diff --git a/CLAUDE.md b/CLAUDE.md
index a3b4b1e0708c088183395bd826665200ab40a1b8..a40795df105407f3eaa81f458803d903131193d9 100644
--- a/CLAUDE.md
+++ b/CLAUDE.md
@@ -403,6 +403,7 @@ If `expand()` in the recorder ever changes, all of those column names change wit
 - `ticks` config: 26 parquet files across builds 001–046 (with gaps; 045 is an empty/aborted build with no telemetry, so no file). Build 041 was recovered from the NVMe spool after the Postgres DB was wiped. Builds 042–043 were added under the old offset scheme; builds 044 and 046 were added 2026-07-13 via **identity mapping** now that the DB realigned to 1:1 — see "Build ID mapping" below.
 - `print_profile_name` is preserved on every row; the `print_profile_id` UUID is *not* materialized here (see the architectural decision above).
 - `events` and `plotter_commands` not surfaced as configs.
+- **`peregrine` config** (added 2026-07-19): sparse, defect-labeled counterpart to `ticks`, shaped like the ORNL Peregrine dataset — one row per *labeled layer*, `data/peregrine/{build:03d}.parquet`, built by `scripts/peregrine/01_extract.py` from `source/labels/` + `data/frames_index/` + `source/telemetry/`. Images are raw JPEG/PNG `binary` (post-recoat chamber = `image_after_powder`, post-scan chamber = `image_after_melt`, galvo = `part_mask`), labels are bbox-level `list<{class,bbox,polarity}>` + `has_<class>` bools + a few process scalars. Only re-runs the changed build (no full rewrite). Post-recoat vs post-scan stills are split by the z2 **step** preceding each label (z2 ramps monotonically to ~29 mm — it does NOT cycle to 0 per recoat; don't reuse the return-to-0 heuristic). Prereqs per build: `sls-export` + `sls-export-labels` + `sls-deliver-frames` must have run.
 - **Forward-fill semantics deferred.** Current attach is "fresh frame within 100 ms of tick or null." Carrying the latest captured frame forward to fill null rows is left to consumers (one-line groupby + ffill); baking it into the dataset would destroy the fresh-vs-stale signal that's the whole point of keeping nulls.
 - `previews/` MP4s and timelapse GIFs: rendered through 046. Timelapse GIFs re-rendered 2026-07-13 with `CHAMBER_BRIGHTNESS_RATIO` raised 0.25 → 0.5 to drop dim-frame flashes.
 
diff --git a/README.md b/README.md
index 5269e63019949f25c08879e691564f79b2425d29..66bc91a423262d1dab5e533ec794c6988b846c6e 100644
--- a/README.md
+++ b/README.md
@@ -13,6 +13,10 @@ configs:
   - split: train
     path: data/ticks/*.parquet
   default: true
+- config_name: peregrine
+  data_files:
+  - split: train
+    path: data/peregrine/*.parquet
 dataset_info:
   features:
   - name: frame_chamber
@@ -46,6 +50,23 @@ Full image bytes are embedded in each row — no separate frame download. Filter
 with_chamber = ds.filter(lambda r: r["frame_chamber"] is not None)
 ```
 
+## `peregrine` config — defect-labeled layers
+
+A second, sparse config shaped like the [ORNL Peregrine dataset](https://huggingface.co/datasets/ppak10/Peregrine-Dataset-v2023-11): **one row per layer that carries a defect label**, for Peregrine-style transfer learning. Only labeled layers are recorded, so it's small and defect-focused (vs. the dense per-tick `ticks` config).
+
+```python
+peregrine = load_dataset("ppak10/Agentic-SLS-Telemetry", "peregrine", split="train")
+row = peregrine[0]
+# row["image_after_powder"]  → JPEG bytes, post-recoat chamber still (Peregrine after_powder)
+# row["image_after_melt"]    → JPEG bytes, post-scan chamber still   (Peregrine after_melt)
+# row["part_mask"]           → PNG bytes, galvo scan mask            (Peregrine part_ids)
+# row["labels"]              → list of {class, bbox [x0,y0,x1,y1] normalized, polarity}
+# row["has_debris"], ...     → bool per class (PEREGRINE_ALL_CLASSES)
+# row["printBed_temp"], row["laser_power_w"], ...  → process scalars at the layer
+```
+
+Images are raw encoded bytes (`binary`, like Peregrine's `image_after_*`) — decode with `PIL.Image.open(io.BytesIO(...))`. Labels are bbox-level (from the live defect detector / operator verdicts), not per-pixel masks; `polarity` is `positive` (defect present) or `negative` (operator marked the alert a false positive). One parquet per build (`data/peregrine/{build:03d}.parquet`), so a new build never rewrites existing files.
+
 ---
 
 ## Timelapse previews
diff --git a/data/frames_index/048.parquet b/data/frames_index/048.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..7f1d455f35e4ab6a49591e852b237b394a929a95
--- /dev/null
+++ b/data/frames_index/048.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:74c590ce7d6baba89d05e6f5d0031208a6ec72775070f3a9a38fe40d124e14b0
+size 11921770
diff --git a/data/peregrine/048.parquet b/data/peregrine/048.parquet
new file mode 100644
index 0000000000000000000000000000000000000000..e7b4e663c02feabc64828451fe622ca6f581c8ce
--- /dev/null
+++ b/data/peregrine/048.parquet
@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:a042a2865bedd9a0665f822425022c30ffd819bb1b8aa9778a1e1231485bd796
+size 297105
diff --git a/scripts/peregrine/01_extract.py b/scripts/peregrine/01_extract.py
new file mode 100644
index 0000000000000000000000000000000000000000..84f1a2ac05a7f0f679dccfae2dc0ca1ec8cac69f
--- /dev/null
+++ b/scripts/peregrine/01_extract.py
@@ -0,0 +1,288 @@
+#!/usr/bin/env python3
+"""Build the `peregrine` config: one row per LABELED layer per build, shaped
+like the ORNL Peregrine dataset (ppak10/Peregrine-Dataset-v2023-11).
+
+Only layers that carry at least one *active* defect label are emitted — this is
+the sparse, defect-focused counterpart to the dense `ticks` config, meant for
+Peregrine-style transfer learning. Frames are recorded ONLY for labeled layers.
+
+Per-build output `data/peregrine/{build_id:03d}.parquet` (zero-padded) so a new
+build only writes its own file — existing builds are never rewritten.
+
+Reads from this dataset's own source/ tree + built frame index:
+  source/labels/{build}.parquet        curated active labels (sls-export-labels)
+  data/frames_index/{build}.parquet    ts_ms -> (archive zip, member) per frame
+  source/telemetry/{build}.parquet     z2 (recoat boundary) + process scalars
+  source/recorder/builds.jsonl         build_name
+
+Schema (Peregrine-aligned; images are raw JPEG bytes as `binary`, exactly like
+Peregrine's image_after_* columns — decode with PIL.Image.open(io.BytesIO(x))):
+  build, build_name, layer, ts
+  image_after_powder   post-recoat chamber still (Peregrine after_powder)
+  image_after_melt     post-scan   chamber still (Peregrine after_melt)
+  part_mask            galvo scan mask           (Peregrine part_ids analog)
+  labels               list<{class, bbox[x0,y0,x1,y1] normalized, polarity}>
+  has_<class>          bool per PEREGRINE_ALL_CLASSES (12)
+  <sensor>_temp / laser_power_w   process scalars nearest-before the layer ts
+
+Frame selection per labeled layer (anchor = earliest label ts on the layer):
+  after_melt   = chamber frame nearest the anchor ts (the detection frame)
+  after_powder = first chamber frame after the recoat that precedes the anchor
+                 (z2 returns to 0 = recoat complete); null if none resolved
+  part_mask    = galvo frame nearest the anchor ts
+
+Usage:
+    uv run scripts/peregrine/01_extract.py            # all builds with labels
+    uv run scripts/peregrine/01_extract.py 48         # specific build ids
+"""
+import io
+import sys
+import zipfile
+from pathlib import Path
+
+import polars as pl
+import pyarrow as pa
+import pyarrow.parquet as pq
+
+sys.path.insert(0, str(Path(__file__).parent.parent))
+from _lib import DATA_DIR, SOURCE_DIR, TELEMETRY_DIR, EXPORTS_DIR, iter_jsonl
+
+LABELS_DIR = SOURCE_DIR / "labels"
+FRAMES_INDEX_DIR = DATA_DIR / "frames_index"
+OUTPUT_DIR = DATA_DIR / "peregrine"
+
+# Bit-position order shared with the defect model (runs/*/constants.py).
+PEREGRINE_ALL_CLASSES = [
+    "powder", "printed", "recoater_hopping", "recoater_streaking",
+    "incomplete_spreading", "swelling", "debris", "super_elevation",
+    "spatter", "misprint", "over_melting", "under_melting",
+]
+
+# Process scalars: (output column, telemetry kind, sensor_id | None for single-sensor).
+SCALAR_SPECS = [
+    ("printBed_temp",    "temp.current",  "printBed"),
+    ("printChamber_temp", "temp.current", "printChamber1"),
+    ("powderBed_temp",   "temp.current",  "powderBed"),
+    ("surface_temp",     "temp.current",  "surface"),
+    ("laser_power_w",    "power.current", None),
+]
+
+LABEL_STRUCT = pa.struct([
+    pa.field("class", pa.string()),
+    pa.field("bbox", pa.list_(pa.float64())),
+    pa.field("polarity", pa.string()),
+])
+
+OUTPUT_SCHEMA = pa.schema(
+    [
+        pa.field("build", pa.int64()),
+        pa.field("build_name", pa.string()),
+        pa.field("layer", pa.int32()),
+        pa.field("ts", pa.timestamp("us", tz="UTC")),
+        pa.field("image_after_powder", pa.binary()),
+        pa.field("image_after_melt", pa.binary()),
+        pa.field("part_mask", pa.binary()),
+        pa.field("labels", pa.list_(LABEL_STRUCT)),
+    ]
+    + [pa.field(f"has_{c}", pa.bool_()) for c in PEREGRINE_ALL_CLASSES]
+    + [pa.field(name, pa.float64()) for name, _, _ in SCALAR_SPECS]
+)
+
+
+def _build_name(build_id: int) -> str | None:
+    for r in iter_jsonl(EXPORTS_DIR / "builds.jsonl"):
+        if r.get("id") == build_id:
+            return r.get("job_name")
+    return None
+
+
+class FrameStore:
+    """Resolve chamber/galvo JPEG bytes by nearest / first-after timestamp.
+
+    Reads the built frame index (ts_ms -> archive zip + member) and lazily opens
+    the store-mode zip chunks, caching handles (a labeled build touches only a
+    handful of chunks).
+    """
+
+    def __init__(self, build_id: int):
+        fi = pl.read_parquet(FRAMES_INDEX_DIR / f"{build_id:03d}.parquet")
+        self._by_kind = {}
+        for kind in ("chamber", "galvo"):
+            k = fi.filter(pl.col("kind") == kind).sort("ts_ms")
+            self._by_kind[kind] = {
+                "ts": k["ts_ms"].to_numpy(),
+                "archive": k["archive"].to_list(),
+                "member": k["member"].to_list(),
+            }
+        self._zips: dict[str, zipfile.ZipFile] = {}
+
+    def _read(self, kind: str, idx: int) -> bytes:
+        k = self._by_kind[kind]
+        archive = k["archive"][idx]
+        zf = self._zips.get(archive)
+        if zf is None:
+            zf = self._zips[archive] = zipfile.ZipFile(archive)
+        return zf.read(k["member"][idx])
+
+    def nearest(self, kind: str, ts_ms: int) -> bytes | None:
+        ts = self._by_kind[kind]["ts"]
+        if len(ts) == 0:
+            return None
+        import numpy as np
+        i = int(np.searchsorted(ts, ts_ms))
+        cands = [j for j in (i - 1, i) if 0 <= j < len(ts)]
+        best = min(cands, key=lambda j: abs(int(ts[j]) - ts_ms))
+        return self._read(kind, best)
+
+    def first_in(self, kind: str, lo_ms: int, hi_ms: int) -> bytes | None:
+        """First frame with lo <= ts <= hi (chronological), else None."""
+        ts = self._by_kind[kind]["ts"]
+        if len(ts) == 0:
+            return None
+        import numpy as np
+        i = int(np.searchsorted(ts, lo_ms, side="left"))
+        if i < len(ts) and int(ts[i]) <= hi_ms:
+            return self._read(kind, i)
+        return None
+
+    def close(self):
+        for zf in self._zips.values():
+            zf.close()
+
+
+# z2 is the build piston: it steps once per deposited layer (Peregrine
+# "z2 advances once per deposited layer") and ramps monotonically to ~29 mm over
+# the build — it does NOT cycle back to 0 per recoat. A layer/recoat boundary is
+# therefore a STEP in z2; the ts the step settles ~= the post-recoat moment.
+Z2_STEP_MIN = 1.0  # ignore sub-unit sensor jitter
+
+
+def _recoat_boundaries_ms(tel: pl.DataFrame):
+    """ts_ms at each z2 step (recoat/layer boundary). Sorted ascending."""
+    z2 = (
+        tel.filter(pl.col("kind") == "position.z2")
+        .select("ts", "value")
+        .sort("ts")
+        .with_columns(ts_ms=pl.col("ts").dt.epoch(time_unit="ms"))
+    )
+    if z2.is_empty():
+        return []
+    z2 = z2.with_columns(step=(pl.col("value") - pl.col("value").shift(1)).abs())
+    steps = z2.filter(pl.col("step") >= Z2_STEP_MIN)
+    return steps["ts_ms"].to_list()
+
+
+def _scalar_series(tel: pl.DataFrame):
+    """{output_col: (ts_ms ndarray, value ndarray)} for nearest-before lookup."""
+    import numpy as np
+    out = {}
+    for name, kind, sensor in SCALAR_SPECS:
+        sel = tel.filter(pl.col("kind") == kind)
+        if sensor is not None:
+            sel = sel.filter(pl.col("sensor_id") == sensor)
+        sel = sel.select("ts", "value").sort("ts")
+        out[name] = (
+            sel["ts"].dt.epoch(time_unit="ms").to_numpy(),
+            sel["value"].to_numpy(),
+        ) if not sel.is_empty() else (np.array([]), np.array([]))
+    return out
+
+
+def _scalar_at(series, ts_ms: int) -> float | None:
+    import numpy as np
+    ts, val = series
+    if len(ts) == 0:
+        return None
+    i = int(np.searchsorted(ts, ts_ms, side="right")) - 1  # nearest-before
+    if i < 0:
+        i = 0  # label before first sample: take earliest
+    return float(val[i])
+
+
+def process_build(build_id: int) -> Path | None:
+    labels_path = LABELS_DIR / f"{build_id:03d}.parquet"
+    fi_path = FRAMES_INDEX_DIR / f"{build_id:03d}.parquet"
+    tel_path = TELEMETRY_DIR / f"{build_id:03d}.parquet"
+    if not labels_path.exists():
+        return None
+    labels = pl.read_parquet(labels_path)
+    if labels.is_empty():
+        return None
+    if not fi_path.exists() or not tel_path.exists():
+        print(f"  build {build_id}: labels present but frames_index/telemetry "
+              f"missing — run sls-deliver-frames + sls-export first; skipped")
+        return None
+
+    tel = pl.read_parquet(tel_path)
+    frames = FrameStore(build_id)
+    boundaries = _recoat_boundaries_ms(tel)
+    scalars = _scalar_series(tel)
+    build_name = _build_name(build_id)
+
+    import numpy as np
+    bnd = np.array(boundaries, dtype="int64")
+
+    rows: list[dict] = []
+    # One row per labeled layer; anchor = earliest label ts on that layer.
+    for (layer,), grp in labels.sort("ts").group_by(["layer"], maintain_order=True):
+        grp = grp.sort("ts")
+        anchor_ts_ms = int(grp["ts"].dt.epoch(time_unit="ms")[0])
+
+        label_list = [
+            {"class": r["class"], "bbox": list(r["bbox"]), "polarity": r["polarity"]}
+            for r in grp.iter_rows(named=True)
+        ]
+        present = {lbl["class"] for lbl in label_list}
+
+        # after_powder: first chamber after the recoat that precedes the anchor.
+        lo = int(bnd[np.searchsorted(bnd, anchor_ts_ms, side="right") - 1]) \
+            if len(bnd) and bnd[0] <= anchor_ts_ms else None
+        after_powder = frames.first_in("chamber", lo, anchor_ts_ms) if lo is not None else None
+
+        row = {
+            "build": build_id,
+            "build_name": build_name,
+            "layer": int(layer),
+            "ts": grp["ts"][0],
+            "image_after_powder": after_powder,
+            "image_after_melt": frames.nearest("chamber", anchor_ts_ms),
+            "part_mask": frames.nearest("galvo", anchor_ts_ms),
+            "labels": label_list,
+        }
+        for c in PEREGRINE_ALL_CLASSES:
+            row[f"has_{c}"] = c in present
+        for name, _, _ in SCALAR_SPECS:
+            row[name] = _scalar_at(scalars[name], anchor_ts_ms)
+        rows.append(row)
+
+    frames.close()
+    rows.sort(key=lambda r: r["layer"])
+
+    table = pa.Table.from_pylist(rows, schema=OUTPUT_SCHEMA)
+    OUTPUT_DIR.mkdir(parents=True, exist_ok=True)
+    out_path = OUTPUT_DIR / f"{build_id:03d}.parquet"
+    pq.write_table(table, out_path, compression="zstd")
+    print(f"  build {build_id}: {len(rows)} labeled layer(s) -> "
+          f"{out_path.relative_to(DATA_DIR.parent)}")
+    return out_path
+
+
+def _builds_with_labels() -> list[int]:
+    return sorted(int(p.stem) for p in LABELS_DIR.glob("*.parquet"))
+
+
+def main():
+    args = sys.argv[1:]
+    builds = [int(a) for a in args] if args else _builds_with_labels()
+    if not builds:
+        print("no builds with source/labels/*.parquet found")
+        return
+    written = 0
+    for b in builds:
+        if process_build(b) is not None:
+            written += 1
+    print(f"done: {written} build(s) written to {OUTPUT_DIR.relative_to(DATA_DIR.parent)}")
+
+
+if __name__ == "__main__":
+    main()
diff --git a/source/frames/048/bedmatrix/chunk-0001.zip b/source/frames/048/bedmatrix/chunk-0001.zip
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