diff --git a/scripts/v28_probe/wiki_signal_probe.py b/scripts/v28_probe/wiki_signal_probe.py
new file mode 100644
index 0000000..d847387
--- /dev/null
+++ b/scripts/v28_probe/wiki_signal_probe.py
@@ -0,0 +1,183 @@
+"""
+V28 Stage A: Signal sanity probe.
+Does the existing sparse attention cache contain usable information about V23 trade outcomes?
+
+Usage:
+    python scripts/v28_probe/wiki_signal_probe.py
+
+Gate (promote to Stage B):
+    spread (WR_has_signal - WR_no_signal) > 3pp
+    AND avg PnL on has_signal trades > 0
+    AND has_signal trade count >= 20
+"""
+
+import gzip
+import json
+import os
+
+import numpy as np
+
+BASE_DIR = os.path.dirname(os.path.dirname(os.path.dirname(os.path.abspath(__file__))))
+RUNS_DIR = os.path.join(BASE_DIR, "runs", "intraday_orb")
+ATTENTION_DIR = os.path.join(BASE_DIR, "data", "cache", "orb_attention")
+
+V23_200D = os.path.join(RUNS_DIR, "intraday_20260419_195924_f325f91a.json")
+V23_400D = os.path.join(RUNS_DIR, "intraday_20260419_213436_27e7c928.json")
+
+# Loaded once per run
+_attention_cache: dict[str, dict] = {}
+
+
+def load_attention(ticker: str) -> dict:
+    if ticker in _attention_cache:
+        return _attention_cache[ticker]
+    path = os.path.join(ATTENTION_DIR, f"{ticker}.json.gz")
+    if not os.path.exists(path):
+        _attention_cache[ticker] = {}
+        return {}
+    try:
+        with gzip.open(path) as f:
+            data = json.load(f)
+        days = data.get("days", {})
+    except Exception:
+        days = {}
+    _attention_cache[ticker] = days
+    return days
+
+
+def has_signal(entry: dict, wiki_thresh: float, article_thresh: int) -> bool:
+    wiki = entry.get("attention_wiki_spike_10d") or 0.0
+    articles = entry.get("attention_article_count_3d") or 0
+    return wiki >= wiki_thresh or articles >= article_thresh
+
+
+def analyze_trades(trades: list, label: str, wiki_thresh: float = 2.0, article_thresh: int = 1) -> dict:
+    signal_trades = []
+    no_signal_trades = []
+    missing = 0
+
+    for trade in trades:
+        ticker = trade["ticker"]
+        date = trade["date"]
+        pnl = trade["pnl"]
+        win = pnl > 0
+
+        days = load_attention(ticker)
+        if date not in days:
+            missing += 1
+            no_signal_trades.append({"pnl": pnl, "win": win, "ticker": ticker, "date": date})
+        elif has_signal(days[date], wiki_thresh, article_thresh):
+            signal_trades.append({
+                "pnl": pnl, "win": win, "ticker": ticker, "date": date,
+                "wiki": days[date].get("attention_wiki_spike_10d", 0),
+                "articles": days[date].get("attention_article_count_3d", 0),
+            })
+        else:
+            no_signal_trades.append({"pnl": pnl, "win": win, "ticker": ticker, "date": date})
+
+    def stats(bucket):
+        if not bucket:
+            return {"count": 0, "wr": 0.0, "avg_pnl": 0.0, "total_pnl": 0.0}
+        wins = sum(1 for t in bucket if t["win"])
+        return {
+            "count": len(bucket),
+            "wr": wins / len(bucket),
+            "avg_pnl": float(np.mean([t["pnl"] for t in bucket])),
+            "total_pnl": sum(t["pnl"] for t in bucket),
+        }
+
+    s = stats(signal_trades)
+    n = stats(no_signal_trades)
+    spread = s["wr"] - n["wr"]
+
+    print(f"\n{'='*60}")
+    print(f"  {label}  [wiki>={wiki_thresh}, articles>={article_thresh}]")
+    print(f"{'='*60}")
+    print(f"  Total trades: {len(trades)}  (cache miss: {missing})")
+    print(f"  HAS SIGNAL: count={s['count']}  WR={s['wr']:.1%}  avg_pnl=${s['avg_pnl']:.2f}")
+    print(f"  NO SIGNAL:  count={n['count']}  WR={n['wr']:.1%}  avg_pnl=${n['avg_pnl']:.2f}")
+    print(f"  WR spread:  {spread:+.1%}")
+
+    gate_pass = spread > 0.03 and s["avg_pnl"] > 0 and s["count"] >= 20
+    print(f"\n  Gate: spread>{0.03:.0%}:{spread*100:.1f}pp  avg_pnl>0:${s['avg_pnl']:.2f}  count>=20:{s['count']}")
+    print(f"  => {'PROMOTE to Stage B' if gate_pass else 'FAIL'}")
+
+    return {
+        "label": label, "signal": s, "no_signal": n, "spread": spread,
+        "gate_pass": gate_pass, "signal_trades": signal_trades,
+    }
+
+
+def analyze_2024_losing_days(run_400d: dict):
+    """For each V23 2024 losing day, what % of cached tickers had wiki signal?"""
+    print(f"\n{'='*60}")
+    print("  2024 LOSING DAY ANALYSIS (400d run)")
+    print(f"{'='*60}")
+
+    losing_2024 = [
+        d for d in run_400d.get("daily_summary", [])
+        if d["date"].startswith("2024") and d["daily_pnl"] < 0 and d["trades"] > 0
+    ]
+    print(f"  V23 2024 losing days (with trades): {len(losing_2024)}")
+
+    tickers = [f.replace(".json.gz", "") for f in os.listdir(ATTENTION_DIR) if f.endswith(".json.gz")][:200]
+
+    rows = []
+    for day in losing_2024[:8]:
+        date = day["date"]
+        checked = with_signal = 0
+        for ticker in tickers:
+            d = load_attention(ticker)
+            if date in d:
+                checked += 1
+                if has_signal(d[date], 2.0, 1):
+                    with_signal += 1
+        pct = (with_signal / checked * 100) if checked else 0
+        rows.append((date, day["daily_pnl"], checked, with_signal, pct))
+        print(f"    {date}: V23 PnL=${day['daily_pnl']:+.0f}  checked={checked}  signal={with_signal} ({pct:.0f}%)")
+
+    if rows:
+        print(f"\n  Avg signal coverage on V23 losing days: {np.mean([r[4] for r in rows]):.0f}%")
+
+
+def main():
+    print("V28 Stage A: Wiki signal sanity probe")
+
+    _attention_cache.clear()
+
+    with open(V23_200D) as f:
+        run_200d = json.load(f)
+    with open(V23_400D) as f:
+        run_400d = json.load(f)
+
+    r200 = analyze_trades(run_200d["trades"], "V23 200d", wiki_thresh=2.0)
+    r400 = analyze_trades(run_400d["trades"], "V23 400d", wiki_thresh=2.0)
+
+    print()
+    r200_low = analyze_trades(run_200d["trades"], "V23 200d [sensitivity: wiki>=1.5]", wiki_thresh=1.5)
+    r400_low = analyze_trades(run_400d["trades"], "V23 400d [sensitivity: wiki>=1.5]", wiki_thresh=1.5)
+
+    analyze_2024_losing_days(run_400d)
+
+    # Final verdict
+    any_pass = r200["gate_pass"] or r400["gate_pass"] or r200_low["gate_pass"] or r400_low["gate_pass"]
+    print(f"\n\n{'='*60}")
+    print("  FINAL VERDICT")
+    print(f"{'='*60}")
+    print(f"  200d (thresh=2.0): {'PASS' if r200['gate_pass'] else 'FAIL'}")
+    print(f"  400d (thresh=2.0): {'PASS' if r400['gate_pass'] else 'FAIL'}")
+    print(f"  200d (thresh=1.5): {'PASS' if r200_low['gate_pass'] else 'FAIL'}")
+    print(f"  400d (thresh=1.5): {'PASS' if r400_low['gate_pass'] else 'FAIL'}")
+    print(f"\n  Decision: {'PROMOTE to Stage B' if any_pass else 'ABORT V28 — signal is noise in existing cache'}")
+
+    if any_pass:
+        best = max([r200, r400, r200_low, r400_low], key=lambda r: r["spread"])
+        print(f"\n  Best: {best['label']} spread={best['spread']:+.1%}")
+        top = sorted(best["signal_trades"], key=lambda x: x["pnl"], reverse=True)[:10]
+        print("  Top signal trades:")
+        for t in top:
+            print(f"    {t['date']} {t['ticker']:6s} wiki={t['wiki']:.2f} articles={t['articles']} pnl=${t['pnl']:+.0f}")
+
+
+if __name__ == "__main__":
+    main()