### Example of MSI installation log output

Source: https://github.com/datadog/datadog-agent/blob/main/tools/windows/DatadogAgentInstaller/QA/README.md

This is an example output from analyzing MSI installation logs, showing detailed steps, custom actions, and context around specific events.

```text
[09/28/23 13:46:14] == datadog-agent-7.49.0-1.x86_64.msi with args 
[09/28/23 13:47:15] == Running scenario 'test case 1'
[09/28/23 13:47:15] == Installing datadog-agent-7.49.0-1.x86_64.msi with args
  Action start 13:47:16: WixSharp_InitRuntime_Action.
  SFXCA: Extracting custom action to temporary directory: C:\Windows\Installer\MSIE0A5.tmp-
  SFXCA: Binding to CLR version v4.0.30319
> Calling custom action WixSharp!WixSharp.ManagedProjectActions.WixSharp_InitRuntime_Action
  Action ended 13:47:17: WixSharp_InitRuntime_Action. Return value 1.
  Action start 13:47:17: AppSearch.
  Action ended 13:47:17: AppSearch. Return value 1.
  Action start 13:47:17: ReadInstallState.
  SFXCA: Extracting custom action to temporary directory: C:\Windows\Installer\MSIE3A4.tmp-
  SFXCA: Binding to CLR version v4.0.30319
> Calling custom action CustomActions!Datadog.CustomActions.InstallStateCustomActions.ReadInstallState
  CA: 13:47:18: RegistryProperty. Found DDAGENTUSER_NAME in registry win-3f423u4i7ug\ddagentuser
  CA: 13:47:18: RegistryProperty. Found PROJECTLOCATION in registry C:\Program Files\Datadog\Datadog Agent\
  CA: 13:47:18: RegistryProperty. Found APPLICATIONDATADIRECTORY in registry C:\ProgramData\Datadog\
  Action start 13:47:25: ReadConfig.
  SFXCA: Extracting custom action to temporary directory: C:\Windows\Installer\MSI1EC.tmp-
  SFXCA: Binding to CLR version v4.0.30319
> Calling custom action CustomActions!Datadog.CustomActions.ConfigCustomActions.ReadConfig
  Action ended 13:47:27: ReadConfig. Return value 1.
  Action start 13:47:27: MigrateFeatureStates.
  Action ended 13:47:27: MigrateFeatureStates. Return value 1.
  Action start 13:47:31: ProcessDdAgentUserCredentials.
  SFXCA: Extracting custom action to temporary directory: C:\Windows\Installer\MSI190F.tmp-
  SFXCA: Binding to CLR version v4.0.30319
> Calling custom action CustomActions!Datadog.CustomActions.ProcessUserCustomActions.ProcessDdAgentUserCredentials
  CA: 13:47:32: ProcessDdAgentUserCredentials. Found ddagentuser in WIN-3F423U4I7UG as SidTypeUser
  CA: 13:47:32: ProcessDdAgentUserCredentials. "WIN-3F423U4I7UG\ddagentuser" (S-1-5-21-2624507216-3994333568-3321347143-1000, SidTypeUser) is a local account
  CA: 13:47:32: ProcessDdAgentUserCredentials. Installing with DDAGENTUSER_PROCESSED_NAME=ddagentuser and DDAGENTUSER_PROCESSED_DOMAIN=WIN-3F423U4I7UG
  Action start 13:48:33: PrepareDecompressPythonDistributions.
  SFXCA: Extracting custom action to temporary directory: C:\Windows\Installer\MSIBC7.tmp-
  SFXCA: Binding to CLR version v4.0.30319
> Calling custom action CustomActions!Datadog.CustomActions.PythonDistributionCustomAction.PrepareDecompressPythonDistributions
  CA: 13:48:34: PrepareDecompressPythonDistributions. Could not set the progress bar size

```

--------------------------------

### Virtualenv Setup for Python Check Dependencies

Source: https://github.com/datadog/datadog-agent/blob/main/docs/dev/checks/README.md

This example demonstrates setting up a virtual environment and installing necessary dependencies, including datadog-checks-base, to run Python checks.

```bash
python3 -m pip install virtualenv
python3 -m pip install '/path/to/integrations-core/datadog_checks_base[deps]'
PYTHONPATH="$PWD/venv/lib/python3.10/site-packages:$PYTHONPATH" dda inv agent.run
```

--------------------------------

### Analyze Datadog Agent installation logs

Source: https://github.com/datadog/datadog-agent/blob/main/tools/windows/DatadogAgentInstaller/QA/README.md

This example shows how to parse installation logs to find specific patterns, like 'Running CustomAction', and display them with surrounding context lines for detailed analysis.

```powershell
Suite "My new test suite" {
    Case "test case 1" {
        Require @(
            "datadog-agent-7.49.0-1.x86_64.msi"
        )
        Test {
            $installResult = Install-MSI -installerName datadog-agent-7.49.0-1.x86_64.msi
            Assert_InstallSuccess $installResult
            $installResult.InstallLogs | Selec-String -Pattern "Running CustomAction" -Context 3 | Write-Host
        }
    }
}
```

--------------------------------

### User Install Setup for Python Check Dependencies

Source: https://github.com/datadog/datadog-agent/blob/main/docs/dev/checks/README.md

This example shows how to install Python check dependencies using a user-level pip install and setting the PYTHONPATH environment variable.

```bash
python3 -m pip install --user '/path/to//integrations-core/datadog_checks_base[deps]'
PYTHONPATH="$HOME/.local/lib/python3.10/site-packages:$PYTHONPATH" dda inv agent.run
```

--------------------------------

### Run Datadog Agent Installer QA Script

Source: https://github.com/datadog/datadog-agent/blob/main/tools/windows/DatadogAgentInstaller/QA/README.md

Example command to execute the QA test suite for the Datadog Agent Windows installer. Ensure all required parameters are provided.

```powershell
PS C:\dev\datadog-agent> & .\tools\windows\DatadogAgentInstaller\QA\qa_installer.ps1 -vmName "Windows Server 2022 FR" -vmUserName "win-3f423u4i7ug\administrateur" -vmUserPassword "********" -vmInitialSnapshotName "Windows Server 2022 FR - (27/07/2023 - 12:06:27)" -qaSessionname "Non-canonical permission issue QA" -qaArtifactsPath "C:\dev\QA\artifacts"
```

--------------------------------

### Full build-ddot-byoc Example

Source: https://github.com/datadog/datadog-agent/blob/main/tools/build-ddot-byoc/README.md

An example demonstrating how to use `build-ddot-byoc` with specific OCI URLs, local binary path, and target OS/architecture.

```bash
./build-ddot-byoc \
  --agent-oci  registry.datadoghq.com/agent:7.78.0 \
  --otel-agent ./bin/otel-agent/otel-agent \
  --output-oci my-registry/my-project/ddot-byoc/agent:custom \
  --os   linux \
  --arch amd64
```

--------------------------------

### Start DogStatsD with Configuration File

Source: https://github.com/datadog/datadog-agent/blob/main/cmd/dogstatsd/README.md

Use this command to start DogStatsD with a specified configuration file.

```bash
dogstatsd start -f datadog.yaml
```

--------------------------------

### Install Development Tools

Source: https://github.com/datadog/datadog-agent/blob/main/AGENTS.md

Run this command to install any missing development tools required for the project.

```bash
dda inv install-tools
```

--------------------------------

### Start Developer Environment with Local Checkout

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/tutorials/dev/env.md

Start a developer environment using your existing local clone of the Datadog Agent repository.

```bash
dda env dev start
```

--------------------------------

### Changelog Upgrade Example

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/guidelines/contributing.md

List actions or limitations upon upgrading the Agent. This example shows how to document upgrade-related information.

```yaml
upgrade:
  - |
    If you run a Nomad agent older than 0.6.0, the `nomad_group`
    tag will be absent until you upgrade your orchestrator.
```

--------------------------------

### SECL args_flags and args_options examples

Source: https://github.com/datadog/datadog-agent/blob/main/CHANGELOG.rst

Examples demonstrating the usage of \"args_flags\" and \"args_options\" in SECL for runtime security rules based on command line arguments.

```plaintext
Examples:

  - ``version`` is part of ``args_flags`` for the command ``cat --version``
  - ``l`` and ``n`` both are in ``args_flags`` for the command ``netstat -ln``
  - ``T=8`` and ``width=8`` both are in ``args_options`` for the command
    ``ls -T 8 --width=8``.
```

--------------------------------

### Install Pre-commit

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/setup/optional.md

Install the `pre-commit` tool using the Datadog Agent's development utility. This is a prerequisite for setting up local pre-commit hooks.

```shell
dda self pip install pre-commit
```

--------------------------------

### Configure Kubernetes Resources as Tags

Source: https://github.com/datadog/datadog-agent/blob/main/CHANGELOG.rst

Associate group resources with annotations-to-tags or labels-to-tags maps. For example, 'pods' can be associated with an annotations-to-tags map to configure annotations as tags for pods.

```json
{"pods": {"annotationKey1": "tag1", "annotationKey2": "tag2"}}
```

--------------------------------

### Configure check_tag_cardinality for Redis

Source: https://github.com/datadog/datadog-agent/blob/main/CHANGELOG.rst

Example of how to set the `check_tag_cardinality` parameter in annotations for a Redis check. This overrides the base agent configuration for cardinality.

```yaml
ad.datadoghq.com/redis.checks: |
  {
    "redisdb": {
      "check_tag_cardinality": "high",
      "instances": [
        {
          "host": "%%host%%",
          "port": "6379"
        }
      ]
    }
  }
```

--------------------------------

### Initialize and Use Forwarder

Source: https://github.com/datadog/datadog-agent/blob/main/comp/forwarder/defaultforwarder/README.md

Demonstrates how to initialize a new forwarder with multiple API keys per domain, start it, submit payloads, and stop it.

```go
KeysPerDomains := map[string][]string{
	"http://api.datadog.com": {"my_secret_key_1", "my_secret_key_2"},
	"http://debug.api.com":   {"secret_api"},
}

forwarder := forwarder.NewForwarder(KeysPerDomains)
forwarder.NumberOfWorkers = 1 // default: config.GetInt("forwarder_num_workers")
forwarder.Start()

// ...

payload1 := []byte("some payload")
payload2 := []byte("another payload")
forwarder.SubmitSeries(Payloads{&payload1, &payload2}, ...)

// ...

forwarder.Stop()
```

--------------------------------

### Initialize KMT with All VM Images

Source: https://github.com/datadog/datadog-agent/blob/main/tasks/kernel_matrix_testing/README.md

Initializes the KMT environment by downloading all available VM images for the current architecture. This process can be time-consuming.

```bash
dda inv -e kmt.init --all-images
```

--------------------------------

### Initialize KMT for Full Setup (Local & Remote)

Source: https://github.com/datadog/datadog-agent/blob/main/tasks/kernel_matrix_testing/README.md

Initializes KMT for both local and remote VMs, downloading necessary VM images and configuring SSH keys. Use `kmt.update-resources` and `kmt.config-ssh-key` for existing setups.

```bash
# This will download the Ubuntu 22.04 image and start the SSH key setup wizard
dda inv -e kmt.init --images=ubuntu_22.04
```

--------------------------------

### Create a package file for the new module

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/how-to/go/modules.md

Create a doc.go file with the necessary package declaration and license information for your new module.

```go
// Unless explicitly stated otherwise all files in this repository are licensed
// under the Apache License Version 2.0.
// This product includes software developed at Datadog (https://www.datadoghq.com/).
// Copyright 2025-present Datadog, Inc.
package mymodule

```

--------------------------------

### Fakeintake API Get Payloads Curl Example

Source: https://github.com/datadog/datadog-agent/blob/main/test/fakeintake/docs/README.md

Example using curl to fetch payloads from a specific POST endpoint on the fake intake server. Replace SERVICE_IP and post_endpoint_path with actual values.

```bash
curl ${SERVICE_IP}/fakeintake/payloads?endpoint={post_endpoint_path}
```

```bash
curl ${SERVICE_IP}/fakeintake/payloads?endpoint=/api/V2/series
```

--------------------------------

### PowerShell Error Reporting Example

Source: https://github.com/datadog/datadog-agent/blob/main/tools/windows/DatadogAgentInstaller/QA/README.md

Illustrates how PowerShell reports errors during script execution, including custom action failures. This output is useful for debugging installation issues.

```powershell
[09/28/23 13:52:21] ❌ Expected installation to succeed, but it failed with code 1603
    + CategoryInfo          : NotSpecified: (:) [Write-Error], WriteErrorException
    + FullyQualifiedErrorId : Microsoft.PowerShell.Commands.WriteErrorException,report_err
    + PSComputerName        : Windows Server 2022 FR
```

--------------------------------

### Default OpenTelemetry Collector Configuration

Source: https://github.com/datadog/datadog-agent/blob/main/comp/otelcol/README.md

This is the default pipeline configuration provided with DDOT, including extensions, receivers, exporters, processors, and service pipelines. It can be used as a starting point for your OpenTelemetry Collector setup.

```yaml
extensions:
  health_check:
    endpoint: localhost:13133
  pprof:
    endpoint: localhost:1777
  zpages:
    endpoint: localhost:55679
  ddflare:
    endpoint: localhost:7777


receivers:
  otlp:
    protocols:
      grpc:
        endpoint: 0.0.0.0:4317
      http:
        endpoint: 0.0.0.0:4318
    # Collect own metrics
  prometheus:
    config:
      scrape_configs:
      - job_name: 'otel-collector'
        fallback_scrape_protocol: PrometheusText0.0.4
        metric_name_validation_scheme: legacy
        scrape_interval: 60s
        scrape_protocols:
          - PrometheusText0.0.4
        static_configs:
        - targets: ['0.0.0.0:8888']
        metric_relabel_configs:
        - source_labels: [__name__]
          regex: ".*grpc_io.*"
          action: drop
exporters:
  datadog:
    hostname: "otelcol-docker"
    api:
      key: ${env:DD_API_KEY}
      site: ${env:DD_SITE}
    sending_queue:
      batch:
processors:
  infraattributes:
  # using the sampler
  probabilistic_sampler:
    sampling_percentage: 30
connectors:
  # Use datadog connector to compute stats for pre-sampled traces
  datadog/connector:
    traces:
      compute_stats_by_span_kind: true
      peer_tags_aggregation: true
service:
  extensions: [health_check, pprof, zpages, ddflare]
  pipelines:
    traces: # this pipeline computes APM stats
      receivers: [otlp]
      exporters: [datadog/connector]
    traces/sampling: # this pipeline uses sampling and sends traces
      receivers: [otlp]
      processors: [probabilistic_sampler, infraattributes]
      exporters: [datadog]
    metrics:
      receivers: [otlp, datadog/connector, prometheus]
      processors: [infraattributes]
      exporters: [datadog]
    logs:
      receivers: [otlp]
      processors: [infraattributes]
      exporters: [datadog]

```

--------------------------------

### Create a Simple Fx App

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/components/fx.md

Demonstrates the basic structure of an Fx application using fx.New, fx.Provide, and fx.Invoke.

```go
someValue = "my password is hunter2"
app := fx.New(
    fx.Provide(scrubber.NewScrubber),
    fx.Invoke(func(sc scrubber.Component) {
        fmt.Printf("scrubbed: %s", sc.ScrubString(somevalue))
    }))
app.Run()
// Output: scrubbed: my password is *******
```

--------------------------------

### Chain Instrumentation Targets

Source: https://github.com/datadog/datadog-agent/blob/main/CHANGELOG-DCA.rst

Chain instrumentation targets where the first matching rule takes precedence. This example installs the Python tracer for pods labeled `language=python` and the Java tracer for pods in a namespace labeled `language=java`.

```yaml
instrumentation:
  enabled: true
  targets:
    - name: "Python Services"
      podSelector:
        matchLabels:
          language: "python"
      ddTraceVersions:
        python: "default"
    - name: "Java Namespaces"
      namespaceSelector:
        matchLabels:
          language: "java"
      ddTraceVersions:
        python: "default"
```

--------------------------------

### Build and Deploy Local Datadog Agent Image

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/tutorials/dev/lab/kind.md

Build a custom Datadog Agent image locally using a specified build command, then load and install it into the Kind cluster. Ensure the developer environment is started first.

```bash
dda env dev start
```

```bash
# Example: build an image tagged datadog/agent-dev:local, then load+install it
dda lab local kind --id dev \
  --build-command "dda inv agent.hacky-dev-image-build --target-image datadog/agent-dev:local"
```

--------------------------------

### Query expvar for collector check names

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/how-to/debug-agents/index.md

Use curl and jq to query the Agent's expvar endpoint to get the names of checks that the collector's check runner has executed. This example retrieves the keys from the 'runner.Checks' object.

```shell
curl -s http://localhost:5000/debug/vars | jq '.runner.Checks | keys'
```

--------------------------------

### Module Configuration Example

Source: https://github.com/datadog/datadog-agent/blob/main/cmd/system-probe/modules/AGENTS.md

Illustrates how to configure system-probe modules using a namespace pattern and environment variables.

```APIDOC
## Configuration

Module configuration follows the namespace pattern:

```go
// In pkg/config/setup/system_probe.go
const myModuleNS = "my_module"

cfg.BindEnvAndSetDefault(join(myModuleNS, "enabled"), false)
cfg.BindEnvAndSetDefault(join(myModuleNS, "option1"), "default")
```

Access in module:
```go
enabled := cfg.GetBool("my_module.enabled")
```
```

--------------------------------

### Installing datadog-checks-base as a Pre-requisite

Source: https://github.com/datadog/datadog-agent/blob/main/docs/dev/checks/README.md

Installs the core datadog-checks-base library, which is a prerequisite for running custom Python checks. Use this command when installing to the Python user install directory.

```bash
python3 -m pip install --user './datadog_checks_base[deps]'
```

--------------------------------

### Import and Use Core Bundle

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/components/using-components.md

Shows how to import the 'core' bundle and use its components, such as 'config'. Only the explicitly requested components are instantiated by Fx.

```go
import (
    // We import the core bundle
    core "github.com/DataDog/datadog-agent/comp/core"

    // Then the interfaces we want to use
    config "github.com/DataDog/datadog-agent/comp/core/config/def"
)

// [...] 
    return fxutil.OneShot(
        myTestCallback, // The function to call from fxutil.OneShot
        core.Bundle(core.WithSecrets()),  // This will tell FX how to create the all the components included in the bundle
    )
// [...] 

func myTestCallback(conf config.Component) {
    api_key := conf.GetString("api_key")

    // [...] 
}
```

--------------------------------

### Install and Verify Bazel-Built Debian Package

Source: https://github.com/datadog/datadog-agent/blob/main/packages/installer/MIGRATION_PLAN.md

Commands to install a Bazel-built Debian package and verify its installation and version.

```bash
dpkg -i datadog-installer_*.deb
ls -la /usr/bin/datadog-bootstrap
/usr/bin/datadog-bootstrap version
```

--------------------------------

### Build Instructions

Source: https://github.com/datadog/datadog-agent/blob/main/cmd/ai_prompt_logger/README.md

Instructions for building the AI prompt logger native host binary using Cargo or Bazel.

```APIDOC
## Build

From the **repository root** (workspace member):

```bash
cargo build -p ai-prompt-logger-native-host --release
```

Binary: `target/release/ai-prompt-logger-native-host` (`.exe` on Windows).

With Bazel (same as CI / omnibus for macOS):

```bash
bazelisk build --config=release //cmd/ai_prompt_logger:ai-prompt-logger-native-host
```
```

--------------------------------

### Installing a Specific Standard Check (redisdb)

Source: https://github.com/datadog/datadog-agent/blob/main/docs/dev/checks/README.md

Installs the 'redisdb' check and its dependencies from the integrations-core repository to the Python user install directory.

```bash
python3 -m pip install --user ./redisdb
```

--------------------------------

### Install Datadog Agent CLI

Source: https://github.com/datadog/datadog-agent/blob/main/AGENTS.md

Installs the Datadog Agent CLI using Homebrew on macOS. This is the recommended way to install the agent for development.

```bash
# install dda on mac OS
brew install --cask dda
```

--------------------------------

### Start Vagrant Development VM

Source: https://github.com/datadog/datadog-agent/blob/main/devenv/README.md

Use this command to start a Vagrant VM. The 'Vagrantfile' must be in the same directory as the box file. Specify the provider if it's not the default.

```bash
vagrant up win-10-prl --provider=parallels
```

--------------------------------

### Install Customized Datadog Agent

Source: https://github.com/datadog/datadog-agent/blob/main/tools/build-ddot-byoc/README.md

Install the Datadog Agent with the custom OpenTelemetry Collector using the standard Datadog installer, pointing to the OCI URL of the customized package.

```bash
sudo datadog-installer install oci://my-registry/my-project/ddot-byoc/agent:custom
```

--------------------------------

### Fx Dependency Injection with fx.In and fx.Out

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/components/fx.md

Shows how to use fx.In and fx.Out to structure constructor dependencies and return values, improving readability and documentation.

```go
type dependencies struct {
    fx.In

    Config config.Component
    Log log.Component
    Status status.Component
}

type provides struct {
    fx.Out

    Component
    // ... (we'll see why this is useful below)
}

func newScrubber(deps dependencies) (provides, error) { // can return an fx.Out struct and other types, such as error
    // ..
    return provides {
        Component: scrubber,
        // ..
    }, nil
}
```

--------------------------------

### Datadog Installer Assertion Utilities

Source: https://github.com/datadog/datadog-agent/blob/main/tools/windows/DatadogAgentInstaller/QA/README.md

Provides a set of assertion functions for testing Datadog Agent installations. These functions check for directory existence, ACL properties, and installation success/failure codes.

```powershell
- `Assert_DaclAutoInherited_Flag_Set`: Asserts that a directory exists and it has the auto-inherited flag set.
- `Assert_DaclAutoInherited_Flag_NotSet`: The opposite of the previous assertion. Provided for convenience. The directory must still exist.
- `Assert_DirectoryExists`: Asserts that the directory exist.
- `Assert_DirectoryNotExists`: Asserts that the directory doesn't exist.
- `Assert_InstallSuccess`: Checks that the result of `Install-MSI` or `Uninstall-MSI` is 0, and if not, reports an error and prints the logs in the console.
- `Assert_InstallFinishedWithCode`: Asserts that the install finished with a specific error code (typically `1603`).
```

--------------------------------

### Install Linux Dependencies

Source: https://github.com/datadog/datadog-agent/blob/main/test/e2e-framework/README.md

Installs necessary Linux dependencies for the testing framework.

```bash
sudo apt install libnotify-bin
```

--------------------------------

### Install rtloader Targets

Source: https://github.com/datadog/datadog-agent/blob/main/rtloader/rtloader/CMakeLists.txt

Defines installation destinations for the rtloader library and headers.

```cmake
install(TARGETS datadog-agent-rtloader
    RUNTIME DESTINATION bin
    LIBRARY DESTINATION ${CMAKE_INSTALL_LIBDIR}
    PUBLIC_HEADER DESTINATION include
    ARCHIVE DESTINATION ${CMAKE_INSTALL_LIBDIR}
)
```

--------------------------------

### Initialize Component Dependencies for Testing

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/components/testing.md

Sets up the necessary mock dependencies (config and log components) required to instantiate a component for testing. Use mocks to isolate the component under test.

```go
package implzstd

import (
    "testing"

    configmock "github.com/DataDog/datadog-agent/comp/core/config/mock"
    logmock "github.com/DataDog/datadog-agent/comp/core/log/mock"
)

func TestCompress(t *testing.T) {
    logComponent := configmock.New(t)
    configComponent := logmock.New(t)

    requires := Requires{
        Conf: configComponent,
        Log: logComponent,
    }
    // [...]
```

--------------------------------

### Initialize KMT with Specific VM Images

Source: https://github.com/datadog/datadog-agent/blob/main/tasks/kernel_matrix_testing/README.md

Initializes the KMT environment by downloading specified VM images and tools, and installing system dependencies. Running this command overwrites previous configurations and downloaded resources.

```bash
dda inv -e kmt.init --images=ubuntu_22.04,debian_10
```

--------------------------------

### Install Python Dependencies

Source: https://github.com/datadog/datadog-agent/blob/main/test/e2e-framework/README.md

Installs Python dependencies for the E2E framework using the dda tool.

```bash
cd ~/dd/datadog-agent/test/e2e-framework && dda -v self dep sync -f legacy-e2e -f legacy-github
```

--------------------------------

### One-time Setup Task

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/how-to/test/e2e.md

Run this setup task once on a new machine to configure the E2E testing environment. It handles AWS keypair creation and Pulumi passphrase generation.

```bash
dda inv e2e.setup
```

```bash
dda inv e2e.setup --with-azure --with-gcp
```

--------------------------------

### Install Reno

Source: https://github.com/datadog/datadog-agent/blob/main/docs/public/guidelines/contributing.md

Install the Reno tool using pip. This is required for creating release notes.

```bash
pip install reno
```

--------------------------------

### Basic Filtering Example

Source: https://github.com/datadog/datadog-agent/blob/main/comp/core/workloadfilter/README.md

Demonstrates how to create a filterable container, define filter groups with precedence, generate a filter bundle, and check if a container should be excluded.

```go
import (
    workloadfilter "github.com/DataDog/datadog-agent/comp/core/workloadfilter/def"
    workloadmetafilter "github.com/DataDog/datadog-agent/comp/core/workloadfilter/util/workloadmeta"
)

// Create a filterable container object
filterableContainer := workloadmetafilter.CreateContainer(wmetaContainer)

// Define filter groups example (in order of precedence)
selectedFilterGroups := [][]workloadfilter.ContainerFilter{
    // High precedence: Check autodiscovery annotations first
    {workloadfilter.ContainerADAnnotations},
    
    // Medium precedence: Check legacy AC filters
    {workloadfilter.LegacyContainerACInclude, workloadfilter.LegacyContainerACExclude},
    
    // Low precedence: Check global filters
    {workloadfilter.LegacyContainerGlobal},
}
filterBundle := filterStore.GetContainerFilters(selectedFilterGroups)

// Check if container should be excluded
if filterBundle.IsExcluded(filterableContainer) {
    // Container is excluded, skip processing
    return
}

// Container is included, proceed with processing

```

--------------------------------

### Build and Run GPU Event Viewer

Source: https://github.com/datadog/datadog-agent/blob/main/pkg/gpu/testutil/event-viewer/README.md

Build the GPU event viewer using the provided command and then run it with the path to the recorded events file.

```bash
dda inv system-probe.build-gpu-event-viewer
pkg/gpu/testutil/event-viewer/event-viewer path-to-events-file
```

--------------------------------

### Start Process-Agent with s6-svc

Source: https://github.com/datadog/datadog-agent/blob/main/Dockerfiles/agent/SUPERVISION.md

Use `s6-svc -u` to re-enable and start the process-agent service after it has been disabled.

```bash
root@95c063fac5c4:/# s6-svc -u /var/run/s6/services/process/
root@95c063fac5c4:/# s6-svstat /var/run/s6/services/process/
up (pid 2597) 5 seconds
```