(docs): removing shell indicators

docs/cloud/features/scheduler/airflow.md

@@ -35,7 +35,7 @@ Start by installing the `tobiko-cloud-scheduler-facade` library in your Airflow
 Make sure to include the `[airflow]` extra in the installation command:
 
 ``` bash
-$ pip install tobiko-cloud-scheduler-facade[airflow]
+pip install tobiko-cloud-scheduler-facade[airflow]
 ```
 
 !!! info "Mac Users"

docs/cloud/features/scheduler/dagster.md

@@ -48,7 +48,7 @@ dependencies = [
 And then install it into the Python environment used by your Dagster project:
 
 ```sh
-$ pip install -e '.[dev]'
+pip install -e '.[dev]'
 ```
 
 ### Connect Dagster to Tobiko Cloud

docs/cloud/features/security/single_sign_on.md

@@ -145,7 +145,7 @@ Here is what you will see if you are accessing Tobiko Cloud via Okta. Click on t
 You can see what the status of your session is with the `status` command:
 
 ``` bash
-$ tcloud auth status
+tcloud auth status
 ```
 
 
@@ -156,7 +156,7 @@ $ tcloud auth status
 Run the `login` command to begin the login process:
 
 ``` bash
-$ tcloud auth login
+tcloud auth login
 ```
 
 ![tcloud_login](./single_sign_on/tcloud_login.png)
@@ -183,11 +183,11 @@ Current Tobiko Cloud SSO session expires in 1439 minutes
 In order to delete your session information you can use the log out command:
 
 ``` bash
-> tcloud auth logout
-Logged out of Tobiko Cloud
+tcloud auth logout
+# Logged out of Tobiko Cloud
 
-> tcloud auth status
-Not currently authenticated
+tcloud auth status
+# Not currently authenticated
 ```
 
 ![tcloud_logout](./single_sign_on/tcloud_logout.png)

docs/cloud/features/xdb_diffing.md

@@ -47,7 +47,7 @@ Then, specify each table's gateway in the `table_diff` command with this syntax:
 For example, we could diff the `landing.table` table across `bigquery` and `snowflake` gateways like this:
 
 ```sh
-$ tcloud sqlmesh table_diff 'bigquery|landing.table:snowflake|landing.table'
+tcloud sqlmesh table_diff 'bigquery|landing.table:snowflake|landing.table'
 ```
 
 This syntax tells SQLMesh to use the cross-database diffing algorithm instead of the normal within-database diffing algorithm.

docs/concepts/state.md

@@ -92,7 +92,7 @@ The state file is a simple `json` file that looks like:
 You can export a specific environment like so:
 
 ```sh
-$ sqlmesh state export --environment my_dev -o my_dev_state.json
+sqlmesh state export --environment my_dev -o my_dev_state.json
 ```
 
 Note that every snapshot that is part of the environment will be exported, not just the differences from `prod`. The reason for this is so that the environment can be fully imported elsewhere without any assumptions about which snapshots are already present in state.
@@ -102,7 +102,7 @@ Note that every snapshot that is part of the environment will be exported, not j
 You can export local state like so:
 
 ```bash
-$ sqlmesh state export --local -o local_state.json
+sqlmesh state export --local -o local_state.json
 ```
 
 This essentially just exports the state of the local context which includes local changes that have not been applied to any virtual data environments.
@@ -174,10 +174,11 @@ If your project has [multiple gateways](../guides/configuration.md#gateways) wit
 
 ```bash
 # state export
-$ sqlmesh --gateway <gateway> state export -o state.json
-
+sqlmesh --gateway <gateway> state export -o state.json
+```
+```bash
 # state import
-$ sqlmesh --gateway <gateway> state import -i state.json
+sqlmesh --gateway <gateway> state import -i state.json
 ```
 
 ## Version Compatibility

docs/guides/configuration.md

@@ -269,7 +269,7 @@ gateways:
 We can override the `dummy_pw` value with the true password `real_pw` by creating the environment variable. This example demonstrates creating the variable with the bash `export` function:
 
 ```bash
-$ export SQLMESH__GATEWAYS__MY_GATEWAY__CONNECTION__PASSWORD="real_pw"
+export SQLMESH__GATEWAYS__MY_GATEWAY__CONNECTION__PASSWORD="real_pw"
 ```
 
 After the initial string `SQLMESH__`, the environment variable name components move down the key hierarchy in the YAML specification: `GATEWAYS` --> `MY_GATEWAY` --> `CONNECTION` --> `PASSWORD`.
@@ -1492,7 +1492,7 @@ Example enabling debug mode for the CLI command `sqlmesh plan`:
 === "Bash"
 
     ```bash
-    $ SQLMESH_DEBUG=1 sqlmesh plan
+    SQLMESH_DEBUG=1 sqlmesh plan
     ```
 
 === "MS Powershell"

docs/guides/migrations.md

@@ -28,7 +28,7 @@ SQLMeshError: SQLMesh (local) is using version '1' which is behind '2' (remote).
 The project metadata can be migrated to the latest metadata format using SQLMesh's migrate command.
 
 ```bash
-> sqlmesh migrate
+sqlmesh migrate
 ```
 
 Migration should be issued manually by a single user and the migration will affect all users of the project. 

docs/integrations/dbt.md

@@ -19,19 +19,19 @@ Therefore, SQLMesh is packaged with multiple "extras," which you may optionally
 At minimum, using the SQLMesh dbt adapter requires installing the dbt extra:
 
 ```bash
-> pip install "sqlmesh[dbt]"
+pip install "sqlmesh[dbt]"
 ```
 
 If your project uses any SQL execution engine other than DuckDB, you must install the extra for that engine. For example, if your project runs on the Postgres SQL engine:
 
 ```bash
-> pip install "sqlmesh[dbt,postgres]"
+pip install "sqlmesh[dbt,postgres]"
 ```
 
 If you would like to use the [SQLMesh Browser UI](../guides/ui.md) to view column-level lineage, include the `web` extra:
 
 ```bash
-> pip install "sqlmesh[dbt,web]"
+pip install "sqlmesh[dbt,web]"
 ```
 
 Learn more about [SQLMesh installation and extras here](../installation.md#install-extras).
@@ -41,7 +41,7 @@ Learn more about [SQLMesh installation and extras here](../installation.md#insta
 Prepare an existing dbt project to be run by SQLMesh by executing the `sqlmesh init` command *within the dbt project root directory* and with the `dbt` template option:
 
 ```bash
-$ sqlmesh init -t dbt
+sqlmesh init -t dbt
 ```
 
 This will create a file called `sqlmesh.yaml` containing the [default model start date](../reference/model_configuration.md#model-defaults). This configuration file is a minimum starting point for enabling SQLMesh to work with your DBT project.
@@ -247,8 +247,8 @@ Instead, SQLMesh provides predefined time macro variables that can be used in th
 For example, the SQL `WHERE` clause with the "ds" column goes in a new jinja block gated by `{% if sqlmesh_incremental is defined %}` as follows:
 
 ```bash
->   WHERE
->     ds BETWEEN '{{ start_ds }}' AND '{{ end_ds }}'
+  WHERE
+    ds BETWEEN '{{ start_ds }}' AND '{{ end_ds }}'
 ```
 
 `{{ start_ds }}` and `{{ end_ds }}` are the jinja equivalents of SQLMesh's `@start_ds` and `@end_ds` predefined time macro variables. See all [predefined time variables](../concepts/macros/macro_variables.md) available in jinja.

docs/integrations/dlt.md

@@ -8,7 +8,7 @@ SQLMesh enables efforless project generation using data ingested through [dlt](h
 To load data from a dlt pipeline into SQLMesh, ensure the dlt pipeline has been run or restored locally. Then simply execute the sqlmesh `init` command *within the dlt project root directory* using the `dlt` template option and specifying the pipeline's name with the `dlt-pipeline` option:
 
 ```bash
-$ sqlmesh init -t dlt --dlt-pipeline <pipeline-name> dialect
+sqlmesh init -t dlt --dlt-pipeline <pipeline-name> dialect
 ```
 
 This will create the configuration file and directories, which are found in all SQLMesh projects:
@@ -33,7 +33,7 @@ SQLMesh will also automatically generate models to ingest data from the pipeline
 The default location for dlt pipelines is `~/.dlt/pipelines/<pipeline_name>`. If your pipelines are in a [different directory](https://dlthub.com/docs/general-usage/pipeline#separate-working-environments-with-pipelines_dir), use the `--dlt-path` argument to specify the path explicitly:
 
 ```bash
-$ sqlmesh init -t dlt --dlt-pipeline <pipeline-name> --dlt-path <pipelines-directory> dialect
+sqlmesh init -t dlt --dlt-pipeline <pipeline-name> --dlt-path <pipelines-directory> dialect
 ```
 
 ### Generating models on demand
@@ -43,25 +43,25 @@ To update the models in your SQLMesh project on demand, use the `dlt_refresh` co
 - **Generate all missing tables**:
 
 ```bash
-$ sqlmesh dlt_refresh <pipeline-name>
+sqlmesh dlt_refresh <pipeline-name>
 ```
 
 - **Generate all missing tables and overwrite existing ones** (use with `--force` or `-f`):
 
 ```bash
-$ sqlmesh dlt_refresh <pipeline-name> --force
+sqlmesh dlt_refresh <pipeline-name> --force
 ```
 
 - **Generate specific dlt tables** (using `--table` or `-t`):
 
 ```bash
-$ sqlmesh dlt_refresh <pipeline-name> --table <dlt-table>
+sqlmesh dlt_refresh <pipeline-name> --table <dlt-table>
 ```
 
 - **Provide the explicit path to the pipelines directory** (using `--dlt-path`):
 
 ```bash
-$ sqlmesh dlt_refresh <pipeline-name> --dlt-path <pipelines-directory>
+sqlmesh dlt_refresh <pipeline-name> --dlt-path <pipelines-directory>
 ```
 
 #### Configuration
@@ -83,7 +83,7 @@ Load package 1728074157.660565 is LOADED and contains no failed jobs
 After the pipeline has run, generate a SQLMesh project by executing:
 
 ```bash
-$ sqlmesh init -t dlt --dlt-pipeline sushi duckdb
+sqlmesh init -t dlt --dlt-pipeline sushi duckdb
 ```
 
 Then the SQLMesh project is all set up. You can then proceed to run the SQLMesh `plan` command to ingest the dlt pipeline data and populate the SQLMesh tables:

docs/integrations/engines/bigquery.md

@@ -22,7 +22,7 @@ Follow the [quickstart installation guide](../../installation.md) up to the step
 Instead of installing just SQLMesh core, we will also include the BigQuery engine libraries:
 
 ```bash
-> pip install "sqlmesh[bigquery]"
+pip install "sqlmesh[bigquery]"
 ```
 
 ### Install Google Cloud SDK
@@ -35,19 +35,19 @@ Follow these steps to install and configure the Google Cloud SDK on your compute
 - Unpack the downloaded file with the `tar` command:
 
     ```bash
-    > tar -xzvf google-cloud-cli-{SYSTEM_SPECIFIC_INFO}.tar.gz
+    tar -xzvf google-cloud-cli-{SYSTEM_SPECIFIC_INFO}.tar.gz
     ```
 
 - Run the installation script:
 
     ```bash
-    > ./google-cloud-sdk/install.sh
+    ./google-cloud-sdk/install.sh
     ```
 
 - Reload your shell profile (e.g., for zsh):
 
     ```bash
-    > source $HOME/.zshrc
+    source $HOME/.zshrc
     ```
 
 - Run [`gcloud init` to setup authentication](https://cloud.google.com/sdk/gcloud/reference/init)
@@ -114,7 +114,7 @@ The output will look something like this:
 We've verified our connection, so we're ready to create and execute a plan in BigQuery:
 
 ```bash
-> sqlmesh plan
+sqlmesh plan
 ```
 
 ### View results in BigQuery Console

docs/reference/configuration.md

@@ -277,33 +277,33 @@ Example enabling debug mode for the CLI command `sqlmesh plan`:
 === "Bash"
 
     ```bash
-    $ sqlmesh --debug plan
+    sqlmesh --debug plan
     ```
 
     ```bash
-    $ SQLMESH_DEBUG=1 sqlmesh plan
+    SQLMESH_DEBUG=1 sqlmesh plan
     ```
 
 === "MS Powershell"
 
     ```powershell
-    PS> sqlmesh --debug plan
+    sqlmesh --debug plan
     ```
 
     ```powershell
-    PS> $env:SQLMESH_DEBUG=1
-    PS> sqlmesh plan
+    $env:SQLMESH_DEBUG=1
+    sqlmesh plan
     ```
 
 === "MS CMD"
 
     ```cmd
-    C:\> sqlmesh --debug plan
+    sqlmesh --debug plan
     ```
 
     ```cmd
-    C:\> set SQLMESH_DEBUG=1
-    C:\> sqlmesh plan
+    set SQLMESH_DEBUG=1
+    sqlmesh plan
     ```
 
 ## Runtime Environment