Best Practices

1. Start with pup for exploration: Use pup to test queries before generating code
2. Match the user's language: If they mention TypeScript, Python, Java, Go, or Rust, use that language
3. Provide complete examples: Include imports, error handling, and configuration
4. Explain authentication: Always mention DD_API_KEY, DD_APP_KEY, DD_SITE
5. Security reminders: Warn about not committing credentials to version control
6. Show both approaches: Mention pup for quick testing + code for integration

Integration with Agents

This skill works with all 46 domain agents in the plugin:

• Each agent describes Datadog functionality (logs, traces, metrics, monitors, etc.)
• Use pup commands that match the agent's domain
• Generate code using the corresponding Datadog API client methods

Common User Phrases

• "Query [logs/metrics/traces]"
• "Generate code to..."
• "Show me [data type]"
• "Create a [monitor/dashboard/SLO]"
• "Write a [Python/TypeScript/Java/Go/Rust] script that..."
• "I need a script to..."
• "How do I integrate Datadog with..."

Resources

• Pup CLI: pup --help
• Pup Documentation: Pup CLI Repository
• TypeScript Client: @datadog/datadog-api-client
• Python Client: datadog-api-client
• Go Client: datadog-api-client-go
• Java Client: datadog-api-client-java
• Rust Client: datadog-api-client-rust
• API Documentation: Datadog API Reference