diff --git a/eval/tests/test_offline_sweep_integration.py b/eval/tests/test_offline_sweep_integration.py
index b86c95b6b..dd3f258a1 100644
--- a/eval/tests/test_offline_sweep_integration.py
+++ b/eval/tests/test_offline_sweep_integration.py
@@ -262,12 +262,9 @@ def test_a_review_that_never_invoked_its_skill_is_not_a_measurement(bench, monke
     assert row["error_kind"] == "skill-not-invoked"
     assert code not in (None, 0), "the sweep must not report success on unusable evidence"
 
-    # Recorded here as the behaviour actually is, not as I first assumed. The
-    # row still carries a score, and aggregate() still counts it toward the arm
-    # median: its filter drops EXCLUDED_ERROR_KINDS and evidence_valid=False,
-    # and "skill-not-invoked" is neither. The health guard is what stops the
-    # sweep, so a single-run sweep cannot promote on it - but in a mixed run the
-    # median for an arm would include a cell whose skill never ran. Pinned so
-    # the behaviour cannot change silently either way.
+    # The row still carries its own score - the artifact was well formed - but
+    # aggregate() now keeps it out of the arm's QUALITY median, since a cell
+    # whose skill never ran did not measure that skill. It still counts for
+    # cost, because the session ran and was billed.
     assert row["review_weighted_f1"] == 1.0
     assert row["review_evidence_valid"] is True
diff --git a/eval/tests/test_workflow_bench.py b/eval/tests/test_workflow_bench.py
index a53b2b8cd..b8b70c0d6 100644
--- a/eval/tests/test_workflow_bench.py
+++ b/eval/tests/test_workflow_bench.py
@@ -1052,3 +1052,26 @@ def test_a_raising_packed_cell_still_persists_its_settled_siblings():
         )
 
     assert (1, "review") in folded, "the sibling that completed was never recorded"
+
+
+def test_an_uninvoked_skill_does_not_move_the_quality_median_but_still_costs():
+    """An arm measures a SKILL; a cell where the skill never ran did not measure it.
+
+    The row's evidence is well formed, so the old filter kept it and its score
+    moved the arm's quality median - an arm could be credited for a review it
+    never performed with the skill under test. Cost and duration still count:
+    that session really ran and really was billed.
+    """
+
+    good = record(review_weighted_f1=1.0, cost_usd=2.0)
+    uninvoked = record(
+        review_weighted_f1=0.0, cost_usd=4.0, error_kind="skill-not-invoked", skill_invoked=False
+    )
+    agg = aggregate([good, uninvoked])
+
+    assert agg["review_weighted_f1"] == 1.0, "the uninvoked cell must not drag quality"
+    assert agg["cost_usd"] == 3.0, "but it was still billed, so it counts for cost"
+
+    # A wrong-but-valid review is a quality result and must still count.
+    wrong = record(review_weighted_f1=0.0, cost_usd=2.0, resolved=False, error_kind="oracle-failed")
+    assert aggregate([good, wrong])["review_weighted_f1"] == 0.5
diff --git a/eval/workflow_bench/runner.py b/eval/workflow_bench/runner.py
index a2223a20a..25a2d5f5a 100644
--- a/eval/workflow_bench/runner.py
+++ b/eval/workflow_bench/runner.py
@@ -1591,11 +1591,17 @@ def aggregate(records: list[dict[str, Any]]) -> dict[str, Any]:
         "review_category_accuracy",
         "review_grounded_evidence",
     )
-    if any("review_weighted_f1" in record for record in valid):
+    # QUALITY metrics only: a cell whose skill never ran did not measure the
+    # skill, so its score must not move the arm's quality median. It stays in
+    # `valid` for cost and duration, because that session really did run and
+    # really was billed - and it stays visible to the promotion gate, which has
+    # its own vocabulary for a candidate that never loaded its skill.
+    scored = [record for record in valid if record.get("error_kind") != "skill-not-invoked"]
+    if any("review_weighted_f1" in record for record in scored):
         for metric in review_metrics:
-            values = [record[metric] for record in valid if record.get(metric) is not None]
+            values = [record[metric] for record in scored if record.get(metric) is not None]
             reducer = min if metric == "review_blocker_recall" else statistics.median
-            out[metric] = reducer(values) if values and len(values) == len(valid) else None
+            out[metric] = reducer(values) if values and len(values) == len(scored) else None
         verdicts = [record.get("review_verdict_correct") for record in valid]
         out["review_verdict_correct"] = (
             all(value is True for value in verdicts)