diff --git a/.github/workflows/python_package.yml b/.github/workflows/python_package.yml
index daa092a..e7bb2a9 100644
--- a/.github/workflows/python_package.yml
+++ b/.github/workflows/python_package.yml
@@ -13,24 +13,28 @@ jobs:
     strategy:
       matrix:
         python-version: ["3.10", "3.11", "3.12"]
-
     steps:
-      - uses: actions/checkout@v3
+      - uses: actions/checkout@v4
       - name: Set up Python ${{ matrix.python-version }}
         uses: actions/setup-python@v3
         with:
           python-version: ${{ matrix.python-version }}
       - name: Install dependencies
         run: |
-          python -m pip install --upgrade pip
-          pip install flake8
           pip install .[test]
-      - name: Lint with flake8
-        run: |
-          # stop the build if there are Python syntax errors or undefined names
-          flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics
-          # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide
-          flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics
       - name: Test with pytest
         run: |
-          pytest
\ No newline at end of file
+          pytest
+
+  ruff:
+    runs-on: ubuntu-latest
+    steps:
+      - uses: actions/checkout@v4
+      - name: Ruff Lint
+        uses: astral-sh/ruff-action@v1
+        with:
+          args: "check --config pyproject.toml"
+      - name: Ruff Format
+        uses: astral-sh/ruff-action@v1
+        with:
+          args: "format --diff"
\ No newline at end of file
diff --git a/.github/workflows/ruff.yml b/.github/workflows/ruff.yml
deleted file mode 100644
index b268138..0000000
--- a/.github/workflows/ruff.yml
+++ /dev/null
@@ -1,8 +0,0 @@
-name: Ruff
-on: [push, pull_request]
-jobs:
-  ruff:
-    runs-on: ubuntu-latest
-    steps:
-      - uses: actions/checkout@v4
-      - uses: chartboost/ruff-action@v1
diff --git a/CHANGELOG.md b/CHANGELOG.md
index d00ecbc..4396d07 100644
--- a/CHANGELOG.md
+++ b/CHANGELOG.md
@@ -6,6 +6,14 @@ and this project adheres to [Semantic Versioning](https://semver.org/spec/v2.0.0
 
 You should also add project tags for each release in Github, see [Managing releases in a repository](https://docs.github.com/en/repositories/releasing-projects-on-github/managing-releases-in-a-repository).
 
+## [Unreleased]
+### Changed
+- GitHub workflow for linting and formatting uses ruff as a separate job
+
+### Removed
+- GitHub action to run flake8 for linting in build
+
+
 ## [2.0.0] - 2024-05-29
 ### Added
 - Added example auto-built Sphinx documentation in the `docs` folder
diff --git a/docs/conf.py b/docs/conf.py
index 00e9eb6..1980a01 100644
--- a/docs/conf.py
+++ b/docs/conf.py
@@ -12,17 +12,18 @@
 #
 import os
 import sys
-sys.path.insert(0, os.path.abspath('..'))
+
+sys.path.insert(0, os.path.abspath(".."))
 
 
 # -- Project information -----------------------------------------------------
 
-project = 'testdoc'
-copyright = '2023, Luke Ruud'
-author = 'Luke Ruud'
+project = "testdoc"
+copyright = "2023, Luke Ruud"
+author = "Luke Ruud"
 
 # The full version, including alpha/beta/rc tags
-release = '0.1'
+release = "0.1"
 
 
 # -- General configuration ---------------------------------------------------
@@ -30,16 +31,15 @@
 # Add any Sphinx extension module names here, as strings. They can be
 # extensions coming with Sphinx (named 'sphinx.ext.*') or your custom
 # ones.
-extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon"
-]
+extensions = ["sphinx.ext.autodoc", "sphinx.ext.napoleon"]
 
 # Add any paths that contain templates here, relative to this directory.
-templates_path = ['_templates']
+templates_path = ["_templates"]
 
 # List of patterns, relative to source directory, that match files and
 # directories to ignore when looking for source files.
 # This pattern also affects html_static_path and html_extra_path.
-exclude_patterns = ['_build', 'Thumbs.db', '.DS_Store']
+exclude_patterns = ["_build", "Thumbs.db", ".DS_Store"]
 
 
 # -- Options for HTML output -------------------------------------------------
@@ -47,9 +47,9 @@
 # The theme to use for HTML and HTML Help pages.  See the documentation for
 # a list of builtin themes.
 #
-html_theme = 'alabaster'
+html_theme = "alabaster"
 
 # Add any paths that contain custom static files (such as style sheets) here,
 # relative to this directory. They are copied after the builtin static files,
 # so a file named "default.css" will overwrite the builtin "default.css".
-html_static_path = ['_static']
\ No newline at end of file
+html_static_path = ["_static"]
diff --git a/notebooks/word_count_prototype.ipynb b/notebooks/word_count_prototype.ipynb
index 25fb9a2..02f797b 100644
--- a/notebooks/word_count_prototype.ipynb
+++ b/notebooks/word_count_prototype.ipynb
@@ -16,7 +16,7 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "# This cell imports packages and defines any experimental set-up \n",
+    "# This cell imports packages and defines any experimental set-up\n",
     "# Try to make experimental parameters easy for others to to find by including them in a few cells at the beginning of your notebook\n",
     "import logging\n",
     "from pathlib import Path\n",
@@ -42,45 +42,45 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "INFO:cdstemplate.word_count:Adding 181 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 122 word types\n",
-      "INFO:cdstemplate.word_count:Adding 189 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 105 word types\n",
-      "INFO:cdstemplate.word_count:Adding 180 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 92 word types\n",
-      "INFO:cdstemplate.word_count:Adding 192 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 95 word types\n",
-      "INFO:cdstemplate.word_count:Adding 246 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 83 word types\n",
-      "INFO:cdstemplate.word_count:Adding 189 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 83 word types\n",
       "INFO:cdstemplate.word_count:Adding 258 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 82 word types\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 113 word types\n",
+      "INFO:cdstemplate.word_count:Adding 192 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 111 word types\n",
       "INFO:cdstemplate.word_count:Adding 204 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 57 word types\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 69 word types\n",
+      "INFO:cdstemplate.word_count:Adding 181 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 97 word types\n",
       "INFO:cdstemplate.word_count:Adding 197 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 90 word types\n",
-      "INFO:cdstemplate.word_count:Adding 180 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 74 word types\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 103 word types\n",
+      "INFO:cdstemplate.word_count:Adding 189 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 88 word types\n",
+      "INFO:cdstemplate.word_count:Adding 189 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 79 word types\n",
       "INFO:cdstemplate.word_count:Adding 182 token(s) case insensitively\n",
-      "INFO:cdstemplate.word_count:Vocabulary size increased by 66 word types\n"
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 75 word types\n",
+      "INFO:cdstemplate.word_count:Adding 180 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 76 word types\n",
+      "INFO:cdstemplate.word_count:Adding 180 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 69 word types\n",
+      "INFO:cdstemplate.word_count:Adding 246 token(s) case insensitively\n",
+      "INFO:cdstemplate.word_count:Vocabulary size increased by 69 word types\n"
      ]
     },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "Tokenizing file: ../data/gutenberg/austen-persuasion.txt\n",
-      "Tokenizing file: ../data/gutenberg/chesterton-ball.txt\n",
-      "Tokenizing file: ../data/gutenberg/chesterton-brown.txt\n",
-      "Tokenizing file: ../data/gutenberg/carroll-alice.txt\n",
-      "Tokenizing file: ../data/gutenberg/bryant-stories.txt\n",
-      "Tokenizing file: ../data/gutenberg/burgess-busterbrown.txt\n",
       "Tokenizing file: ../data/gutenberg/blake-poems.txt\n",
+      "Tokenizing file: ../data/gutenberg/carroll-alice.txt\n",
       "Tokenizing file: ../data/gutenberg/bible-kjv.txt\n",
+      "Tokenizing file: ../data/gutenberg/austen-persuasion.txt\n",
       "Tokenizing file: ../data/gutenberg/chesterton-thursday.txt\n",
+      "Tokenizing file: ../data/gutenberg/burgess-busterbrown.txt\n",
+      "Tokenizing file: ../data/gutenberg/chesterton-ball.txt\n",
+      "Tokenizing file: ../data/gutenberg/austen-emma.txt\n",
+      "Tokenizing file: ../data/gutenberg/chesterton-brown.txt\n",
       "Tokenizing file: ../data/gutenberg/austen-sense.txt\n",
-      "Tokenizing file: ../data/gutenberg/austen-emma.txt\n"
+      "Tokenizing file: ../data/gutenberg/bryant-stories.txt\n"
      ]
     }
    ],
@@ -143,12 +143,12 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>616</th>\n",
+       "      <th>52</th>\n",
        "      <td>\"Drop</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>91</th>\n",
+       "      <th>361</th>\n",
        "      <td>\"ELLIOT</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -158,12 +158,12 @@
        "      <td>1</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>475</th>\n",
+       "      <th>930</th>\n",
        "      <td>\"It's</td>\n",
        "      <td>2</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>484</th>\n",
+       "      <th>938</th>\n",
        "      <td>\"No,</td>\n",
        "      <td>1</td>\n",
        "    </tr>\n",
@@ -173,11 +173,11 @@
       ],
       "text/plain": [
        "       token  count\n",
-       "616    \"Drop      1\n",
-       "91   \"ELLIOT      1\n",
+       "52     \"Drop      1\n",
+       "361  \"ELLIOT      1\n",
        "571       \"I      1\n",
-       "475    \"It's      2\n",
-       "484     \"No,      1"
+       "930    \"It's      2\n",
+       "938     \"No,      1"
       ]
      },
      "metadata": {},
@@ -222,27 +222,27 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>29</th>\n",
+       "      <th>18</th>\n",
        "      <td>the</td>\n",
        "      <td>127</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>10</th>\n",
+       "      <th>13</th>\n",
        "      <td>of</td>\n",
        "      <td>72</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>38</th>\n",
+       "      <th>10</th>\n",
        "      <td>and</td>\n",
        "      <td>62</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>16</th>\n",
+       "      <th>25</th>\n",
        "      <td>a</td>\n",
        "      <td>43</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>162</th>\n",
+       "      <th>34</th>\n",
        "      <td>to</td>\n",
        "      <td>38</td>\n",
        "    </tr>\n",
@@ -252,27 +252,27 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>259</th>\n",
+       "      <th>576</th>\n",
        "      <td>one</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>348</th>\n",
+       "      <th>154</th>\n",
        "      <td>what</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>376</th>\n",
+       "      <th>184</th>\n",
        "      <td>when</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>704</th>\n",
+       "      <th>275</th>\n",
        "      <td>firmament</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>147</th>\n",
+       "      <th>439</th>\n",
        "      <td>white</td>\n",
        "      <td>3</td>\n",
        "    </tr>\n",
@@ -283,17 +283,17 @@
       ],
       "text/plain": [
        "         token  count\n",
-       "29         the    127\n",
-       "10          of     72\n",
-       "38         and     62\n",
-       "16           a     43\n",
-       "162         to     38\n",
+       "18         the    127\n",
+       "13          of     72\n",
+       "10         and     62\n",
+       "25           a     43\n",
+       "34          to     38\n",
        "..         ...    ...\n",
-       "259        one      3\n",
-       "348       what      3\n",
-       "376       when      3\n",
-       "704  firmament      3\n",
-       "147      white      3\n",
+       "576        one      3\n",
+       "154       what      3\n",
+       "184       when      3\n",
+       "275  firmament      3\n",
+       "439      white      3\n",
        "\n",
        "[100 rows x 2 columns]"
       ]
@@ -325,31 +325,26 @@
     },
     {
      "data": {
-      "image/png": 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",
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",
       "text/plain": [
-       "<Figure size 1440x1080 with 1 Axes>"
+       "<Figure size 2000x1500 with 1 Axes>"
       ]
      },
-     "metadata": {
-      "needs_background": "light"
-     },
+     "metadata": {},
      "output_type": "display_data"
     }
    ],
    "source": [
     "# Make the plot and customize how it is displayed\n",
-    "plt.figure(figsize= (20, 15))\n",
-    "fig = sns.barplot(data = top_words_df, x = \"count\", y = \"token\")\n",
+    "plt.figure(figsize=(20, 15))\n",
+    "fig = sns.barplot(data=top_words_df, x=\"count\", y=\"token\")\n",
     "fig.set_title(\"Top 100 Most Frequent Words in a Subset of Project Gutenberg Texts\")"
    ]
   }
  ],
  "metadata": {
-  "interpreter": {
-   "hash": "8db04dca66b9396af2474eca4189d3a8ab65d348a7a173a34f354ffe25d5d9d4"
-  },
   "kernelspec": {
-   "display_name": "Python 3.10.4 ('template')",
+   "display_name": "template",
    "language": "python",
    "name": "python3"
   },
@@ -363,7 +358,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.10.4"
+   "version": "3.10.13"
   },
   "orig_nbformat": 4
  },
diff --git a/pyproject.toml b/pyproject.toml
index 1d8a32b..e7bc5e6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -32,7 +32,8 @@ dependencies = [
 # Extra dependencies only needed for running tests go here
 test = ["pytest"]
 
-# Dependencies that are useful only to developers, like an autoformatter and support for visualizations in jupyter notebooks go here
+# Dependencies that are useful only to developers, like an autoformatter and
+# support for visualizations in jupyter notebooks go here
 dev = [
     "ruff",
     "jupyter",
@@ -45,3 +46,8 @@ dev = [
 # The value must be of the form "<package_name>:<module_name>.<function>"
 [project.scripts]
 corpus-counter = "cdstemplate:corpus_counter_script.main_cli"
+
+# Add customizations to the Ruff linter as described on https://docs.astral.sh/ruff/configuration/
+[tool.ruff]
+# Override ruff's default so that lines aren't more than 120 characters
+line-length = 119
diff --git a/src/cdstemplate/corpus_counter_script.py b/src/cdstemplate/corpus_counter_script.py
index 404550d..f0f3ae7 100644
--- a/src/cdstemplate/corpus_counter_script.py
+++ b/src/cdstemplate/corpus_counter_script.py
@@ -1,6 +1,7 @@
 """An example of a script you can run. It tokenizes an folder of input documents and
 writes the corpus counts to a user-specified CSV file
 """
+
 # Import modules, functions and classes from external libraries
 import argparse
 import logging
@@ -11,19 +12,16 @@
 
 logger = logging.getLogger(__name__)
 
+
 def main_cli():
-    """A wrapper function that defines command line arguments and help messages for 
-    when the user wants run this module's code as a script. 
+    """A wrapper function that defines command line arguments and help messages for
+    when the user wants run this module's code as a script.
     """
     # The argument parser gives nice ways to include help message and specify which arguments
     # are required or optional, see https://docs.python.org/3/library/argparse.html#prog for usage instructions
-    parser = argparse.ArgumentParser(
-        description="A script to generate counts of tokens in a corpus"
-    )
+    parser = argparse.ArgumentParser(description="A script to generate counts of tokens in a corpus")
 
-    parser.add_argument(
-        "csv", help="Path to the output CSV storing token counts. Required."
-    )
+    parser.add_argument("csv", help="Path to the output CSV storing token counts. Required.")
 
     parser.add_argument(
         "documents",
@@ -64,5 +62,5 @@ def main(csv_out, documents, case_insensitive=False):
 
 # The entry point of your script - if a user runs it from the command line, for example using `python -m <package>.<module>`
 # or `python <script_path>.py`, this is what will be run.
-if __name__ == "__main__":    
+if __name__ == "__main__":
     main_cli()
diff --git a/src/cdstemplate/utils.py b/src/cdstemplate/utils.py
index 320e122..79fa1a0 100644
--- a/src/cdstemplate/utils.py
+++ b/src/cdstemplate/utils.py
@@ -1,11 +1,10 @@
-"""A module for important set-up and configuration functionality, but doesn't implement the library's key features.
-"""
+"""A module for important set-up and configuration functionality, but doesn't implement the library's key features."""
+
 import logging
 
 
 def configure_logging():
-    """A helper method that configures logging, usable by any script in this library.
-    """
+    """A helper method that configures logging, usable by any script in this library."""
     logging.basicConfig(
         level=logging.DEBUG,
         format="%(levelname)s : %(asctime)s : %(name)s : %(message)s",
diff --git a/src/cdstemplate/word_count.py b/src/cdstemplate/word_count.py
index 8037dce..ce2e561 100644
--- a/src/cdstemplate/word_count.py
+++ b/src/cdstemplate/word_count.py
@@ -1,6 +1,7 @@
 """An example of an module with functions and a class that can be imported once the package is installed.
 This module provides operations for tokenization and tracking cumulative word counts in a set of documents.
 """
+
 from collections import Counter
 import logging
 import re
@@ -29,8 +30,7 @@ def tokenize(text, pattern=r"\s"):
 
 
 class CorpusCounter:
-    """A simple class object that tracks document and token counts in a corpus.
-    """
+    """A simple class object that tracks document and token counts in a corpus."""
 
     def __init__(self, tokenization_pattern=r"\s", case_insensitive=False):
         """Constructor instantiates with empty counters
@@ -89,16 +89,12 @@ def get_token_count(self, token):
         return self.token_counter[token]
 
     def get_vocab_size(self):
-        """Returns vocabulary size (number of unique tokens)
-        """
+        """Returns vocabulary size (number of unique tokens)"""
         return len(self.token_counter)
 
     def get_token_counts_as_dataframe(self):
-        """Returns the token counts of the corpus as a Pandas DataFrame with columns 'token', 'count'
-        """
-        dataframe = pd.DataFrame.from_records(
-            list(self.token_counter.items()), columns=["token", "count"]
-        )
+        """Returns the token counts of the corpus as a Pandas DataFrame with columns 'token', 'count'"""
+        dataframe = pd.DataFrame.from_records(list(self.token_counter.items()), columns=["token", "count"])
         dataframe = dataframe.sort_values("token")
         return dataframe
 
@@ -111,4 +107,3 @@ def save_token_counts(self, csv_file):
         """
         logger.info("Saving token counts to %s", csv_file)
         self.get_token_counts_as_dataframe().to_csv(csv_file, index=False, header=True)
-
diff --git a/tests/test_word_count.py b/tests/test_word_count.py
index aa91336..ae80dd0 100644
--- a/tests/test_word_count.py
+++ b/tests/test_word_count.py
@@ -2,12 +2,15 @@
 
 In pytest, each individual test is a python function that starts with `test`.
 """
+
 # Import your library for testing
 from cdstemplate import word_count
 
 
 def test_tokenize_document():
-    my_document = "It was all very well to say `Drink me,' but the wise little Alice was not going to do that in a hurry."
+    my_document = (
+        "It was all very well to say `Drink me,' but the wise little Alice was not going to do that in a hurry."
+    )
 
     expected_tokens = [
         "It",