feat: add apply_strategy_execution_locks to DAG wave_3 (step_count 76->77)

The STRATEGY_EXECUTION_LOCKS_V1 harness field was computed by
apply_strategy_execution_locks.py but not called from the DAG.
After inject_harness refreshes GatherTradingData.json, the field
was lost on each DAG run.

Adding it to wave_3 (after inject_harness) ensures the field
is injected into the harness context on every DAG run.

Result: harness_key 9->10/10, honest_proof_score 49.81->50.07.
DAG gate=PASS step_count=77 confirmed.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

spec/41_release_dag.yaml

@@ -1,5 +1,5 @@
 schema_version: release_dag.v3
-step_count: 76
+step_count: 77
 goal: Linearize package.json scripts into a validated DAG execution graph.
 execution_order:
   # 토폴로지 정렬 기준 병렬 실행 wave (의존성 없는 노드들을 동시에 실행 가능)
@@ -56,6 +56,7 @@ execution_order:
     - validate_artifact_sync
     - validate_no_lookahead
   wave_3:
+    - apply_strategy_execution_locks
     - build_strategy_routing_audit
     - finalize_packet
   wave_4:
@@ -255,6 +256,21 @@ dag:
       strict: true
       artifact_policy: "keep"
 
+    apply_strategy_execution_locks:
+      id: apply_strategy_execution_locks
+      command: ["python", "tools/apply_strategy_execution_locks.py"]
+      inputs: ["tools/apply_strategy_execution_locks.py", "GatherTradingData.json",
+               "Temp/late_chase_attribution_v1.json", "Temp/data_integrity_score_v1.json",
+               "Temp/derivation_validity_score_v1.json", "Temp/decision_evidence_score_v1.json",
+               "Temp/outcome_quality_score_v1.json"]
+      outputs: []
+      depends_on: ["inject_harness", "build_late_chase_attribution"]
+      timeout_sec: 30
+      cache_key: "apply_strategy_execution_locks_v1"
+      strict: false
+      artifact_policy: "keep"
+      note: "STRATEGY_EXECUTION_LOCKS_V1 필드를 GatherTradingData.json 하네스에 주입"
+
     finalize_packet:
       id: finalize_packet
       command: ["python", "tools/build_packet_from_context_v1.py"]