diff --git a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
index ef974dc176e51..0e6b69ae274e1 100644
--- a/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
+++ b/sql/catalyst/src/main/scala/org/apache/spark/sql/internal/SQLConf.scala
@@ -509,7 +509,7 @@ object SQLConf {
         "'spark.sql.adaptive.skewJoin.skewedPartitionThresholdInBytes'")
       .version("3.0.0")
       .intConf
-      .checkValue(_ > 0, "The skew factor must be positive.")
+      .checkValue(_ >= 0, "The skew factor cannot be negative.")
       .createWithDefault(5)
 
   val SKEW_JOIN_SKEWED_PARTITION_THRESHOLD =
diff --git a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
index f003830f3a45d..45ba2202d83d3 100644
--- a/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
+++ b/sql/core/src/test/scala/org/apache/spark/sql/execution/adaptive/AdaptiveQueryExecSuite.scala
@@ -30,7 +30,7 @@ import org.apache.spark.sql.execution.{PartialReducerPartitionSpec, QueryExecuti
 import org.apache.spark.sql.execution.command.DataWritingCommandExec
 import org.apache.spark.sql.execution.datasources.noop.NoopDataSource
 import org.apache.spark.sql.execution.datasources.v2.V2TableWriteExec
-import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, Exchange, REPARTITION_WITH_NUM, ReusedExchangeExec, ShuffleExchangeExec, ShuffleExchangeLike}
+import org.apache.spark.sql.execution.exchange.{BroadcastExchangeExec, Exchange, REPARTITION, REPARTITION_WITH_NUM, ReusedExchangeExec, ShuffleExchangeExec, ShuffleExchangeLike}
 import org.apache.spark.sql.execution.joins.{BaseJoinExec, BroadcastHashJoinExec, SortMergeJoinExec}
 import org.apache.spark.sql.execution.ui.SparkListenerSQLAdaptiveExecutionUpdate
 import org.apache.spark.sql.functions._
@@ -1319,8 +1319,15 @@ class AdaptiveQueryExecSuite
   }
 
   test("SPARK-33551: Do not use custom shuffle reader for repartition") {
+    def hasRepartitionShuffle(plan: SparkPlan): Boolean = {
+      find(plan) {
+        case s: ShuffleExchangeLike =>
+          s.shuffleOrigin == REPARTITION || s.shuffleOrigin == REPARTITION_WITH_NUM
+        case _ => false
+      }.isDefined
+    }
+
     withSQLConf(SQLConf.ADAPTIVE_EXECUTION_ENABLED.key -> "true",
-      SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "80",
       SQLConf.SHUFFLE_PARTITIONS.key -> "5") {
       val df = sql(
         """
@@ -1331,45 +1338,97 @@ class AdaptiveQueryExecSuite
           |ON value = b
         """.stripMargin)
 
-      // Repartition with no partition num specified.
-      val dfRepartition = df.repartition('b)
-      dfRepartition.collect()
-      val plan = dfRepartition.queryExecution.executedPlan
-      val bhj = findTopLevelBroadcastHashJoin(plan)
-      assert(bhj.length == 1)
-      checkNumLocalShuffleReaders(plan, 1)
-      // Probe side is coalesced.
-      val customReader = bhj.head.right.find(_.isInstanceOf[CustomShuffleReaderExec])
-      assert(customReader.isDefined)
-      assert(customReader.get.asInstanceOf[CustomShuffleReaderExec].hasCoalescedPartition)
-
-      // Repartition with partition default num specified.
-      val dfRepartitionWithNum = df.repartition(5, 'b)
-      dfRepartitionWithNum.collect()
-      val planWithNum = dfRepartitionWithNum.queryExecution.executedPlan
-      val bhjWithNum = findTopLevelBroadcastHashJoin(planWithNum)
-      assert(bhjWithNum.length == 1)
-      checkNumLocalShuffleReaders(planWithNum, 1)
-      // Probe side is not coalesced.
-      assert(bhjWithNum.head.right.find(_.isInstanceOf[CustomShuffleReaderExec]).isEmpty)
-
-      // Repartition with partition non-default num specified.
-      val dfRepartitionWithNum2 = df.repartition(3, 'b)
-      dfRepartitionWithNum2.collect()
-      val planWithNum2 = dfRepartitionWithNum2.queryExecution.executedPlan
-      val bhjWithNum2 = findTopLevelBroadcastHashJoin(planWithNum2)
-      assert(bhjWithNum2.length == 1)
-      // The top shuffle from repartition is not optimized out, and this is the only shuffle that
-      // does not have local shuffle reader.
-      val repartition = find(planWithNum2) {
-        case s: ShuffleExchangeLike => s.shuffleOrigin == REPARTITION_WITH_NUM
-        case _ => false
+      withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "80") {
+        // Repartition with no partition num specified.
+        val dfRepartition = df.repartition('b)
+        dfRepartition.collect()
+        val plan = dfRepartition.queryExecution.executedPlan
+        // The top shuffle from repartition is optimized out.
+        assert(!hasRepartitionShuffle(plan))
+        val bhj = findTopLevelBroadcastHashJoin(plan)
+        assert(bhj.length == 1)
+        checkNumLocalShuffleReaders(plan, 1)
+        // Probe side is coalesced.
+        val customReader = bhj.head.right.find(_.isInstanceOf[CustomShuffleReaderExec])
+        assert(customReader.isDefined)
+        assert(customReader.get.asInstanceOf[CustomShuffleReaderExec].hasCoalescedPartition)
+
+        // Repartition with partition default num specified.
+        val dfRepartitionWithNum = df.repartition(5, 'b)
+        dfRepartitionWithNum.collect()
+        val planWithNum = dfRepartitionWithNum.queryExecution.executedPlan
+        // The top shuffle from repartition is optimized out.
+        assert(!hasRepartitionShuffle(planWithNum))
+        val bhjWithNum = findTopLevelBroadcastHashJoin(planWithNum)
+        assert(bhjWithNum.length == 1)
+        checkNumLocalShuffleReaders(planWithNum, 1)
+        // Probe side is not coalesced.
+        assert(bhjWithNum.head.right.find(_.isInstanceOf[CustomShuffleReaderExec]).isEmpty)
+
+        // Repartition with partition non-default num specified.
+        val dfRepartitionWithNum2 = df.repartition(3, 'b)
+        dfRepartitionWithNum2.collect()
+        val planWithNum2 = dfRepartitionWithNum2.queryExecution.executedPlan
+        // The top shuffle from repartition is not optimized out, and this is the only shuffle that
+        // does not have local shuffle reader.
+        assert(hasRepartitionShuffle(planWithNum2))
+        val bhjWithNum2 = findTopLevelBroadcastHashJoin(planWithNum2)
+        assert(bhjWithNum2.length == 1)
+        checkNumLocalShuffleReaders(planWithNum2, 1)
+        val customReader2 = bhjWithNum2.head.right.find(_.isInstanceOf[CustomShuffleReaderExec])
+        assert(customReader2.isDefined)
+        assert(customReader2.get.asInstanceOf[CustomShuffleReaderExec].isLocalReader)
+      }
+
+      // Force skew join
+      withSQLConf(SQLConf.AUTO_BROADCASTJOIN_THRESHOLD.key -> "-1",
+        SQLConf.SKEW_JOIN_ENABLED.key -> "true",
+        SQLConf.SKEW_JOIN_SKEWED_PARTITION_THRESHOLD.key -> "1",
+        SQLConf.SKEW_JOIN_SKEWED_PARTITION_FACTOR.key -> "0",
+        SQLConf.ADVISORY_PARTITION_SIZE_IN_BYTES.key -> "10") {
+        // Repartition with no partition num specified.
+        val dfRepartition = df.repartition('b)
+        dfRepartition.collect()
+        val plan = dfRepartition.queryExecution.executedPlan
+        // The top shuffle from repartition is optimized out.
+        assert(!hasRepartitionShuffle(plan))
+        val smj = findTopLevelSortMergeJoin(plan)
+        assert(smj.length == 1)
+        // No skew join due to the repartition.
+        assert(!smj.head.isSkewJoin)
+        // Both sides are coalesced.
+        val customReaders = collect(smj.head) {
+          case c: CustomShuffleReaderExec if c.hasCoalescedPartition => c
+        }
+        assert(customReaders.length == 2)
+
+        // Repartition with default partition num specified.
+        val dfRepartitionWithNum = df.repartition(5, 'b)
+        dfRepartitionWithNum.collect()
+        val planWithNum = dfRepartitionWithNum.queryExecution.executedPlan
+        // The top shuffle from repartition is optimized out.
+        assert(!hasRepartitionShuffle(planWithNum))
+        val smjWithNum = findTopLevelSortMergeJoin(planWithNum)
+        assert(smjWithNum.length == 1)
+        // No skew join due to the repartition.
+        assert(!smjWithNum.head.isSkewJoin)
+        // No coalesce due to the num in repartition.
+        val customReadersWithNum = collect(smjWithNum.head) {
+          case c: CustomShuffleReaderExec if c.hasCoalescedPartition => c
+        }
+        assert(customReadersWithNum.isEmpty)
+
+        // Repartition with default non-partition num specified.
+        val dfRepartitionWithNum2 = df.repartition(3, 'b)
+        dfRepartitionWithNum2.collect()
+        val planWithNum2 = dfRepartitionWithNum2.queryExecution.executedPlan
+        // The top shuffle from repartition is not optimized out.
+        assert(hasRepartitionShuffle(planWithNum2))
+        val smjWithNum2 = findTopLevelSortMergeJoin(planWithNum2)
+        assert(smjWithNum2.length == 1)
+        // Skew join can apply as the repartition is not optimized out.
+        assert(smjWithNum2.head.isSkewJoin)
       }
-      assert(repartition.isDefined)
-      checkNumLocalShuffleReaders(planWithNum2, 1)
-      val customReader2 = bhjWithNum2.head.right.find(_.isInstanceOf[CustomShuffleReaderExec])
-      assert(customReader2.isDefined)
-      assert(customReader2.get.asInstanceOf[CustomShuffleReaderExec].isLocalReader)
     }
   }
 }