diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml
index 0ff7cd29..e1e8b208 100644
--- a/.pre-commit-config.yaml
+++ b/.pre-commit-config.yaml
@@ -1,6 +1,20 @@
+default_language_version:
+    # all hooks should run with python 3.6+
+    python: python3
 repos:
- - repo: https://github.com/astral-sh/ruff-pre-commit
-   # Ruff version.
-   rev: v0.1.0
-   hooks:
-     - id: ruff
+  - repo: https://github.com/pre-commit/pre-commit-hooks
+    rev: v4.5.0
+    hooks:
+      - id: no-commit-to-branch
+      - id: check-executables-have-shebangs
+      - id: check-shebang-scripts-are-executable
+      - id: check-added-large-files
+        args: ['--maxkb=500', '--enforce-all']
+      - id: check-yaml
+
+  - repo: https://github.com/astral-sh/ruff-pre-commit
+    # Ruff version.
+    rev: v0.1.3
+    hooks:
+      - id: ruff
+        args: [--show-source, --fix]
diff --git a/examples/DA_paper/uphill/generate_rs.py b/examples/DA_paper/uphill/generate_rs.py
index ee6cbdcf..2a15ffa6 100644
--- a/examples/DA_paper/uphill/generate_rs.py
+++ b/examples/DA_paper/uphill/generate_rs.py
@@ -268,6 +268,10 @@ def baised_md(configuration, mlp, temp, dt, interval, bias, **kwargs):
                                      rng=RandomState())
     
     traj = ASETrajectory("tmp.traj", 'w', ase_atoms)
+    energies = []
+
+    def append_energy(_atoms=ase_atoms):
+        energies.append(_atoms.get_potential_energy())
 
     if temp > 0:                                         # Default Langevin NVT
         dyn = Langevin(ase_atoms, dt * ase_units.fs,
@@ -285,7 +289,7 @@ def baised_md(configuration, mlp, temp, dt, interval, bias, **kwargs):
     trajectory = mlt.ConfigurationSet()
     for i in range(10, len(traj)):
         trajectory.append(traj[i])
-    energies = energies[10:] # noqa: F821
+    energies = energies[10:]
   
     for i, (frame, energy) in enumerate(zip(trajectory, energies)):
         frame.update_attr_from(configuration)
@@ -314,7 +318,7 @@ def generate_rs(TS, solution, mlp, box_size):
     for i, species in enumerate(reactants):
         bias = mlt.Bias(zeta_func=mlt.AverageDistance((1,12), (6,11)), kappa=0.5, reference=ref[i])
         traj = baised_md(configuration=species,
-                              mlp=endo, # noqa: F821
+                              mlp=mlp,
                               temp=300,
                               dt=0.5,
                               interval=20,