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-
-`cohortextractor` is an action provided by the OpenSAFELY framework. Every pipeline will start with this as its first action.
-It is used to convert the study definition into an actual analysis-ready dataset based on dummy or real data.
-
-The dataset it creates can be either:
-
-* A dummy dataset used for developing and testing analysis code on the user's own machine.
-  Users have control over the characteristics of each dummy variable, which are defined inside the study definition.
-* A real dataset created from the OpenSAFELY database, used for the analysis proper.
-  Real datasets never leave the secure server, only summary data and other outputs that are derived from them can be released (after disclosivity checks).
-
-`cohortextractor` also performs some other tasks, like creating [Measures](measures.md), and many of the functions that are needed within a study definition script.
-
-
-## Installing `cohortextractor`
-
-**In most cases you won't need to install `cohortextractor` directly**.
-Instead, you create an action in your [project.yaml](actions-pipelines.md#project-yaml-format) file and run it via `opensafely run <action>`.
-This will use `cohortextractor` via a Docker image rather than a local installation.
-
-<details>
-  <summary>If you need to install, follow these instructions</summary>
-
-
-Go to the Anaconda prompt and run the following command (or use another method to install the module if you know how):
-
-```
-pip install opensafely-cohort-extractor
-```
-
-To check this has installed successfully, run:
-
-```
-cohortextractor --version
-```
-
-If you need to install a new version, update with:
-
-```
-pip install --upgrade opensafely-cohort-extractor
-```
-
-</details>
-
-
-## Using `cohortextractor` in the `project.yaml`
-
-Typically, you will be writing `cohortextractor` commands inside the `project.yaml` and executing them using the `opensafely run` command.
-The available `cohortextractor` commands and how they are declared in the `project.yaml` are detailed below.
-
-
-### `generate_cohort`
-This is the command used to generate a dataset from the study definition.
-
-A basic `generate_cohort` action looks like this:
-
-```yaml
-generate_study_cohort
-  run: cohortextractor:latest generate_cohort --output-format=csv.gz
-  outputs:
-    highly_sensitive:
-      data: output/input.csv.gz
-```
-
-Running the action with `opensafely run generate_study_population` will create the file `output/input.csv.gz`. This `.csv.gz` file is compressed to reduce storage space and improve performance in the OpenSAFELY backend.
-
-!!! note
-    To view the uncompressed version of the `.csv.gz`, you can use `opensafely unzip output/input.csv.gz`
-
-The size of the dummy dataset is determined by the `population_size` option [in the `project.yaml`](actions-pipelines.md#project-yaml-format).
-
-Running the action again will overwrite the existing dataset with a new one (check the file's _modified_ date) which will contain different data than previously (even if the `study_definition.py` didn't change) because the values are randomly generated.
-
-If you have multiple study definitions in the repo (e.g., `study_definition_cohort1.py` and `study_definition_cohort2.py`) then `generate_cohort` will by default create a dataset for each and name them appropriately (e.g., `input_cohort1.csv.gz` and `input_cohort2.csv.gz`).
-You can restrict the command to produce a single study definition using the `study-definition` option, like this:
-
-
-```yaml
-generate_study_cohort
-  run: cohortextractor:latest generate_cohort --study-definition study_definition_cohort2 --output-format=csv.gz
-  outputs:
-    highly_sensitive:
-      data: output/input_cohort2.csv.gz
-      ### This file is produced in compressed format.
-```
-
-You can change the location of the outputted `.csv.gz` file using the `--output-dir` option, for example `run: cohortextractor:latest generate_cohort --format csv.gz --output-dir output/cohorts`
-
-
----8<-- 'includes/glossary.md'