diff --git a/SKILL.md b/SKILL.md
index 63eb84c..eed9710 100644
--- a/SKILL.md
+++ b/SKILL.md
@@ -73,6 +73,7 @@ Helpers (`helpers/transcribe.py`, `helpers/render.py`, etc.) live alongside this
 - **`transcribe_batch.py <videos_dir>`** — 4-worker parallel transcription. Use for multi-take.
 - **`pack_transcripts.py --edit-dir <dir>`** — `transcripts/*.json` → `takes_packed.md` (phrase-level, break on silence ≥ 0.5s).
 - **`timeline_view.py <video> <start> <end>`** — filmstrip + waveform PNG. On-demand visual drill-down. **Not a scan tool** — use it at decision points, not constantly.
+- **`sync_audio.py --config <sync.json>`** — precise multicam audio sync via GCC-PHAT. Use for any project with more than one camera/microphone. Cross-correlates 10s+ audio windows around clap markers (or any shared transient) with phase-transform whitening — sub-frame accurate even between dissimilar mics with different frequency response or room reverb. **Always reach for this on multicam — never eyeball offsets from Scribe's `(claps)` audio_event timestamps**, those drift 200–500ms. See the file header for config shape. Outputs `sync_offsets.json` with `final` offsets per target camera; if two events disagree by > 50ms it flags clap-pattern aliasing for review.
 - **`render.py <edl.json> -o <out>`** — per-segment extract → concat → overlays (PTS-shifted) → subtitles LAST. `--preview` for 720p fast. `--build-subtitles` to generate master.srt inline.
 - **`grade.py <in> -o <out>`** — ffmpeg filter chain grade. Presets + `--filter '<raw>'` for custom.
 
diff --git a/helpers/sync_audio.py b/helpers/sync_audio.py
new file mode 100644
index 0000000..63c307d
--- /dev/null
+++ b/helpers/sync_audio.py
@@ -0,0 +1,295 @@
+"""Precise multicam audio sync via GCC-PHAT cross-correlation.
+
+The standard cross-correlation peak between mismatched microphones is
+broad and ambiguous. GCC-PHAT (Generalized Cross-Correlation with Phase
+Transform) whitens the spectrum before correlating — the peak becomes
+sharp regardless of mic frequency-response mismatch or room reverb.
+
+This is the audio-engineering standard for multicam sync. Sub-frame
+accurate, robust between dissimilar microphones (e.g. studio mic vs
+on-camera mic).
+
+Inputs:
+  - A reference video (the one whose audio you want to keep)
+  - One or more target videos
+  - Rough sync timestamps for at least one clap or shared transient
+
+Workflow:
+  - Extracts a window of audio around each rough timestamp (default 10s)
+  - Runs GCC-PHAT to find the precise lag
+  - Combines lag with window-start delta → precise source-time offset
+  - Reports per-event measurements + averaged final per target
+
+Usage examples:
+    # JSON config (recommended)
+    uv run python helpers/sync_audio.py --config sync.json
+
+    # CLI flags
+    uv run python helpers/sync_audio.py \\
+        --reference /path/desk.mp4 \\
+        --target A1=/path/c0036.mp4 \\
+        --target DJI=/path/dji.mp4 \\
+        --event clap_1:reference=6.23,A1=101.03 \\
+        --event clap_2:reference=204.72,A1=299.76,DJI=5.02 \\
+        --window-default 10 \\
+        --window DJI=30 \\
+        --out sync_offsets.json
+
+Config file shape:
+{
+  "reference": {"name": "DESK", "path": "/abs/desk.mp4"},
+  "targets":   {"A1": "/abs/c0036.mp4", "DJI": "/abs/dji.mp4"},
+  "events": [
+    {"name": "clap_1", "times": {"DESK": 6.23, "A1": 101.03}},
+    {"name": "clap_2", "times": {"DESK": 204.72, "A1": 299.76, "DJI": 5.02}}
+  ],
+  "windows": {"_default": 10, "DJI": 30},
+  "out": "sync_offsets.json"
+}
+
+Output JSON:
+{
+  "final": {"A1": 95.94, "DJI": -197.17},
+  "measurements": [...]
+}
+
+Tips:
+  - At least one shared sync event per target is required.
+  - Multiple events per target enable consistency checks and flag clock drift.
+  - If two measurements for the same target disagree by > 0.05s, the script
+    prints a warning. The most likely cause is clap-pattern aliasing (3 claps
+    spaced ~0.5s create correlation peaks at integer multiples of that period).
+    Pick the offset with the highest sharpness, or verify visually.
+"""
+
+from __future__ import annotations
+
+import argparse
+import json
+import subprocess
+import sys
+import tempfile
+from pathlib import Path
+
+import numpy as np
+from scipy import signal
+from scipy.io import wavfile
+
+SR = 16000  # 16kHz — enough for transient localization, fast to process
+HIGHPASS_HZ = 200  # removes AC/rumble before correlation
+
+
+def extract_audio(video: Path, start: float, duration: float) -> np.ndarray:
+    """Extract mono PCM audio at SR. Returns float32 array in [-1, 1]."""
+    with tempfile.NamedTemporaryFile(suffix=".wav", delete=False) as tmp:
+        tmp_path = Path(tmp.name)
+    cmd = [
+        "ffmpeg", "-y", "-hide_banner", "-loglevel", "error",
+        "-ss", f"{max(0.0, start):.3f}", "-i", str(video),
+        "-t", f"{duration:.3f}",
+        "-ac", "1", "-ar", str(SR), "-c:a", "pcm_s16le",
+        "-vn", str(tmp_path),
+    ]
+    subprocess.run(cmd, check=True)
+    sr, data = wavfile.read(tmp_path)
+    tmp_path.unlink(missing_ok=True)
+    if sr != SR:
+        raise RuntimeError(f"unexpected sample rate {sr}")
+    if data.ndim > 1:
+        data = data.mean(axis=1)
+    return data.astype(np.float32) / 32768.0
+
+
+def gcc_phat(reference: np.ndarray, target: np.ndarray) -> tuple[int, float]:
+    """Generalized Cross-Correlation with Phase Transform.
+
+    Whitens the cross-power spectrum (keeps only phase, discards amplitude)
+    so the correlation peak responds purely to timing. Robust to mismatched
+    mic frequency response and room reverb.
+
+    Returns (lag_samples, sharpness).
+      lag_samples > 0 means `target` is later than `reference`.
+      sharpness = peak_height / mean(|corr|). Larger = more confident.
+    """
+    sos = signal.butter(4, HIGHPASS_HZ, btype="highpass", fs=SR, output="sos")
+    ref_f = signal.sosfilt(sos, reference)
+    tgt_f = signal.sosfilt(sos, target)
+
+    n = len(ref_f) + len(tgt_f) - 1
+    nfft = 1 << (n - 1).bit_length()
+    R = np.fft.rfft(ref_f, nfft)
+    T = np.fft.rfft(tgt_f, nfft)
+    cross = T * np.conj(R)
+    cross /= np.abs(cross) + 1e-9  # PHAT whitening
+
+    corr = np.fft.irfft(cross, nfft)
+    corr = np.concatenate((corr[-len(tgt_f) + 1:], corr[:len(ref_f)]))
+    lags = np.arange(-len(tgt_f) + 1, len(ref_f))
+
+    peak_idx = int(np.argmax(corr))
+    lag = int(lags[peak_idx])
+    sharpness = float(corr[peak_idx] / (np.mean(np.abs(corr)) + 1e-9))
+    return lag, sharpness
+
+
+def measure_event(
+    ref_path: Path,
+    cam_path: Path,
+    ref_t: float,
+    cam_t: float,
+    window: float,
+    label: str,
+    verbose: bool = True,
+) -> dict:
+    """Measure precise offset of `cam` relative to `ref` at one shared event."""
+    half = window / 2.0
+    ref_start = max(0.0, ref_t - half)
+    cam_start = max(0.0, cam_t - half)
+
+    if verbose:
+        print(f"  {label}  window={window:.0f}s")
+        print(f"    REF window: {ref_start:7.2f}–{ref_start + window:7.2f}")
+        print(f"    CAM window: {cam_start:7.2f}–{cam_start + window:7.2f}")
+
+    ref_audio = extract_audio(ref_path, ref_start, window)
+    cam_audio = extract_audio(cam_path, cam_start, window)
+
+    lag_samples, sharpness = gcc_phat(ref_audio, cam_audio)
+    lag_sec = lag_samples / SR
+
+    # cam_time = ref_time + offset
+    rough_offset = cam_t - ref_t
+    window_delta = cam_start - ref_start
+    precise_offset = window_delta + lag_sec
+
+    if verbose:
+        print(f"    → precise offset = {precise_offset:+.4f}s  (rough was {rough_offset:+.4f}, lag {lag_sec:+.4f}s within windows, sharpness {sharpness:.1f})")
+
+    return {
+        "label": label,
+        "rough_offset": round(rough_offset, 4),
+        "precise_offset": round(precise_offset, 4),
+        "lag_within_windows_sec": round(lag_sec, 4),
+        "sharpness": round(sharpness, 2),
+        "ref_window_start": round(ref_start, 3),
+        "cam_window_start": round(cam_start, 3),
+        "window_seconds": window,
+    }
+
+
+def parse_event_arg(s: str) -> dict:
+    """Parse --event flag: name:ref=t,cam1=t,cam2=t"""
+    name, rest = s.split(":", 1)
+    times = {}
+    for pair in rest.split(","):
+        k, v = pair.split("=", 1)
+        times[k.strip()] = float(v.strip())
+    return {"name": name.strip(), "times": times}
+
+
+def load_config(args) -> dict:
+    if args.config:
+        return json.loads(Path(args.config).read_text())
+
+    if not (args.reference and args.target and args.event):
+        sys.exit("Need --config OR (--reference + --target + --event …)")
+
+    ref_name, ref_path = args.reference.split("=", 1) if "=" in args.reference else ("REF", args.reference)
+    targets = {}
+    for t in args.target:
+        k, v = t.split("=", 1)
+        targets[k] = v
+    events = [parse_event_arg(e) for e in args.event]
+    windows = {"_default": args.window_default}
+    for w in args.window or []:
+        k, v = w.split("=", 1)
+        windows[k] = float(v)
+    return {
+        "reference": {"name": ref_name, "path": ref_path},
+        "targets": targets,
+        "events": events,
+        "windows": windows,
+        "out": args.out,
+    }
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser(description="Precise multicam audio sync via GCC-PHAT")
+    ap.add_argument("--config", help="JSON config file (overrides CLI flags)")
+    ap.add_argument("--reference", help="REF_NAME=/path/to/ref.mp4 (audio anchor)")
+    ap.add_argument("--target", action="append", help="TARGET_NAME=/path (repeat for multiple cams)")
+    ap.add_argument("--event", action="append", help="name:ref=time,target=time[,target=time] (repeat)")
+    ap.add_argument("--window-default", type=float, default=10.0, help="Default window seconds (10)")
+    ap.add_argument("--window", action="append", help="Per-target window: TARGET=seconds")
+    ap.add_argument("--out", default="sync_offsets.json", help="Output JSON path")
+    ap.add_argument("--tolerance", type=float, default=0.05, help="Disagreement threshold across events")
+    args = ap.parse_args()
+
+    cfg = load_config(args)
+    ref_name = cfg["reference"]["name"]
+    ref_path = Path(cfg["reference"]["path"]).resolve()
+    targets = {k: Path(v).resolve() for k, v in cfg["targets"].items()}
+    events = cfg["events"]
+    windows = cfg.get("windows", {"_default": 10.0})
+    default_window = windows.get("_default", 10.0)
+    tolerance = args.tolerance
+
+    if not ref_path.exists():
+        sys.exit(f"reference not found: {ref_path}")
+    for k, p in targets.items():
+        if not p.exists():
+            sys.exit(f"target {k} not found: {p}")
+
+    measurements: list[dict] = []
+    by_target: dict[str, list[dict]] = {k: [] for k in targets}
+
+    for ev in events:
+        if ref_name not in ev["times"]:
+            print(f"  skip {ev['name']}: no reference time")
+            continue
+        ref_t = ev["times"][ref_name]
+        for cam_name, cam_path in targets.items():
+            if cam_name not in ev["times"]:
+                continue
+            cam_t = ev["times"][cam_name]
+            window = float(windows.get(cam_name, default_window))
+            label = f"{cam_name} {ev['name']}"
+            print(f"\nMeasuring {label}:")
+            m = measure_event(ref_path, cam_path, ref_t, cam_t, window, label)
+            m["target"] = cam_name
+            m["event"] = ev["name"]
+            measurements.append(m)
+            by_target[cam_name].append(m)
+
+    final: dict[str, float] = {}
+    print("\n" + "=" * 60)
+    print("FINAL OFFSETS")
+    print("=" * 60)
+    for cam, ms in by_target.items():
+        if not ms:
+            print(f"  {cam}: no measurements")
+            continue
+        offsets = [m["precise_offset"] for m in ms]
+        spread = max(offsets) - min(offsets) if len(offsets) > 1 else 0.0
+        if spread > tolerance:
+            print(f"  ⚠️  {cam}: measurements disagree by {spread:.3f}s (> {tolerance}s tolerance)")
+            print("      Likely cause: clap-pattern aliasing or genuine clock drift.")
+            print("      Per-event offsets:")
+            for m in ms:
+                print(f"        {m['event']}: {m['precise_offset']:+.4f}  (sharpness {m['sharpness']:.1f})")
+            # Pick the highest-sharpness measurement.
+            best = max(ms, key=lambda m: m["sharpness"])
+            print(f"      → using {best['event']} (highest sharpness): {best['precise_offset']:+.4f}")
+            final[cam] = best["precise_offset"]
+        else:
+            avg = sum(offsets) / len(offsets)
+            final[cam] = round(avg, 4)
+            print(f"  ✓  {cam}: {avg:+.4f}s  (avg of {len(ms)} event(s), spread {spread*1000:.0f}ms)")
+
+    out_path = Path(cfg.get("out") or args.out).resolve()
+    out_path.write_text(json.dumps({"final": final, "measurements": measurements}, indent=2))
+    print(f"\nWrote {out_path}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/pyproject.toml b/pyproject.toml
index 1221f19..a89887f 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -9,6 +9,7 @@ dependencies = [
     "matplotlib",
     "pillow",
     "numpy",
+    "scipy",
 ]
 
 [project.optional-dependencies]