diff --git a/crop_borders.py b/crop_borders.py
new file mode 100755
index 0000000..1365171
--- /dev/null
+++ b/crop_borders.py
@@ -0,0 +1,157 @@
+#!/usr/bin/env -S uv run --script
+# /// script
+# dependencies = ["Pillow"]
+# ///
+import argparse
+from functools import cache
+import logging
+from pathlib import Path
+import sys
+from typing import Tuple
+
+from PIL import Image, ImageChops, ImageStat
+
+
+logging.basicConfig(level=logging.INFO, format="%(asctime)s %(levelname)s: %(message)s")
+
+
+def parse_args() -> argparse.Namespace:
+    p = argparse.ArgumentParser(formatter_class=argparse.ArgumentDefaultsHelpFormatter)
+    p.add_argument("paths", nargs="+", type=Path, help="Image paths to crop")
+    p.add_argument(
+        "--outdir",
+        "-o",
+        type=Path,
+        default=None,
+        help="Output directory. If omitted, saves as <stem>.borderless.<ext> next to original.",
+    )
+    p.add_argument("--tolerance", "-t", type=int, default=10, help="Color tolerance (0-255) for border detection")
+    p.add_argument("--min-border", type=int, default=1, help="Minimum border thickness to consider cropping")
+    return p.parse_args()
+
+
+def is_near_color(pixel, color, tol: int) -> bool:
+    return all(abs(int(pixel[i]) - int(color[i])) <= tol for i in range(len(color)))
+
+
+def dominant_edge_color(im: Image.Image) -> Tuple[int, int, int]:
+    # sample edges and return median color (RGB)
+    w, h = im.size
+    samples = []
+    # take 1-pixel wide strips from each edge
+    left = im.crop((0, 0, 1, h))
+    right = im.crop((w - 1, 0, w, h))
+    top = im.crop((0, 0, w, 1))
+    bottom = im.crop((0, h - 1, w, h))
+    for region in (left, right, top, bottom):
+        stat = ImageStat.Stat(region)
+        # stat.mean may have 1 (L) or 3 (RGB) channels
+        mean = stat.mean
+        if len(mean) == 1:
+            samples.append((int(mean[0]), int(mean[0]), int(mean[0])))
+        else:
+            samples.append(tuple(int(x) for x in mean[:3]))
+    # return median of samples per channel
+    channels = list(zip(*samples))
+    med = tuple(int(sorted(ch)[len(ch) // 2]) for ch in channels)
+    return med
+
+
+def find_crop_box(im: Image.Image, tol: int, min_border: int) -> Tuple[int, int, int, int]:
+    # Convert to RGB
+    rgb = im.convert("RGB")
+    w, h = rgb.size
+    edge_color = dominant_edge_color(rgb)
+
+    def col_at(x, y):
+        return rgb.getpixel((x, y))
+
+    left = 0
+    for x in range(w):
+        # check column x: all pixels near edge_color?
+        col_pixels = [col_at(x, y) for y in range(h)]
+        if all(is_near_color(px, edge_color, tol) for px in col_pixels):
+            left = x + 1
+            continue
+        break
+
+    right = w
+    for x in range(w - 1, -1, -1):
+        col_pixels = [col_at(x, y) for y in range(h)]
+        if all(is_near_color(px, edge_color, tol) for px in col_pixels):
+            right = x
+            continue
+        break
+
+    top = 0
+    for y in range(h):
+        row_pixels = [col_at(x, y) for x in range(w)]
+        if all(is_near_color(px, edge_color, tol) for px in row_pixels):
+            top = y + 1
+            continue
+        break
+
+    bottom = h
+    for y in range(h - 1, -1, -1):
+        row_pixels = [col_at(x, y) for x in range(w)]
+        if all(is_near_color(px, edge_color, tol) for px in row_pixels):
+            bottom = y
+            continue
+        break
+
+    # enforce minimum border threshold
+    if left < min_border:
+        left = 0
+    if (w - right) < min_border:
+        right = w
+    if top < min_border:
+        top = 0
+    if (h - bottom) < min_border:
+        bottom = h
+
+    # ensure valid box
+    if left >= right or top >= bottom:
+        return 0, 0, w, h
+    return left, top, right, bottom
+
+
+def crop_image(path: Path, target: Path, tolerance: int, min_border: int) -> Path:
+    im = Image.open(path)
+    box = find_crop_box(im, tol=tolerance, min_border=min_border)
+    if box == (0, 0, im.width, im.height):
+        logging.info(f"no border detected: {path}")
+        return path
+    cropped = im.crop(box)
+    cropped.save(target)
+    logging.info(f"wrote: {target} (cropped box={box})")
+    return target
+
+
+@cache
+def make_dir(path: Path) -> None:
+    path.mkdir(parents=True, exist_ok=True)
+
+
+def main() -> None:
+    args = parse_args()
+
+    for p in args.paths:
+        p: Path
+        if not p.exists():
+            logging.error(f"not found: {p}")
+            continue
+        try:
+            # resolve target path here (simple outdir logic)
+            if args.outdir is None:
+                target = p.with_stem(f"{p.stem}.borderless")
+            else:
+                make_dir(args.outdir)
+                target = args.outdir / p.name
+
+            crop_image(path=p, target=target, tolerance=args.tolerance, min_border=args.min_border)
+        except Exception as e:
+            logging.exception(f"failed to process {p}: {e}")
+
+
+if __name__ == "__main__":
+    main()