diff --git a/.github/workflows/github-ci.yml b/.github/workflows/github-ci.yml
index 2cbd029b..c3a1b951 100644
--- a/.github/workflows/github-ci.yml
+++ b/.github/workflows/github-ci.yml
@@ -26,9 +26,9 @@ jobs:
     steps:
       - uses: actions/checkout@v2
       # Required for pre-commit
-      - run: pip3 install bandit cpplint docker mypy pylint types-toml
+      - run: pip3 install bandit black cpplint docker mypy pylint types-toml
       - run: sudo snap install shfmt
-      - run: sudo apt install black shellcheck
+      - run: sudo apt install shellcheck
       # NOTE: This is deprecated in favor of pre-commit.ci
       - uses: pre-commit/action@v2.0.3
         with:
diff --git a/he-samples/examples/logistic-regression/datasets/generate_data.py b/he-samples/examples/logistic-regression/datasets/generate_data.py
index 0b346981..7109ccc4 100644
--- a/he-samples/examples/logistic-regression/datasets/generate_data.py
+++ b/he-samples/examples/logistic-regression/datasets/generate_data.py
@@ -21,21 +21,21 @@
 def doTrain(Xtrain, ytrain, Xtest, ytest, epochs=10, verbose=False):
     """Efficient logistic regression training
 
-  Efficient logistic regression training by Bergamaschi et. al (https://eprint.iacr.org/2019/425)
-  Provides a fast/efficient logistic regression training with cleartext data
-
-  Args:
-    Xtrain (numpy.array): Training data samples in numpy 2d array
-    ytrain (numpy.array): Target of training set
-    Xtest (numpy.array): Test data samples for validation in numpy 2d array
-    ytest (numpy.array): Target of test set for validation
-    epochs (int): Number of training epochs. Default = 10
-    verbose (bool): Set to True for printing training progress. Default = False
-
-  Returns:
-    bias (float): bias of logistic regression trained model
-    weights (numpy.array): weights of logistic regression trained model
-  """
+    Efficient logistic regression training by Bergamaschi et. al (https://eprint.iacr.org/2019/425)
+    Provides a fast/efficient logistic regression training with cleartext data
+
+    Args:
+      Xtrain (numpy.array): Training data samples in numpy 2d array
+      ytrain (numpy.array): Target of training set
+      Xtest (numpy.array): Test data samples for validation in numpy 2d array
+      ytest (numpy.array): Target of test set for validation
+      epochs (int): Number of training epochs. Default = 10
+      verbose (bool): Set to True for printing training progress. Default = False
+
+    Returns:
+      bias (float): bias of logistic regression trained model
+      weights (numpy.array): weights of logistic regression trained model
+    """
 
     v = lrb.get_initweight(Xtrain, ytrain)
     w = v
@@ -45,7 +45,7 @@ def doTrain(Xtrain, ytrain, Xtest, ytest, epochs=10, verbose=False):
         print("== Logistic Regression Training ==")
     for i in range(epochs):
         learning_rate = 10.0 / ((i + 1) + 1)
-        new_lmbda = (1.0 + np.sqrt(1 + 4 * lmbda ** 2)) / 2.0
+        new_lmbda = (1.0 + np.sqrt(1 + 4 * lmbda**2)) / 2.0
         smoothing = (1 - lmbda) / new_lmbda
         lmbda = new_lmbda
 
@@ -76,14 +76,14 @@ class DataMode(Enum):
 def saveData(dataName, X, y, datamode: DataMode = DataMode.eval):
     """Save data samples to csv file
 
-  Stores the data samples to be used for the LRHE example.
+    Stores the data samples to be used for the LRHE example.
 
-  Args:
-    dataName (str): data name prefix
-    X (numpy.array): data samples to be stored in 2d numpy array
-    y (numpy.array): targets to be stored in a 1d numpy array
-    datamode (DataMode): Determines the suffix [train, test, eval]. Default = DataMode.eval
-  """
+    Args:
+      dataName (str): data name prefix
+      X (numpy.array): data samples to be stored in 2d numpy array
+      y (numpy.array): targets to be stored in a 1d numpy array
+      datamode (DataMode): Determines the suffix [train, test, eval]. Default = DataMode.eval
+    """
     nFeatures = X.shape[1]
     suffix = datamode.name
     features = [f"feature_{i}" for i in range(nFeatures)]
@@ -101,13 +101,13 @@ def saveData(dataName, X, y, datamode: DataMode = DataMode.eval):
 def saveModel(dataName, b, w):
     """Save logistic regression model to csv file
 
-  Stores the model to be used for the LRHE example.
+    Stores the model to be used for the LRHE example.
 
-  Args:
-    dataName (str): data name prefix
-    b (float): bias of LR model
-    w (numpy.array): weights of LR model
-  """
+    Args:
+      dataName (str): data name prefix
+      b (float): bias of LR model
+      w (numpy.array): weights of LR model
+    """
     lr_model = np.concatenate(([b], w), axis=0).tolist()
     with open(f"{dataName}_lrmodel.csv", "w") as csvfile:
         writer = csv.writer(csvfile, delimiter=",")
@@ -117,23 +117,23 @@ def saveModel(dataName, b, w):
 def generateSynData(nSamples, nFeatures):
     """Generate synthetic dataset
 
-  Generates synthetic datasets with the use of sklearn.datasets.make_classification.
-  Splits the entire dataset into train, test and eval with the ratio of 2:1:1.
+    Generates synthetic datasets with the use of sklearn.datasets.make_classification.
+    Splits the entire dataset into train, test and eval with the ratio of 2:1:1.
 
-  Note that this will generate purposely well-fitted data samples for LR training
+    Note that this will generate purposely well-fitted data samples for LR training
 
-  Args:
-    nSamples (int): number of data samples
-    nFeatures (int): number of features
+    Args:
+      nSamples (int): number of data samples
+      nFeatures (int): number of features
 
-  Returns:
-    Xtrain (numpy.array): train dataset. 1/4 of nSamples
-    ytrain (numpy.array): train target
-    Xtest (numpy.array): test dataset. 1/4 of nSamples
-    ytest (numpy.array): test target
-    Xeval (numpy.array): eval dataset. 1/4 of nSamples
-    yeval (numpy.array): eval target
-  """
+    Returns:
+      Xtrain (numpy.array): train dataset. 1/4 of nSamples
+      ytrain (numpy.array): train target
+      Xtest (numpy.array): test dataset. 1/4 of nSamples
+      ytest (numpy.array): test target
+      Xeval (numpy.array): eval dataset. 1/4 of nSamples
+      yeval (numpy.array): eval target
+    """
 
     data = sklearn.datasets.make_classification(
         n_samples=nSamples,
diff --git a/he-samples/examples/logistic-regression/datasets/lr_base.py b/he-samples/examples/logistic-regression/datasets/lr_base.py
index 90fe2ac2..b6d9dbf2 100644
--- a/he-samples/examples/logistic-regression/datasets/lr_base.py
+++ b/he-samples/examples/logistic-regression/datasets/lr_base.py
@@ -21,7 +21,7 @@ def sigmoid_poly3(x):
 
 # 4-degree polynomial representation of log(sigmoid(x)) function, effective in range of [-5, 5]
 def log_sig4(x):
-    return 0.000527 * x ** 4 - 0.0822 * x ** 2 + 0.5 * x - 0.78
+    return 0.000527 * x**4 - 0.0822 * x**2 + 0.5 * x - 0.78
 
 
 # Realign target to -1, 1 and calculate X@y'
diff --git a/kit/commands/check_deps.py b/kit/commands/check_deps.py
index 7bc214ca..7c16180c 100644
--- a/kit/commands/check_deps.py
+++ b/kit/commands/check_deps.py
@@ -53,7 +53,7 @@ def make_from_str(cls, dep_str: str) -> Dep:
 
 
 def version_string_to_tuple(ver_str: str) -> Tuple[int, ...]:
-    """version '10.11.12' -> (10, 11, 12) """
+    """version '10.11.12' -> (10, 11, 12)"""
     try:
         return tuple(int(i) for i in ver_str.split("."))
     except ValueError as e:
diff --git a/kit/commands/docker_build.py b/kit/commands/docker_build.py
index e49263ba..3d4a2411 100644
--- a/kit/commands/docker_build.py
+++ b/kit/commands/docker_build.py
@@ -24,7 +24,6 @@ def try_setup_docker(args):  # pylint: disable=unused-argument
         print("This command is disabled. To enable it install the docker-py dependency")
         print("  pip install docker")
 
-
 else:
 
     def try_setup_docker(args):
diff --git a/kit/commands/init.py b/kit/commands/init.py
index cf0abbe2..a90afc97 100644
--- a/kit/commands/init.py
+++ b/kit/commands/init.py
@@ -19,7 +19,7 @@ class Tags:
 
 
 def file_exists(file: Path) -> bool:
-    """ Wrapper to check if file exists because Path.exists() cannot be mocked
+    """Wrapper to check if file exists because Path.exists() cannot be mocked
     directly due to being used internally by pytest creating some clash"""
     return file.exists()
 
@@ -85,7 +85,7 @@ def append_to_rc(path: Path, content: str) -> None:
 
 
 def get_rc_file() -> Path:
-    """ Return the correct file to add shell commands"""
+    """Return the correct file to add shell commands"""
     active_shell_path = Path(environment["SHELL"]).name
 
     if active_shell_path == "bash":
@@ -138,7 +138,7 @@ def init_hekit(args) -> None:
     # 2-Register hekit link and hekit.py script to enable tab completion
     eval_lines = (
         "if [ -n $(type -p register-python-argcomplete) ]; then\n"
-        '  eval "$(register-python-argcomplete hekit)"\n'
+        ' eval "$(register-python-argcomplete hekit)"\n'
         "fi\n"
     )
     content = "".join([export_line, path_line, eval_lines])
diff --git a/kit/commands/list_cmd.py b/kit/commands/list_cmd.py
index 8f8804b9..368413f7 100644
--- a/kit/commands/list_cmd.py
+++ b/kit/commands/list_cmd.py
@@ -17,8 +17,8 @@
 
 
 class RepoProperties:
-    """ Contains a dictionary with the structure of the repo
-        and widths of the widest component and instance"""
+    """Contains a dictionary with the structure of the repo
+    and widths of the widest component and instance"""
 
     def __init__(self, repo_location: str, separation_spaces: int = _SEP_SPACES):
         # Get the components and instances
@@ -50,7 +50,7 @@ def structure(self) -> Dict[str, List[str]]:
     @staticmethod
     def _repo_struct(path: PathType) -> Dict[str, List[str]]:
         """Return a dictionary with sorted keys as components and values as
-           sorted list of instances"""
+        sorted list of instances"""
         path = Path(path)
         return {component: list_dirs(path / component) for component in list_dirs(path)}
 
diff --git a/kit/tools/healg.py b/kit/tools/healg.py
index 67484f17..92cd80e0 100644
--- a/kit/tools/healg.py
+++ b/kit/tools/healg.py
@@ -43,15 +43,15 @@ def write_primes(start: int, stop: int, outfile=stdout):
 
 def powerset(iterable):
     """Returns a generator.
-       Note that we do not return the empty set.
-       https://docs.python.org/3/library/itertools.html"""
+    Note that we do not return the empty set.
+    https://docs.python.org/3/library/itertools.html"""
     s = list(iterable)
     return chain.from_iterable(combinations(s, r) for r in range(1, len(s) + 1))
 
 
 def find_ms(ps, ds, factorize):
     """Generator returns the p, gen for max m's for p^d"""
-    prime_factors = factorize(p ** d - 1 for p in ps for d in ds)
+    prime_factors = factorize(p**d - 1 for p in ps for d in ds)
     if prime_factors:
         all_factors = tuple(powerset(primes) for primes in prime_factors)
         pd = ((p, d) for p in ps for d in ds)
@@ -62,7 +62,7 @@ def find_ms(ps, ds, factorize):
 
 def str_to_range(s):
     """Parse a string and return a Python range object.
-       This function expects a positive integer."""
+    This function expects a positive integer."""
     if s.isdigit():
         num = int(s)
         return range(num, num + 1)
@@ -128,7 +128,7 @@ def is_prime(self, n):
 
 
 def parse_factor_line(line):
-    """ 'num: f1 f2 f3' -> (num, (f1, f2, f3))"""
+    """'num: f1 f2 f3' -> (num, (f1, f2, f3))"""
     split_line = line.split()  # ['key:', 'v1', 'v2' , ...]
     key = split_line[0]
     if key[-1] != ":":
@@ -140,7 +140,7 @@ def parse_factor_line(line):
 
 def compute_prime_factors(numbers, factor_util="factor"):
     """Return generator. Keys m, Value prime factors.
-       Process out to factor"""
+    Process out to factor"""
 
     numbers = list(numbers)
     if len(numbers) == 0:
@@ -193,8 +193,8 @@ def set_gen_algebras_subparser(subparsers):
 
 
 def phi(prime_factors):
-    """"Euler's totient from prime factors.
-        This function assumes that only primes are passed in."""
+    """ "Euler's totient from prime factors.
+    This function assumes that only primes are passed in."""
     c = Counter(prime_factors)
     return math.prod((p - 1) * p ** (k - 1) for p, k in c.items())
 
@@ -206,7 +206,7 @@ def correct_for_d(p, d, m):
 
     This function expects that p is a prime number."""
     for e in range(1, d + 1):
-        if (p ** e) % m == 1:
+        if (p**e) % m == 1:
             break
 
     return e, e != d
diff --git a/kit/utils/component_builder.py b/kit/utils/component_builder.py
index bda610c2..1723ccb7 100644
--- a/kit/utils/component_builder.py
+++ b/kit/utils/component_builder.py
@@ -22,7 +22,7 @@ def __init__(self, message, error):
 
 def chain_run(funcs: Iterable[Callable]):
     """Run functions sequentially. Fail at first function with failed
-       return value."""
+    return value."""
     for fn in funcs:
         success, return_code = fn()
         if not success:
@@ -56,7 +56,7 @@ def run(cmd_and_args: Union[str, List[str]]) -> Tuple[bool, int]:
 
 def try_run(spec: dict, attrib: str):
     """Try to run the attrib in the spec.
-       Do nothing (pass success) if no key in dict.
+    Do nothing (pass success) if no key in dict.
     """
     try:
         return run(spec[attrib])
@@ -118,7 +118,7 @@ def setup(self):
 
     def already_successful(self, stage):
         """Returns True if stage already recorded in info file
-           as successful"""
+        as successful"""
         return self._info_file["status"][stage] == "success"
 
     def update_info_file(self, stage, success):
diff --git a/kit/utils/spec.py b/kit/utils/spec.py
index 65899548..b89f9528 100644
--- a/kit/utils/spec.py
+++ b/kit/utils/spec.py
@@ -107,8 +107,8 @@ def fill_dep_str(s: str) -> str:
 
 
 def get_dependencies(instances_list: List) -> List[str]:
-    """ Returns a list of dependencies defined
-    and used in the recipe file """
+    """Returns a list of dependencies defined
+    and used in the recipe file"""
     dependency_list: List[str] = []
 
     def fill_dependencies_list(s: str, d: dict) -> None:
@@ -135,7 +135,7 @@ def fill_dependencies_list(s: str, d: dict) -> None:
 
 def fill_rloc_paths(d: dict, repo_location: PathType) -> dict:
     """Create absolute path for the top-level attribs that begin
-       with 'init_' or '_export_' by prepending repo location"""
+    with 'init_' or '_export_' by prepending repo location"""
     for k, v in d.items():
         if k.startswith("init_") or k.startswith("export_"):
             d[k] = f"{repo_location}/{v}"