Jan 22, 2023 · Jan 22, 2023
diff --git a/_data/nav.yml b/_data/nav.yml
  pages:
    - title: Introduction to Codefresh pipelines
      url: "/introduction-to-codefresh-pipelines"
    - title: Creatinga CI pipeline
    - title: Creatingpipelines
      url: "/pipelines"
    - title: Steps in pipelines
      url: "/steps"
      url: "/stages"
    - title: Caching for pipelines
      url: "/pipeline-caching"
    - title: DebuggingCIpipelines
    - title: Debugging pipelines
      url: "/debugging-pipelines"
    - title: MonitoringCIpipelines
    - title: Monitoring pipelines
      url: "/monitoring-pipelines"
    - title: Advanced workflows
    - title: Advanced workflows for pipelines
      url: "/advanced-workflows"
    - title: Deployment environments
      url: "/deployment-environments"
diff --git a/_docs/example-catalog/ci-examples/cpp-cmake.md b/_docs/example-catalog/ci-examples/cpp-cmake.md
 ## Related articles
 [C example]({{site.baseurl}}/docs/example-catalog/ci-examples/c-make/)
 [Codefresh YAML for pipeline definitions]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/)
 [Steps in pipelines]({{site.baseurl}}/docs/codefresh-yaml/steps/)
 [Steps in pipelines]({{site.baseurl}}/docs/pipelinessteps/)
 [Creating pipelines]({{site.baseurl}}/docs/pipelines/pipelines/)
 [How Codefresh pipelines work]({{site.baseurl}}/docs/pipelines/introduction-to-codefresh-pipelines/)
diff --git a/_docs/getting-started/intro-to-codefresh.md b/_docs/getting-started/intro-to-codefresh.md
 ---
 title: "What is Codefresh"
 title: "What is Codefresh?"
 description: "Understand the features and benefits of Codefresh"
 group: getting-started
 toc: true
 ---

 TBD
 What is Codefresh?

 The short answer: Complete CI/CD and GitOps-powered Argo CD in the same product.

 <!--- What's different in Codefresh when it comes to continuous integration (CI) and continuous delivery (CD)?
 There is a long answer and a short one.
 Now for the long answer.

 Let's start with the short answer:
 ## Codefresh & CI/CD

 Codefresh:
 * Is a _complete CI/CD solution_, and not just CI.
 * Works with all major Git platforms and cloud providers. There is no lock-in with any particular vendor.
 * Has pipelines with Has several unique features such as a distributed Docker layer cache, an auto-mounted shared volume, a private Docker registry and a private Helm repository.
 Codefresh is a _complete CI/CD solution_, not just CI.
 We cover the full software lifecycle. View a release in the Kubernetes dashboard, click on it and go to the Docker image, click on the Docker image and go to the build that created it, all from a single interface.

 Codefresh is turbo-charged CI/CD!

 We work with all major Git platforms and cloud providers. There is no lock-in with any particular vendor. Unlike other CI/CD platforms which can be tightly coupled to a single Git provider, or a specific vendor or set of tools, Codefresh supports a fully programmtic implementation.

 Everything in the pipeline is defined as code and applied with the command line. Storing the definitions in a code repo ensures consistenct. Upscaling or expanind Doing so means we can create all of our pipelines in a consistent way and store those definitions in a code repository. Taking this one step step deeper, we could then create a bootstrap pipeline in Codefresh that generates pipelines when new definitions are added to this repo. See our previous post Programmatic Creation of Codefresh Pipelines (part 2) for more on this.

 Has pipelines with Has several unique features such as a distributed Docker layer cache, an auto-mounted shared volume, a private Docker registry and a private Helm repository.
 * Built-in Kubernetes and Helm dashboards
 * Helm charts browser, and Helm environment board

 As you can see, Codefresh is turbo-charged CI/CD. We cover the full software lifecycle. View a release in the Kubernetes dashboard, click on it and go to the Docker image, click on the Docker image and go to the build that created it, all from a single interface.



 How does Codefresh do it?
 Everything for CI/CD in Codefresh starts and ends with pipelines. In Codefresh, a pipeline can do pretty much anyhting, only CI, only CD, both CI and CD.

 Your CI pipeline can compile and package code, build and push Docker images. The CD pipeline can deploy applications/artifacts to VMs, Kubernetes clusters, FTP sites, S3 buckets, and more. And yet another pipeline combines both integration and deployment for full CI/CD.
 Other pipelines can run unit tests, integration tests, acceptance tests etc. CI/CD pipeline to create and deploy your applications, or a pipeline run any custom action, such as tests.





 Completely programmatic approach
 Unlike other CI/CD platforms which can be tightly coupled to a single Git provider, or a specific vendor or set of tools, Codefresh supports a fully programmtic implementation. Pipelines are create pipelines and define the pipeline’s steps, triggers, and variables.
 Pipelines are create pipelines and define the pipeline’s steps, triggers, and variables.


 Everything in the pipeline is defined as code and applied with the command line. Storing the definitions in a code repo ensures consistenct. Upscaling or expanind Doing so means we can create all of our pipelines in a consistent way and store those definitions in a code repository. Taking this one step step deeper, we could then create a bootstrap pipeline in Codefresh that generates pipelines when new definitions are added to this repo. See our previous post Programmatic Creation of Codefresh Pipelines (part 2) for more on this.


 The Docker registry integrations and all cluster integrations are automatically available to all pipelines. You don’t need docker login commands or kubectl commands to set up a Kube context inside your pipeline.
 To see how pipeline work, start with the [Introduction to Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/), or jump to [pipeline creation]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipelines/).

 ## Releases and dashboards
 Dashboards are key to providing information at the right time and in
 Dashboards are key to providing information at the right time.
 perational Dashboards ~ Exposing the most commonly needed application and environmental information to developers so that they can troubleshoot without needing assistance from the DevOps teams; even in production

 Analytical Dashboards ~ Developers often need to reach out to the DevOps team to get statistics and metrics around builds and deployments. Codefresh automatically generates DORA metrics as well as many other key indicators of build and deployment efficiency, which can be easily viewed in seconds by product owners and management alike.

 ## Integrations
 ### Integrations
 For a seamless CI/CD experience, Codefresh has native integrations with alomst every major provider.
 Easily connect Git providers, registry providers, storage providers, secret stores, and notification channels

 Docker registry and all cluster integrations are automatically available to all pipelines. You don’t need Docker login commands or `kubectl` commands to set up a Kube context inside your pipeline.

 Go to Pipeline Integrations in the UI, and see how to
 ## Where to go from here
 ### Where to go from here
 Here are several links we think wouuld be useful t

 ###Quick starts
 Quick starts
 To get up and running, follow our quick starts. The quick start modules are a series of flows that guide you from setting up your first account in Codefresh, to creating a basic pipeline, and deploying to Kubernetes.
 See XREF TBD

 ###Example catalog
 Example catalog
 For those who are familiar with CI/CD, we have an extensive collection of examples, covering several CI and CD scenarios:
 [CI examples]({{site.baseurl}}/docs/example-catalog/ci-examples/)
 [CD examples]({{site.baseurl}}/docs/example-catalog/cd-examples/)

 ###Guides
 Guides
 And finally, if you want more meat, dive in to our detailed guides.
 XREF TBD

 -->
 ## Codefresh & GitOps with Argo CD

diff --git a/_docs/integrations/docker-registries/digital-ocean-container-registry.md b/_docs/integrations/docker-registries/digital-ocean-container-registry.md
    type: "git-clone"
    repo: "anais-codefresh/react-article-display"
    # CF_BRANCH value is auto set when pipeline is triggered
    # Learn more at codefresh.io/docs/docs/codefresh-yaml/variables/
    # Learn more at codefresh.io/docs/docs/pipelinesvariables/
    revision: "${{CF_BRANCH}}"
    git: "github"
    stage: "clone"
diff --git a/_docs/pipelines/advanced-workflows.md b/_docs/pipelines/advanced-workflows.md

 With the parallel execution mode, you can define complex pipelines with fan-in/out configurations capable of matching even the most complicated workflows within an organization.

 >In Codefresh, parallel execution is unrelated to [stages]({{site.baseurl}}/docs/codefresh-yaml/stages/). Stages are only a way to visually organize your pipeline steps. The actual execution is independent from the visual layout in the logs view.
 >In Codefresh, parallel execution is unrelated to [stages]({{site.baseurl}}/docs/pipelines/stages/). Stages are only a way to visually organize your pipeline steps. The actual execution is independent from the visual layout in the logs view.

 Before going any further make sure that you are familiar with the [basics of Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/).


 ### Example: Running multiple test suites in parallel

 All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of [freestyle steps]({{site.baseurl}}/docs/codefresh-yaml/steps/freestyle/) for unit/integration tests.
 All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of [freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) for unit/integration tests.

 Let's say that you have a Docker image with a Python back-end and a JavaScript front-end. You could run both types of tests in parallel with the following yaml syntax:

 * Pending
 * Running

 Finished is a shorthand for `success` or `failure` or `skipped`. It is only valid when used in [step dependencies]({{site.baseurl}}/docs/codefresh-yaml/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.
 Finished is a shorthand for `success` or `failure` or `skipped`. It is only valid when used in [step dependencies]({{site.baseurl}}/docs/pipelines/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.

 You can mix and match completion states from any other step in your pipeline. Here are some examples:

diff --git a/_docs/pipelines/hooks.md b/_docs/pipelines/hooks.md
 {% endraw %}
 {% endhighlight %}

 You can change this behavior by using the existing [fail_fast property]({{site.baseurl}}/docs/codefresh-yaml/what-is-the-codefresh-yaml/#execution-flow) inside an `on_elected` hook.
 You can change this behavior by using the existing [fail_fast property]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/#execution-flow) inside an `on_elected` hook.

 `codefresh.yml`
 {% highlight yaml %}
diff --git a/_docs/pipelines/introduction-to-codefresh-pipelines.md b/_docs/pipelines/introduction-to-codefresh-pipelines.md

 * Compile and package code
 * Build Docker images
 * Push Docker images to any [Docker Registry]({{site.baseurl}}/docs/docker-registries/external-docker-registries/)
 * Push Docker images to any [Docker Registry]({{site.baseurl}}/docs/integrations/docker-registries/)
 * Deploy applications/artifacts to VMs, Kubernetes clusters, FTP sites, S3 buckets etc.
 * Run [unit tests]({{site.baseurl}}/docs/testing/unit-tests/), [integration tests]({{site.baseurl}}/docs/testing/integration-tests/), acceptance tests etc.
 * Any custom action that you define
 %}


 1. The first step runs under the context of a Node image that prepares the application and runs[unit tests]({{site.baseurl}}/docs/testing/unit-tests/).
 1. The first step runs under the context of a Node image that prepares the application and runs unit tests.
 1. The second step uses an image with s3 command line tools and uploads the test results to a bucket that holds test reports.
 1. The helm step creates a Helm chart and pushes it to a Helm repository.

 1. Finally, `/codefresh/volume` is an internal folder name, and you should use  `{% raw %}${{CF_VOLUME_PATH}}{% endraw %}` in your codefresh.yml file
 if you really want to reference this folder. You can also reference your project folder as `{% raw %}${{CF_VOLUME_PATH}}/${{CF_REPO_NAME}}{% endraw %}` if you need it.

 Seethe [System Provided Variables]({{site.baseurl}}/docs/pipelines/variables/#system-provided-variables) section for more information.
 See[system variables]({{site.baseurl}}/docs/pipelines/variables/#system-variables) for more information.

 ### Working with Docker inside a Codefresh pipeline

  commands:
    - pip install .
 ```
 For the plugins in the [Step Marketplace](https://codefresh.io/steps/) we already give an example of the YAML part that must be included in your pipeline:
 For the plugins in the [Step Marketplace](https://codefresh.io/steps/){:target="\_blank"} we already give an example of the YAML part that must be included in your pipeline:

 {% include
 image.html
 ### Creating Docker images dynamically as build tools


 Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that [freestyle pipeline steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) are just a series of commands that run inside the context of a Docker container. In most cases the images used
 for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or [private Docker registries]({{site.baseurl}}/docs/docker-registries/external-docker-registries/).
 Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that [freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) in pipelines are just a series of commands that run inside the context of a Docker container. In most cases the images used for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or [private Docker registries]({{site.baseurl}}/docs/integrations/docker-registries/).

 Codefresh is one the few CI/CD solutions that not only offers easy Docker registry integration
 accessible to all pipelines

 ### How caching works in Codefresh

 Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our [caching guide]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipeline-caching/) for more details.
 Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our [caching guide]({{site.baseurl}}/docs/pipelines/pipeline-caching/) for more details.

 ### Calling other pipelines
Original file line number	Diff line number	Diff line change
Expand Up		@@ -362,7 +362,7 @@
		pages:
		- title: Introduction to Codefresh pipelines
		url: "/introduction-to-codefresh-pipelines"
		- title: Creatinga CI pipeline
		- title: Creatingpipelines
		url: "/pipelines"
		- title: Steps in pipelines
		url: "/steps"
Expand DownExpand Up		@@ -414,11 +414,11 @@
		url: "/stages"
		- title: Caching for pipelines
		url: "/pipeline-caching"
		- title: DebuggingCIpipelines
		- title: Debugging pipelines
		url: "/debugging-pipelines"
		- title: MonitoringCIpipelines
		- title: Monitoring pipelines
		url: "/monitoring-pipelines"
		- title: Advanced workflows
		- title: Advanced workflows for pipelines
		url: "/advanced-workflows"
		- title: Deployment environments
		url: "/deployment-environments"
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -122,6 +122,6 @@ change the version of Gcc/g++ by starting from a different public or private Doc
		## Related articles
		[C example]({{site.baseurl}}/docs/example-catalog/ci-examples/c-make/)
		[Codefresh YAML for pipeline definitions]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/)
		[Steps in pipelines]({{site.baseurl}}/docs/codefresh-yaml/steps/)
		[Steps in pipelines]({{site.baseurl}}/docs/pipelinessteps/)
		[Creating pipelines]({{site.baseurl}}/docs/pipelines/pipelines/)
		[How Codefresh pipelines work]({{site.baseurl}}/docs/pipelines/introduction-to-codefresh-pipelines/)
Original file line number	Diff line number	Diff line change
		@@ -1,42 +1,47 @@
		---
		title: "What is Codefresh"
		title: "What is Codefresh?"
		description: "Understand the features and benefits of Codefresh"
		group: getting-started
		toc: true
		---

		TBD
		What is Codefresh?

		The short answer: Complete CI/CD and GitOps-powered Argo CD in the same product.

		<!--- What's different in Codefresh when it comes to continuous integration (CI) and continuous delivery (CD)?
		There is a long answer and a short one.
		Now for the long answer.

		Let's start with the short answer:
		## Codefresh & CI/CD

		Codefresh:
		* Is a _complete CI/CD solution_, and not just CI.
		* Works with all major Git platforms and cloud providers. There is no lock-in with any particular vendor.
		* Has pipelines with Has several unique features such as a distributed Docker layer cache, an auto-mounted shared volume, a private Docker registry and a private Helm repository.
		Codefresh is a _complete CI/CD solution_, not just CI.
		We cover the full software lifecycle. View a release in the Kubernetes dashboard, click on it and go to the Docker image, click on the Docker image and go to the build that created it, all from a single interface.

		Codefresh is turbo-charged CI/CD!

		We work with all major Git platforms and cloud providers. There is no lock-in with any particular vendor. Unlike other CI/CD platforms which can be tightly coupled to a single Git provider, or a specific vendor or set of tools, Codefresh supports a fully programmtic implementation.

		Everything in the pipeline is defined as code and applied with the command line. Storing the definitions in a code repo ensures consistenct. Upscaling or expanind Doing so means we can create all of our pipelines in a consistent way and store those definitions in a code repository. Taking this one step step deeper, we could then create a bootstrap pipeline in Codefresh that generates pipelines when new definitions are added to this repo. See our previous post Programmatic Creation of Codefresh Pipelines (part 2) for more on this.

		Has pipelines with Has several unique features such as a distributed Docker layer cache, an auto-mounted shared volume, a private Docker registry and a private Helm repository.
		* Built-in Kubernetes and Helm dashboards
		* Helm charts browser, and Helm environment board

		As you can see, Codefresh is turbo-charged CI/CD. We cover the full software lifecycle. View a release in the Kubernetes dashboard, click on it and go to the Docker image, click on the Docker image and go to the build that created it, all from a single interface.



		How does Codefresh do it?
		Everything for CI/CD in Codefresh starts and ends with pipelines. In Codefresh, a pipeline can do pretty much anyhting, only CI, only CD, both CI and CD.

		Your CI pipeline can compile and package code, build and push Docker images. The CD pipeline can deploy applications/artifacts to VMs, Kubernetes clusters, FTP sites, S3 buckets, and more. And yet another pipeline combines both integration and deployment for full CI/CD.
		Other pipelines can run unit tests, integration tests, acceptance tests etc. CI/CD pipeline to create and deploy your applications, or a pipeline run any custom action, such as tests.





		Completely programmatic approach
		Unlike other CI/CD platforms which can be tightly coupled to a single Git provider, or a specific vendor or set of tools, Codefresh supports a fully programmtic implementation. Pipelines are create pipelines and define the pipeline’s steps, triggers, and variables.
		Pipelines are create pipelines and define the pipeline’s steps, triggers, and variables.


		Everything in the pipeline is defined as code and applied with the command line. Storing the definitions in a code repo ensures consistenct. Upscaling or expanind Doing so means we can create all of our pipelines in a consistent way and store those definitions in a code repository. Taking this one step step deeper, we could then create a bootstrap pipeline in Codefresh that generates pipelines when new definitions are added to this repo. See our previous post Programmatic Creation of Codefresh Pipelines (part 2) for more on this.


		The Docker registry integrations and all cluster integrations are automatically available to all pipelines. You don’t need docker login commands or kubectl commands to set up a Kube context inside your pipeline.
Expand DownExpand Up		@@ -82,32 +87,33 @@ A Codefresh pipeline has two distinct aspects:
		To see how pipeline work, start with the [Introduction to Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/), or jump to [pipeline creation]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipelines/).

		## Releases and dashboards
		Dashboards are key to providing information at the right time and in
		Dashboards are key to providing information at the right time.
		perational Dashboards ~ Exposing the most commonly needed application and environmental information to developers so that they can troubleshoot without needing assistance from the DevOps teams; even in production

		Analytical Dashboards ~ Developers often need to reach out to the DevOps team to get statistics and metrics around builds and deployments. Codefresh automatically generates DORA metrics as well as many other key indicators of build and deployment efficiency, which can be easily viewed in seconds by product owners and management alike.

		## Integrations
		### Integrations
		For a seamless CI/CD experience, Codefresh has native integrations with alomst every major provider.
		Easily connect Git providers, registry providers, storage providers, secret stores, and notification channels

		Docker registry and all cluster integrations are automatically available to all pipelines. You don’t need Docker login commands or `kubectl` commands to set up a Kube context inside your pipeline.

		Go to Pipeline Integrations in the UI, and see how to
		## Where to go from here
		### Where to go from here
		Here are several links we think wouuld be useful t

		###Quick starts
		Quick starts
		To get up and running, follow our quick starts. The quick start modules are a series of flows that guide you from setting up your first account in Codefresh, to creating a basic pipeline, and deploying to Kubernetes.
		See XREF TBD

		###Example catalog
		Example catalog
		For those who are familiar with CI/CD, we have an extensive collection of examples, covering several CI and CD scenarios:
		[CI examples]({{site.baseurl}}/docs/example-catalog/ci-examples/)
		[CD examples]({{site.baseurl}}/docs/example-catalog/cd-examples/)

		###Guides
		Guides
		And finally, if you want more meat, dive in to our detailed guides.
		XREF TBD

		-->
		## Codefresh & GitOps with Argo CD
Original file line number	Diff line number	Diff line change
Expand Up		@@ -97,7 +97,7 @@ steps:
		type: "git-clone"
		repo: "anais-codefresh/react-article-display"
		# CF_BRANCH value is auto set when pipeline is triggered
		# Learn more at codefresh.io/docs/docs/codefresh-yaml/variables/
		# Learn more at codefresh.io/docs/docs/pipelinesvariables/
		revision: "${{CF_BRANCH}}"
		git: "github"
		stage: "clone"
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -16,7 +16,7 @@ You can easily create:

		With the parallel execution mode, you can define complex pipelines with fan-in/out configurations capable of matching even the most complicated workflows within an organization.

		>In Codefresh, parallel execution is unrelated to [stages]({{site.baseurl}}/docs/codefresh-yaml/stages/). Stages are only a way to visually organize your pipeline steps. The actual execution is independent from the visual layout in the logs view.
		>In Codefresh, parallel execution is unrelated to [stages]({{site.baseurl}}/docs/pipelines/stages/). Stages are only a way to visually organize your pipeline steps. The actual execution is independent from the visual layout in the logs view.

		Before going any further make sure that you are familiar with the [basics of Codefresh pipelines]({{site.baseurl}}/docs/configure-ci-cd-pipeline/introduction-to-codefresh-pipelines/).

Expand DownExpand Up		@@ -168,7 +168,7 @@ As you can see we have also marked the steps with [stages]({{site.baseurl}}/docs

		### Example: Running multiple test suites in parallel

		All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of [freestyle steps]({{site.baseurl}}/docs/codefresh-yaml/steps/freestyle/) for unit/integration tests.
		All types of steps can by placed inside a parallel phase. Another common use case would be the parallel execution of [freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) for unit/integration tests.

		Let's say that you have a Docker image with a Python back-end and a JavaScript front-end. You could run both types of tests in parallel with the following yaml syntax:

Expand DownExpand Up		@@ -837,7 +837,7 @@ You can now add extra conditions regarding the completion state of specific step
		* Pending
		* Running

		Finished is a shorthand for `success` or `failure` or `skipped`. It is only valid when used in [step dependencies]({{site.baseurl}}/docs/codefresh-yaml/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.
		Finished is a shorthand for `success` or `failure` or `skipped`. It is only valid when used in [step dependencies]({{site.baseurl}}/docs/pipelines/advanced-workflows/#single-step-dependencies), and cannot be used in custom conditions.

		You can mix and match completion states from any other step in your pipeline. Here are some examples:

Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -327,7 +327,7 @@ steps:
		{% endraw %}
		{% endhighlight %}

		You can change this behavior by using the existing [fail_fast property]({{site.baseurl}}/docs/codefresh-yaml/what-is-the-codefresh-yaml/#execution-flow) inside an `on_elected` hook.
		You can change this behavior by using the existing [fail_fast property]({{site.baseurl}}/docs/pipelines/what-is-the-codefresh-yaml/#execution-flow) inside an `on_elected` hook.

		`codefresh.yml`
		{% highlight yaml %}
Expand Down
Original file line number	Diff line number	Diff line change
Expand Up		@@ -16,7 +16,7 @@ Use CI pipelines to:

		* Compile and package code
		* Build Docker images
		* Push Docker images to any [Docker Registry]({{site.baseurl}}/docs/docker-registries/external-docker-registries/)
		* Push Docker images to any [Docker Registry]({{site.baseurl}}/docs/integrations/docker-registries/)
		* Deploy applications/artifacts to VMs, Kubernetes clusters, FTP sites, S3 buckets etc.
		* Run [unit tests]({{site.baseurl}}/docs/testing/unit-tests/), [integration tests]({{site.baseurl}}/docs/testing/integration-tests/), acceptance tests etc.
		* Any custom action that you define
Expand DownExpand Up		@@ -70,7 +70,7 @@ max-width="70%"
		%}


		1. The first step runs under the context of a Node image that prepares the application and runs[unit tests]({{site.baseurl}}/docs/testing/unit-tests/).
		1. The first step runs under the context of a Node image that prepares the application and runs unit tests.
		1. The second step uses an image with s3 command line tools and uploads the test results to a bucket that holds test reports.
		1. The helm step creates a Helm chart and pushes it to a Helm repository.

Expand DownExpand Up		@@ -217,7 +217,7 @@ your build step can run commands exactly as you would run them locally (e.g. `np
		1. Finally, `/codefresh/volume` is an internal folder name, and you should use `{% raw %}${{CF_VOLUME_PATH}}{% endraw %}` in your codefresh.yml file
		if you really want to reference this folder. You can also reference your project folder as `{% raw %}${{CF_VOLUME_PATH}}/${{CF_REPO_NAME}}{% endraw %}` if you need it.

		Seethe [System Provided Variables]({{site.baseurl}}/docs/pipelines/variables/#system-provided-variables) section for more information.
		See[system variables]({{site.baseurl}}/docs/pipelines/variables/#system-variables) for more information.

		### Working with Docker inside a Codefresh pipeline

Expand DownExpand Up		@@ -256,7 +256,7 @@ CollectAllMyDeps:
		commands:
		- pip install .
		```
		For the plugins in the [Step Marketplace](https://codefresh.io/steps/) we already give an example of the YAML part that must be included in your pipeline:
		For the plugins in the [Step Marketplace](https://codefresh.io/steps/){:target="\_blank"} we already give an example of the YAML part that must be included in your pipeline:

		{% include
		image.html
Expand All		@@ -273,8 +273,7 @@ Each plugin also defines its input/output in the form of environment variables a
		### Creating Docker images dynamically as build tools


		Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that [freestyle pipeline steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) are just a series of commands that run inside the context of a Docker container. In most cases the images used
		for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or [private Docker registries]({{site.baseurl}}/docs/docker-registries/external-docker-registries/).
		Now we reach one of the most powerful features of Codefresh pipelines. We have already seen that [freestyle steps]({{site.baseurl}}/docs/pipelines/steps/freestyle/) in pipelines are just a series of commands that run inside the context of a Docker container. In most cases the images used for the freestyle steps are known in advance and come from public (e.g. Dockerhub) or [private Docker registries]({{site.baseurl}}/docs/integrations/docker-registries/).

		Codefresh is one the few CI/CD solutions that not only offers easy Docker registry integration
		accessible to all pipelines
Expand DownExpand Up		@@ -308,7 +307,7 @@ inside the pipeline they are actually needed. This ensures that both the applica

		### How caching works in Codefresh

		Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our [caching guide]({{site.baseurl}}/docs/configure-ci-cd-pipeline/pipeline-caching/) for more details.
		Codefresh employs several caching mechanisms for both Dockerized and non-dockerized applications. The shared volume is also cached behind the scenes automatically. See our [caching guide]({{site.baseurl}}/docs/pipelines/pipeline-caching/) for more details.

		### Calling other pipelines

Expand Down