diff --git a/src/cli.py b/src/cli.py
index 4391de0..ba265fd 100644
--- a/src/cli.py
+++ b/src/cli.py
@@ -1,66 +1,9 @@
-import polars as pl
-import duckdb
-import toml
-import os
-from pathlib import Path
-import seaborn as sns
-import matplotlib.pyplot as plt
-from enum import Enum, auto
 import click
 
-DATA_DIR = Path(os.environ['DATA_MINING_DATA_DIR'])
-APP_DIR = Path(os.environ['DATA_MINING_APP_DIR'])
-
-db = duckdb.connect(str(DATA_DIR / 'project.duckdb'))
-
 @click.group()
 def cli():
     ...
 
-
-class PlotName(str, Enum):
-    TitleLength = "title_len"
-    OutletStories = "outlet_stories"
-
-@cli.command()
-@click.option('-n', '--name', required=True, type=click.Choice(PlotName))
-@click.option('-o', '--output', required=False, type=click.Path())
-def plot(name: PlotName, output: Path):
-    output = output if output else APP_DIR / f'docs/{name}.png'
-    if name == PlotName.TitleLength:
-        fig, ax = plt.subplots(1,1)
-        data = db.sql("""
-            select
-            length(title) as len
-            from stories
-        """).df()
-        sns.histplot(x=data['len'], bins=50, ax=ax[0])
-        ax[0].set(ylabel="count", xlabel="title length")
-    elif name == PlotName.OutletStories:
-
-        data = db.sql("""
-            with cte as (
-            select
-                count(1) as stories
-            from stories
-            group by outlet
-            )
-            select
-                row_number() over(order by stories desc) as id
-                ,log(stories) as log_count
-            from cte
-        """).df()
-
-        fig, ax = plt.subplots(1,1)
-        sns.lineplot(x=data['id'], y=data['log_count'], ax=ax)
-        from matplotlib.ticker import ScalarFormatter
-        ax.set(yscale='log', xlabel="outlet", ylabel="log(count of stories)", majorformater=ScalarFormatter)
-        plt.show()
-
-    else:
-        raise NotImplementedError("option unrecognized")
-    plt.savefig(output)
-
 if __name__ == "__main__":
     import scrape
     cli.add_command(scrape.download)
@@ -76,7 +19,8 @@ if __name__ == "__main__":
     cli.add_command(bias.parse)
     cli.add_command(bias.load)
     cli.add_command(bias.normalize)
-    # import mine
-    # cli.add_command(mine.embeddings)
-    # cli.add_command(mine.cluster)
+    import mine
+    cli.add_command(mine.embeddings)
+    cli.add_command(mine.cluster)
+    cli.add_command(mine.plot)
     cli()
diff --git a/src/mine.py b/src/mine.py
index 3bc74dd..d7f1d29 100644
--- a/src/mine.py
+++ b/src/mine.py
@@ -2,6 +2,11 @@ from data import data_dir, connect
 import numpy as np
 import sklearn
 from sklearn.cluster import MiniBatchKMeans
+import click
+from pathlib import Path
+import seaborn as sns
+import matplotlib.pyplot as plt
+from enum import Enum, auto
 
 
 @click.command(name="mine:embeddings")
@@ -63,6 +68,49 @@ def cluster():
     len(stories)
     data.shape
 
+class PlotName(str, Enum):
+    TitleLength = "title_len"
+    OutletStories = "outlet_stories"
+
+@click.command(name='mine:plot')
+@click.option('-n', '--name', required=True, type=click.Choice(PlotName))
+@click.option('-o', '--output', required=False, type=click.Path())
+def plot(name: PlotName, output: Path):
+    output = output if output else APP_DIR / f'docs/{name}.png'
+    if name == PlotName.TitleLength:
+        fig, ax = plt.subplots(1,1)
+        data = db.sql("""
+            select
+            length(title) as len
+            from stories
+        """).df()
+        sns.histplot(x=data['len'], bins=50, ax=ax[0])
+        ax[0].set(ylabel="count", xlabel="title length")
+    elif name == PlotName.OutletStories:
+
+        data = db.sql("""
+            with cte as (
+            select
+                count(1) as stories
+            from stories
+            group by outlet
+            )
+            select
+                row_number() over(order by stories desc) as id
+                ,log(stories) as log_count
+            from cte
+        """).df()
+
+        fig, ax = plt.subplots(1,1)
+        sns.lineplot(x=data['id'], y=data['log_count'], ax=ax)
+        from matplotlib.ticker import ScalarFormatter
+        ax.set(yscale='log', xlabel="outlet", ylabel="log(count of stories)", majorformater=ScalarFormatter)
+        plt.show()
+
+    else:
+        raise NotImplementedError("option unrecognized")
+    plt.savefig(output)
+
 def main():
     db.sql("""
         select
@@ -83,17 +131,18 @@ def main():
             ,sum(length(title)) as characters
         from cte
     """).fetchall()
-    """
-    let's calculate the size of the word embeddings stored as a list in the database
-    db.sql("""
-        with cte as (
-        select
-            distinct title
-        from stories
-        )
+    # let's calculate the size of the word embeddings stored as a list in the database
+    # db.sql("""
+    #     with cte as (
+    #     select
+    #         distinct title
+    #     from stories
+    #     )
+    #     ...
+    # """)
 
-    db.sql("""
-        select
-            count(distinct url)
-        from stories
-    """)
+    # db.sql("""
+    #     select
+    #         count(distinct url)
+    #     from stories
+    # """)