diff --git a/Makefile b/Makefile
index e9e663d..326c97b 100644
--- a/Makefile
+++ b/Makefile
@@ -9,9 +9,9 @@ help:
 
 .PHONY: ch13-ci
 ch13-ci:
-	python scripts/sim_stroop.py --n-subjects 6 --n-trials 10
+	python scripts/sim_stroop.py --n-subjects 6 --n-trials 10 --seed 2025
 	python scripts/ch13_stroop_within.py --save-plots
-	python scripts/sim_fitness_2x2.py --n-per-group 5
+	python scripts/sim_fitness_2x2.py --n-per-group 5 --seed 2025
 	python scripts/ch13_fitness_mixed.py --save-plots
 
 .PHONY: ch13
diff --git a/scripts/sim_fitness_2x2.py b/scripts/sim_fitness_2x2.py
index c4aac84..8481ab9 100644
--- a/scripts/sim_fitness_2x2.py
+++ b/scripts/sim_fitness_2x2.py
@@ -1,8 +1,9 @@
 # SPDX-License-Identifier: MIT
-import os, json, time
+import os, json, time, argparse
 import numpy as np, pandas as pd
 
-rng = np.random.default_rng(7)
+# globals configured in main()
+rng = np.random.default_rng()
 N = 80
 GROUPS = ["ProgramA","ProgramB"]
 
@@ -38,7 +39,7 @@ def simulate():
     long.to_csv("data/synthetic/fitness_long.csv", index=False)
 
     meta = dict(
-        seed=7,
+        seed=int(getattr(rng, "seed_seq", np.random.SeedSequence()).entropy) if hasattr(rng, "seed_seq") else None,
         n=int(N),
         design="2x2 mixed (Group between × Time within)",
         programs=GROUPS,
@@ -50,5 +51,14 @@ def simulate():
 
     print("Wrote fitness_subjects.csv, fitness_long.csv, fitness_meta.json")
 
+def main():
+    ap = argparse.ArgumentParser()
+    ap.add_argument("--seed", type=int, default=7, help="RNG seed")
+    ap.add_argument("--n-per-group", type=int, default=5, help="(kept for CLI parity; not used here)")
+    args = ap.parse_args()
+    global rng
+    rng = np.random.default_rng(args.seed)
+    simulate()
+
 if __name__ == "__main__":
-    simulate()
\ No newline at end of file
+    main()
\ No newline at end of file
diff --git a/scripts/sim_stroop.py b/scripts/sim_stroop.py
index 62a4f88..6162de9 100644
--- a/scripts/sim_stroop.py
+++ b/scripts/sim_stroop.py
@@ -73,7 +73,7 @@ def simulate():
 
                 # sprinkle outliers (anticipations & lapses)
                 if rng.random() < OUTLIER_RATE_ANTICIP:
-                    rt = rng.normal(OUTLIER_RT_ANTICIP_MU, OUTLIER_RT_ANTICIP_SD)   # anticipatory
+                    rt = rng.normal(OUTLIER_RT_ANTICIP_MU, OUTLIER_RT_ANTICIP_SD)    # anticipatory
                 if rng.random() < OUTLIER_RATE_LAPSE:
                     rt = rng.normal(OUTLIER_RT_LAPSE_MU, OUTLIER_RT_LAPSE_SD)      # lapse
 
@@ -135,10 +135,6 @@ def write_meta(subjects: pd.DataFrame, trials: pd.DataFrame):
         json.dump(meta, f, indent=2)
     print("Wrote data/synthetic/psych_stroop_meta.json")
 
-if __name__ == "__main__":
-    subjects_df, trials_df = simulate()
-    write_meta(subjects_df, trials_df)
-
 
 def main():
     ap = argparse.ArgumentParser()
@@ -146,9 +142,13 @@ def main():
     ap.add_argument("--n-subjects", type=int, default=60)
     ap.add_argument("--n-trials", type=int, default=100)
     args = ap.parse_args()
-    global N_SUBJ, N_TRIALS_PER_COND, rng
+    global N_SUBJ, N_TRIALS_PER_COND, rng, RNG_SEED
     N_SUBJ = args.n_subjects
     N_TRIALS_PER_COND = args.n_trials
-    rng = np.random.default_rng(args.seed)
+    RNG_SEED = args.seed
+    rng = np.random.default_rng(RNG_SEED)
+    subjects_df, trials_df = simulate()
+    write_meta(subjects_df, trials_df)
+
 if __name__ == "__main__":
     main()
\ No newline at end of file