diff --git a/benchmarks/measurements/2026-10-13-hunt-repeats/analyse.py b/benchmarks/measurements/2026-10-13-hunt-repeats/analyse.py
new file mode 100644
index 0000000..1c6d838
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+++ b/benchmarks/measurements/2026-10-13-hunt-repeats/analyse.py
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+"""Paired comparison of the baseline hunt program (DSPy) and the pilot hunter over 5 repeats on the 12 positives.
+
+Fixed before the runs finished. Unit = (change, repeat). Arm A = pilot hunter (benchmarks/valid/hunter_arm.py,
+classifier on), arm B = baseline hunt program (openultrasast.hunt.cli). A hit is `benchmarks.unseen.score.catches`.
+Reported: per-change hit rate per arm, overall recall per arm with a Wilson interval over all (change, repeat)
+units, the paired difference B - A with a bootstrap that resamples whole changes (each change is its own
+repository here), and exact McNemar on the (change, repeat) pairs. Instrument failures and parse failures are
+counted and shown, never dropped from the denominator.
+
+    PYTHONPATH=. .venv/bin/python benchmarks/measurements/2026-10-13-hunt-repeats/analyse.py ~/ousast-results/hunt/repeats
+"""
+
+from __future__ import annotations
+
+import json
+import sys
+from pathlib import Path
+
+import pandas as pd
+
+from benchmarks.unseen import score
+from openultrasast import stats
+
+
+def load(root: Path) -> pd.DataFrame:
+    rows = []
+    for arm, prefix in (("hunter", "hunter-"), ("dspy", "dspy-")):
+        for run in sorted(root.glob(prefix + "*")):
+            if not run.is_dir() or not (run / "inputs.json").is_file():
+                continue
+            repeat = int(run.name.split("-")[1])
+            for record in score.load_records(run):
+                if record.get("kind") != "vulnerability":
+                    continue
+                defects = record.get("push", {}).get("admission", {}).get("defects", [])
+                rows.append(
+                    {
+                        "arm": arm,
+                        "repeat": repeat,
+                        "change": record["id"],
+                        "repo": record.get("repo"),
+                        "status": record.get("status", "missing"),
+                        "parse_failed": bool(record.get("parse_failed")),
+                        "suspicions": len(defects),
+                        "hit": int(bool(record.get("done")) and bool(score.catches(record))),
+                    }
+                )
+    return pd.DataFrame(rows)
+
+
+def main(root: Path) -> dict:
+    frame = load(root)
+    wide = frame.pivot_table(index=["change", "repo", "repeat"], columns="arm", values="hit").dropna().reset_index()
+    out: dict = {"units": len(wide), "repeats": sorted(frame["repeat"].unique().tolist())}
+    for arm in ("hunter", "dspy"):
+        part = frame[frame["arm"] == arm]
+        hits, n = int(part["hit"].sum()), len(part)
+        out[arm] = {
+            "hits": hits,
+            "units": n,
+            "recall": hits / n if n else None,
+            "wilson95": stats.wilson(hits, n),
+            "instrument_failures": int((part["status"] == "instrument_failure").sum()),
+            "parse_failures": int(part["parse_failed"].sum()),
+            "suspicions_mean": float(part["suspicions"].mean()) if n else None,
+        }
+    estimate = stats.paired_bootstrap(wide, "change", "hunter", "dspy", resamples=5000, seed=0)
+    out["dspy_minus_hunter"] = {"diff": estimate.diff, "ci95": estimate.ci, "changes": estimate.groups}
+    mc = stats.mcnemar_exact(list(wide["hunter"].astype(bool)), list(wide["dspy"].astype(bool)))
+    out["mcnemar"] = {"only_hunter": mc.b, "only_dspy": mc.c, "concordant": mc.concordant, "p": mc.p_value}
+    per_change = frame.groupby(["change", "repo", "arm"])["hit"].mean().unstack("arm").reset_index()
+    out["per_change"] = per_change.sort_values("repo").to_dict(orient="records")
+    return out
+
+
+if __name__ == "__main__":
+    print(json.dumps(main(Path(sys.argv[1]).expanduser()), indent=2, default=str))