Harden the Phase-1 worker contract before real extractors land

Constraint: planner outputs must be copy-runnable in the current environment and live PostgreSQL entrypoints must treat schema input as untrusted.
Rejected: defer state guards until real inference arrives | rejected because repeat execution and empty-scope drift would corrupt Phase-1 evidence now.
Confidence: high
Scope-risk: moderate
Directive: keep using the guarded job contract (expected status, schema validation, explicit python path) when replacing dry-run with real writes.
Tested: py_compile for live bootstrap/planner/worker scripts; live PostgreSQL bootstrap for model registry, reference members, and extraction jobs; regenerated extraction plan report; chromaprint + mert dry-run worker runs with scope=20; double-claim guard report returns non-zero; architect review APPROVED.
Not-tested: real fingerprint writes, real embedding writes, large-scale production reference-set ingestion beyond the 20-song acr_test sample.

@@ -51,8 +51,8 @@
         "target scope: reference_set:phase1_hot_reference_v1"
       ],
       "command_suggestions": [
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\npython workers/run_chromaprint_job.py",
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\n/usr/local/miniconda3/bin/python workers/run_chromaprint_job.py --complete-dry-run",
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
       ]
     },
     {
@@ -96,8 +96,8 @@
         "target scope: reference_set:phase1_hot_reference_v1"
       ],
       "command_suggestions": [
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
       ]
     },
     {
@@ -141,8 +141,8 @@
         "target scope: reference_set:phase1_hot_reference_v1"
       ],
       "command_suggestions": [
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
       ]
     },
     {
@@ -186,8 +186,8 @@
         "target scope: reference_set:phase1_hot_reference_v1"
       ],
       "command_suggestions": [
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
       ]
     },
     {
@@ -231,8 +231,8 @@
         "target scope: reference_set:phase1_hot_reference_v1"
       ],
       "command_suggestions": [
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-        "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+        "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
       ]
     }
   ],
@@ -279,8 +279,8 @@
           "target scope: reference_set:phase1_hot_reference_v1"
         ],
         "command_suggestions": [
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\npython workers/run_chromaprint_job.py",
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\n/usr/local/miniconda3/bin/python workers/run_chromaprint_job.py --complete-dry-run",
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
         ]
       }
     ],
@@ -326,8 +326,8 @@
           "target scope: reference_set:phase1_hot_reference_v1"
         ],
         "command_suggestions": [
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
         ]
       },
       {
@@ -371,8 +371,8 @@
           "target scope: reference_set:phase1_hot_reference_v1"
         ],
         "command_suggestions": [
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
         ]
       },
       {
@@ -416,8 +416,8 @@
           "target scope: reference_set:phase1_hot_reference_v1"
         ],
         "command_suggestions": [
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
         ]
       },
       {
@@ -461,8 +461,8 @@
           "target scope: reference_set:phase1_hot_reference_v1"
         ],
         "command_suggestions": [
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py",
-          "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\npython workers/mark_job_status.py --status running"
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run",
+          "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test \\\n/usr/local/miniconda3/bin/python workers/mark_job_status.py --status running --expected-status pending"
         ]
       }
     ]
@@ -477,7 +477,7 @@
       "window_sec": 5.0,
       "hop_sec": 2.5,
       "physical_target": "audio_fingerprint",
-      "primary_command": "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\npython workers/run_chromaprint_job.py"
+      "primary_command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \\\n/usr/local/miniconda3/bin/python workers/run_chromaprint_job.py --complete-dry-run"
     },
     {
       "order": 2,
@@ -488,7 +488,7 @@
       "window_sec": 5.0,
       "hop_sec": 2.5,
       "physical_target": "audio_embedding",
-      "primary_command": "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py"
+      "primary_command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run"
     },
     {
       "order": 3,
@@ -499,7 +499,7 @@
       "window_sec": 10.0,
       "hop_sec": 5.0,
       "physical_target": "audio_embedding",
-      "primary_command": "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py"
+      "primary_command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=3 FEATURE_SET_ID=4 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run"
     },
     {
       "order": 4,
@@ -510,7 +510,7 @@
       "window_sec": 5.0,
       "hop_sec": 2.5,
       "physical_target": "audio_embedding",
-      "primary_command": "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py"
+      "primary_command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=4 FEATURE_SET_ID=5 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=muq MODEL_VERSION=large-msd-iter VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run"
     },
     {
       "order": 5,
@@ -521,7 +521,7 @@
       "window_sec": 5.0,
       "hop_sec": 2.5,
       "physical_target": "audio_embedding",
-      "primary_command": "PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\npython workers/run_embedding_job.py"
+      "primary_command": "cd /workspace/acr-engine && PG_DSN=\"${PG_DSN:?set PG_DSN}\" EXTRACTION_JOB_ID=5 FEATURE_SET_ID=6 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=ecapa MODEL_VERSION=acr-baseline-v1 VECTOR_TABLE=audio_embedding_vector_192 OUTPUT_TARGET=audio_embedding \\\n/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run"
     }
   ]
 }
\ No newline at end of file

+{
+  "schema": "acr_test",
+  "dsn_redacted": "postgres://d2:***@127.0.0.1:5432/d2",
+  "set_name": "phase1_hot_reference_v1",
+  "member_role": "hot_reference",
+  "inserted_recording_ids": [],
+  "reused_recording_ids": [
+    1,
+    2,
+    3,
+    4,
+    5,
+    6,
+    7,
+    8,
+    9,
+    10,
+    11,
+    12,
+    13,
+    14,
+    15,
+    16,
+    17,
+    18,
+    19,
+    20
+  ],
+  "reference_set_id": 2,
+  "counts": {
+    "reference_recordings_seen": 20,
+    "inserted_members": 0,
+    "reused_members": 20,
+    "member_count": 20,
+    "ready_asset_count": 20,
+    "active_window_count": 20
+  }
+}
\ No newline at end of file

@@ -40,16 +40,16 @@
     "scope_value": "phase1_hot_reference_v1",
     "reference_set_id": 2,
     "reference_set_name": "phase1_hot_reference_v1",
-    "recording_count": 0,
-    "ready_asset_count": 0,
-    "active_window_count": 0
+    "recording_count": 20,
+    "ready_asset_count": 20,
+    "active_window_count": 20
   },
   "status_after_start": {
     "extraction_job_id": 1,
     "job_status": "running",
-    "input_count": 0,
+    "input_count": 20,
     "output_count": null,
-    "started_at": "2026-06-04T13:02:56.589356+08:00",
+    "started_at": "2026-06-04T13:17:25.914513+08:00",
     "finished_at": null,
     "log_uri": null,
     "metadata_json": {
@@ -63,21 +63,21 @@
       "target_scope_summary": {
         "scope_type": "reference_set",
         "scope_value": "phase1_hot_reference_v1",
-        "recording_count": 0,
+        "recording_count": 20,
         "reference_set_id": 2,
-        "ready_asset_count": 0,
+        "ready_asset_count": 20,
         "reference_set_name": "phase1_hot_reference_v1",
-        "active_window_count": 0
+        "active_window_count": 20
       }
     }
   },
   "status_after_complete": {
     "extraction_job_id": 1,
     "job_status": "completed",
-    "input_count": 0,
+    "input_count": 20,
     "output_count": 0,
-    "started_at": "2026-06-04T13:02:56.589356+08:00",
-    "finished_at": "2026-06-04T13:02:56.591597+08:00",
+    "started_at": "2026-06-04T13:17:25.914513+08:00",
+    "finished_at": "2026-06-04T13:17:25.915231+08:00",
     "log_uri": null,
     "metadata_json": {
       "lane": "exact",
@@ -92,11 +92,11 @@
       "target_scope_summary": {
         "scope_type": "reference_set",
         "scope_value": "phase1_hot_reference_v1",
-        "recording_count": 0,
+        "recording_count": 20,
         "reference_set_id": 2,
-        "ready_asset_count": 0,
+        "ready_asset_count": 20,
         "reference_set_name": "phase1_hot_reference_v1",
-        "active_window_count": 0
+        "active_window_count": 20
       }
     }
   },

+{
+  "double_claim_exit_code": 1,
+  "stdout": "",
+  "stderr": "failed to update feature_extraction_job=1 with expected_status=pending\n",
+  "expected_result": "non_zero_exit_due_to_expected_status_guard"
+}
\ No newline at end of file

@@ -39,16 +39,16 @@
     "scope_value": "phase1_hot_reference_v1",
     "reference_set_id": 2,
     "reference_set_name": "phase1_hot_reference_v1",
-    "recording_count": 0,
-    "ready_asset_count": 0,
-    "active_window_count": 0
+    "recording_count": 20,
+    "ready_asset_count": 20,
+    "active_window_count": 20
   },
   "status_after_start": {
     "extraction_job_id": 2,
     "job_status": "running",
-    "input_count": 0,
+    "input_count": 20,
     "output_count": null,
-    "started_at": "2026-06-04T13:02:56.714882+08:00",
+    "started_at": "2026-06-04T13:17:26.054365+08:00",
     "finished_at": null,
     "log_uri": null,
     "metadata_json": {
@@ -63,21 +63,21 @@
       "target_scope_summary": {
         "scope_type": "reference_set",
         "scope_value": "phase1_hot_reference_v1",
-        "recording_count": 0,
+        "recording_count": 20,
         "reference_set_id": 2,
-        "ready_asset_count": 0,
+        "ready_asset_count": 20,
         "reference_set_name": "phase1_hot_reference_v1",
-        "active_window_count": 0
+        "active_window_count": 20
       }
     }
   },
   "status_after_complete": {
     "extraction_job_id": 2,
     "job_status": "completed",
-    "input_count": 0,
+    "input_count": 20,
     "output_count": 0,
-    "started_at": "2026-06-04T13:02:56.714882+08:00",
-    "finished_at": "2026-06-04T13:02:56.715469+08:00",
+    "started_at": "2026-06-04T13:17:26.054365+08:00",
+    "finished_at": "2026-06-04T13:17:26.055184+08:00",
     "log_uri": null,
     "metadata_json": {
       "lane": "semantic",
@@ -93,11 +93,11 @@
       "target_scope_summary": {
         "scope_type": "reference_set",
         "scope_value": "phase1_hot_reference_v1",
-        "recording_count": 0,
+        "recording_count": 20,
         "reference_set_id": 2,
-        "ready_asset_count": 0,
+        "ready_asset_count": 20,
         "reference_set_name": "phase1_hot_reference_v1",
-        "active_window_count": 0
+        "active_window_count": 20
       }
     }
   },

 {
   "worker": "mark_job_status",
   "schema": "acr_test",
+  "job_id": 1,
   "update": {
     "extraction_job_id": 1,
     "job_status": "pending",
-    "input_count": 0,
+    "input_count": 20,
     "output_count": 0,
-    "started_at": "2026-06-04T13:02:56.589356+08:00",
-    "finished_at": "2026-06-04T13:02:56.591597+08:00",
+    "started_at": "2026-06-04T13:17:25.914513+08:00",
+    "finished_at": "2026-06-04T13:17:25.915231+08:00",
     "log_uri": null,
     "metadata_json": {
       "lane": "exact",
       "phase": "phase1",
-      "priority": "p0"
+      "worker": "run_chromaprint_job",
+      "dry_run": true,
+      "priority": "p0",
+      "output_target": "audio_fingerprint",
+      "dry_run_result": "completed_without_feature_write",
+      "execution_mode": "dry_run",
+      "write_target_table": "audio_fingerprint",
+      "target_scope_summary": {
+        "scope_type": "reference_set",
+        "scope_value": "phase1_hot_reference_v1",
+        "recording_count": 20,
+        "reference_set_id": 2,
+        "ready_asset_count": 20,
+        "reference_set_name": "phase1_hot_reference_v1",
+        "active_window_count": 20
+      }
     }
   }
 }
\ No newline at end of file

@@ -4,11 +4,17 @@ from __future__ import annotations
 import argparse
 import json
 from pathlib import Path
+import sys
 from typing import Any
 
 import psycopg
 
 ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from workers._job_common import validate_schema
+
 DEFAULT_OUTPUT = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'phase1_extraction_jobs_report.json'
 
 JOB_SPECS = [
@@ -117,6 +123,11 @@ def ensure_job(conn: psycopg.Connection, feature_set_id: int, job: dict[str, Any
             """
             UPDATE feature_extraction_job
             SET job_status = %s,
+                input_count = NULL,
+                output_count = NULL,
+                started_at = NULL,
+                finished_at = NULL,
+                log_uri = NULL,
                 metadata_json = %s::jsonb
             WHERE extraction_job_id = %s;
             """,
@@ -148,14 +159,15 @@ def main() -> None:
     ap.add_argument('--schema', default='acr_test')
     ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
     args = ap.parse_args()
+    schema = validate_schema(args.schema)
 
     summary: dict[str, Any] = {
-        'schema': args.schema,
+        'schema': schema,
         'dsn_redacted': 'postgres://d2:***@127.0.0.1:5432/d2',
         'jobs': [],
     }
     with psycopg.connect(args.dsn, autocommit=True) as conn:
-        conn.execute(f'SET search_path TO {args.schema}, public;')
+        conn.execute(f'SET search_path TO {schema}, public;')
         for job in JOB_SPECS:
             feature_set_id = resolve_feature_set_id(conn, job)
             extraction_job_id, operation = ensure_job(conn, feature_set_id, job)

@@ -4,11 +4,17 @@ from __future__ import annotations
 import argparse
 import json
 from pathlib import Path
+import sys
 from typing import Any
 
 import psycopg
 
 ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from workers._job_common import validate_schema
+
 DEFAULT_OUTPUT = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'phase1_registry_bootstrap_report.json'
 
 MODELS = [
@@ -360,9 +366,10 @@ def main() -> None:
     ap.add_argument('--schema', default='acr_test')
     ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
     args = ap.parse_args()
+    schema = validate_schema(args.schema)
 
     summary: dict[str, Any] = {
-        'schema': args.schema,
+        'schema': schema,
         'dsn_redacted': 'postgres://d2:***@127.0.0.1:5432/d2',
         'models': [],
         'feature_sets': [],
@@ -370,7 +377,7 @@ def main() -> None:
     }
 
     with psycopg.connect(args.dsn, autocommit=True) as conn:
-        conn.execute(f'SET search_path TO {args.schema}, public;')
+        conn.execute(f'SET search_path TO {schema}, public;')
         model_ids: dict[tuple[str, str], int] = {}
         for model in MODELS:
             model_id, operation = upsert_model(conn, model)

+#!/usr/bin/env /usr/local/miniconda3/bin/python
+from __future__ import annotations
+
+import argparse
+import json
+from pathlib import Path
+import sys
+
+import psycopg
+
+ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from workers._job_common import validate_schema
+
+DEFAULT_OUTPUT = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'phase1_reference_member_bootstrap_report.json'
+
+
+def main() -> None:
+    ap = argparse.ArgumentParser()
+    ap.add_argument('--dsn', required=True)
+    ap.add_argument('--schema', default='acr_test')
+    ap.add_argument('--set-name', default='phase1_hot_reference_v1')
+    ap.add_argument('--member-role', default='hot_reference')
+    ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
+    args = ap.parse_args()
+    schema = validate_schema(args.schema)
+
+    summary: dict[str, object] = {
+        'schema': schema,
+        'dsn_redacted': 'postgres://d2:***@127.0.0.1:5432/d2',
+        'set_name': args.set_name,
+        'member_role': args.member_role,
+        'inserted_recording_ids': [],
+        'reused_recording_ids': [],
+    }
+
+    with psycopg.connect(args.dsn, autocommit=True) as conn:
+        conn.execute(f'SET search_path TO {schema}, public;')
+
+        ref_row = conn.execute(
+            'SELECT reference_set_id FROM reference_set_registry WHERE set_name = %s LIMIT 1;',
+            (args.set_name,),
+        ).fetchone()
+        if not ref_row:
+            raise RuntimeError(f'reference_set_registry.set_name not found: {args.set_name}')
+        reference_set_id = int(ref_row[0])
+        summary['reference_set_id'] = reference_set_id
+
+        recordings = conn.execute(
+            """
+            SELECT recording_id
+            FROM recording
+            WHERE is_reference = TRUE
+            ORDER BY recording_id;
+            """
+        ).fetchall()
+        for row in recordings:
+            recording_id = int(row[0])
+            existing = conn.execute(
+                """
+                SELECT 1
+                FROM reference_set_member
+                WHERE reference_set_id = %s
+                  AND recording_id = %s
+                LIMIT 1;
+                """,
+                (reference_set_id, recording_id),
+            ).fetchone()
+            if existing:
+                summary['reused_recording_ids'].append(recording_id)
+                continue
+            conn.execute(
+                """
+                INSERT INTO reference_set_member (reference_set_id, recording_id, member_role)
+                VALUES (%s, %s, %s);
+                """,
+                (reference_set_id, recording_id, args.member_role),
+            )
+            summary['inserted_recording_ids'].append(recording_id)
+
+        counts = conn.execute(
+            """
+            SELECT
+                count(*) AS member_count,
+                count(DISTINCT ra.asset_id) FILTER (WHERE ra.ingest_status = 'ready') AS ready_asset_count,
+                count(DISTINCT aw.window_id) FILTER (WHERE aw.active_for_index) AS active_window_count
+            FROM reference_set_member rsm
+            LEFT JOIN recording_asset ra ON ra.recording_id = rsm.recording_id
+            LEFT JOIN audio_window aw ON aw.recording_id = rsm.recording_id
+            WHERE rsm.reference_set_id = %s;
+            """,
+            (reference_set_id,),
+        ).fetchone()
+        summary['counts'] = {
+            'reference_recordings_seen': len(recordings),
+            'inserted_members': len(summary['inserted_recording_ids']),
+            'reused_members': len(summary['reused_recording_ids']),
+            'member_count': int(counts[0]),
+            'ready_asset_count': int(counts[1]),
+            'active_window_count': int(counts[2]),
+        }
+
+    output = Path(args.output)
+    output.parent.mkdir(parents=True, exist_ok=True)
+    output.write_text(json.dumps(summary, ensure_ascii=False, indent=2), encoding='utf-8')
+    print(json.dumps(summary, ensure_ascii=False, indent=2))
+
+
+if __name__ == '__main__':
+    main()

@@ -7,11 +7,17 @@ from collections import defaultdict
 from dataclasses import dataclass
 from pathlib import Path
 from statistics import median
+import sys
 from typing import Any
 
 import psycopg
 
 ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from workers._job_common import validate_schema
+
 DEFAULT_SCHEMA_SQL = ROOT / 'sql' / 'acr_pg_schema_v2.sql'
 DEFAULT_REFERENCE = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'reference_embeddings.jsonl'
 DEFAULT_QUERY = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'query_embeddings.jsonl'
@@ -84,6 +90,7 @@ def aggregate_song_scores(rows: list[dict[str, Any]]) -> list[dict[str, Any]]:
 
 
 def reset_schema(conn: psycopg.Connection, schema: str) -> None:
+    schema = validate_schema(schema)
     conn.execute(f'DROP SCHEMA IF EXISTS {schema} CASCADE;')
     conn.execute(f'CREATE SCHEMA {schema};')
     conn.execute(f'SET search_path TO {schema}, public;')
@@ -455,16 +462,17 @@ def main() -> None:
     ap.add_argument('--topk', type=int, default=10)
     ap.add_argument('--reset-schema', action='store_true')
     args = ap.parse_args()
+    schema = validate_schema(args.schema)
 
     refs = load_jsonl(Path(args.reference_embeddings_jsonl))
     queries = load_jsonl(Path(args.query_embeddings_jsonl))
 
     with psycopg.connect(args.dsn, autocommit=True) as conn:
         if args.reset_schema:
-            reset_schema(conn, args.schema)
+            reset_schema(conn, schema)
         else:
-            conn.execute(f'CREATE SCHEMA IF NOT EXISTS {args.schema};')
-            conn.execute(f'SET search_path TO {args.schema}, public;')
+            conn.execute(f'CREATE SCHEMA IF NOT EXISTS {schema};')
+            conn.execute(f'SET search_path TO {schema}, public;')
         apply_schema(conn, Path(args.schema_sql))
         model_id, feature_set_id, reference_set_id, retrieval_index_id = seed_registry(conn)
         entities = ingest_references(conn, refs, feature_set_id, reference_set_id)
@@ -483,7 +491,7 @@ def main() -> None:
         }
 
     payload = {
-        'schema': args.schema,
+        'schema': schema,
         'dsn_redacted': 'postgres://d2:***@127.0.0.1:5432/d2',
         'input': {
             'reference_embeddings_jsonl': args.reference_embeddings_jsonl,

@@ -4,11 +4,17 @@ from __future__ import annotations
 import argparse
 import json
 from pathlib import Path
+import sys
 from typing import Any
 
 import psycopg
 
 ROOT = Path(__file__).resolve().parents[1]
+if str(ROOT) not in sys.path:
+    sys.path.insert(0, str(ROOT))
+
+from workers._job_common import validate_schema
+
 DEFAULT_OUTPUT = ROOT / 'data' / 'pgvector_eval' / 'music20' / 'phase1_extraction_plan_report.json'
 
 LANE_PRIORITY = {
@@ -16,6 +22,7 @@ LANE_PRIORITY = {
     'semantic': 1,
     'cover': 2,
 }
+PYTHON_BIN = '/usr/local/miniconda3/bin/python'
 
 
 def parse_target_scope(target_scope: str) -> dict[str, Any]:
@@ -26,8 +33,10 @@ def parse_target_scope(target_scope: str) -> dict[str, Any]:
 
 
 def build_command_suggestions(job: dict[str, Any], schema: str) -> list[str]:
+    command_prefix = 'cd /workspace/acr-engine && '
     base_env = (
-        'PG_DSN="${PG_DSN:?set PG_DSN}" '
+        command_prefix
+        + 'PG_DSN="${PG_DSN:?set PG_DSN}" '
         f"EXTRACTION_JOB_ID={job['extraction_job_id']} "
         f"FEATURE_SET_ID={job['feature_set_id']} "
         f"TARGET_SCOPE='{job['target_scope']}' "
@@ -37,16 +46,16 @@ def build_command_suggestions(job: dict[str, Any], schema: str) -> list[str]:
     if job['lane'] == 'exact':
         commands.append(
             base_env
-            + " OUTPUT_TARGET=audio_fingerprint \\\npython workers/run_chromaprint_job.py"
+            + f" OUTPUT_TARGET=audio_fingerprint \\\n{PYTHON_BIN} workers/run_chromaprint_job.py --complete-dry-run"
         )
     else:
         commands.append(
             base_env
-            + f" MODEL_NAME={job['model_name']} MODEL_VERSION={job['model_version']} VECTOR_TABLE={job['vector_table']} OUTPUT_TARGET={job['physical_target']} \\\npython workers/run_embedding_job.py"
+            + f" MODEL_NAME={job['model_name']} MODEL_VERSION={job['model_version']} VECTOR_TABLE={job['vector_table']} OUTPUT_TARGET={job['physical_target']} \\\n{PYTHON_BIN} workers/run_embedding_job.py --complete-dry-run"
         )
     commands.append(
         base_env
-        + " \\\npython workers/mark_job_status.py --status running"
+        + f" \\\n{PYTHON_BIN} workers/mark_job_status.py --status running --expected-status pending"
     )
     return commands
 
@@ -59,8 +68,9 @@ def main() -> None:
     ap.add_argument('--output', default=str(DEFAULT_OUTPUT))
     args = ap.parse_args()
 
+    schema = validate_schema(args.schema)
     with psycopg.connect(args.dsn) as conn:
-        conn.execute(f'SET search_path TO {args.schema}, public;')
+        conn.execute(f'SET search_path TO {schema}, public;')
         rows = conn.execute(
             """
             SELECT
@@ -138,7 +148,7 @@ def main() -> None:
                 f"target scope: {row[2]}",
             ],
         }
-        item['command_suggestions'] = build_command_suggestions(item, args.schema)
+        item['command_suggestions'] = build_command_suggestions(item, schema)
         jobs.append(item)
         by_lane.setdefault(lane, []).append(item)
 
@@ -147,7 +157,7 @@ def main() -> None:
         lane_jobs.sort(key=lambda x: x['extraction_job_id'])
 
     payload = {
-        'schema': args.schema,
+        'schema': schema,
         'dsn_redacted': 'postgres://d2:***@127.0.0.1:5432/d2',
         'job_status_filter': args.job_status,
         'counts': {

@@ -10,6 +10,7 @@ from typing import Any
 import psycopg
 
 SCHEMA_RE = re.compile(r'^[A-Za-z_][A-Za-z0-9_]*$')
+ALLOWED_JOB_STATUSES = {'pending', 'running', 'completed', 'failed'}
 
 
 @dataclass
@@ -177,6 +178,7 @@ def update_job_status(
     extraction_job_id: int,
     *,
     status: str,
+    expected_status: str | None = None,
     input_count: int | None = None,
     output_count: int | None = None,
     log_uri: str | None = None,
@@ -184,6 +186,10 @@ def update_job_status(
     set_started_at: bool = False,
     set_finished_at: bool = False,
 ) -> dict[str, Any]:
+    if status not in ALLOWED_JOB_STATUSES:
+        raise SystemExit(f'invalid job status: {status}')
+    if expected_status is not None and expected_status not in ALLOWED_JOB_STATUSES:
+        raise SystemExit(f'invalid expected job status: {expected_status}')
     patch = json.dumps(metadata_patch or {}, ensure_ascii=False)
     row = conn.execute(
         """
@@ -197,11 +203,12 @@ def update_job_status(
                 ELSE started_at
             END,
             finished_at = CASE
-                WHEN %s THEN NOW()
+                WHEN %s THEN COALESCE(finished_at, NOW())
                 ELSE finished_at
             END,
             metadata_json = COALESCE(metadata_json, '{}'::jsonb) || %s::jsonb
         WHERE extraction_job_id = %s
+          AND (%s OR job_status = %s)
         RETURNING extraction_job_id, job_status, input_count, output_count, started_at, finished_at, log_uri, metadata_json;
         """,
         (
@@ -213,10 +220,13 @@ def update_job_status(
             set_finished_at,
             patch,
             extraction_job_id,
+            expected_status is None,
+            expected_status,
         ),
     ).fetchone()
     if not row:
-        raise SystemExit(f'failed to update feature_extraction_job={extraction_job_id}')
+        expectation = f' with expected_status={expected_status}' if expected_status else ''
+        raise SystemExit(f'failed to update feature_extraction_job={extraction_job_id}{expectation}')
     return {
         'extraction_job_id': int(row[0]),
         'job_status': row[1],

@@ -5,15 +5,16 @@ import argparse
 import json
 import os
 
-from _job_common import connect, emit_payload, require_env, update_job_status
+from _job_common import ALLOWED_JOB_STATUSES, connect, emit_payload, update_job_status
 
 
 def main() -> None:
     ap = argparse.ArgumentParser()
     ap.add_argument('--dsn', default=os.environ.get('PG_DSN'))
     ap.add_argument('--schema', default=os.environ.get('PG_SCHEMA', 'acr_test'))
-    ap.add_argument('--job-id', type=int, default=int(require_env('EXTRACTION_JOB_ID', '0')))
-    ap.add_argument('--status', required=True)
+    ap.add_argument('--job-id', type=int)
+    ap.add_argument('--status', required=True, choices=sorted(ALLOWED_JOB_STATUSES))
+    ap.add_argument('--expected-status', choices=sorted(ALLOWED_JOB_STATUSES))
     ap.add_argument('--input-count', type=int)
     ap.add_argument('--output-count', type=int)
     ap.add_argument('--log-uri')
@@ -25,7 +26,8 @@ def main() -> None:
 
     if not args.dsn:
         raise SystemExit('missing --dsn or PG_DSN')
-    if not args.job_id:
+    job_id = args.job_id or int(os.environ.get('EXTRACTION_JOB_ID', '0'))
+    if not job_id:
         raise SystemExit('missing --job-id or EXTRACTION_JOB_ID')
 
     metadata_patch = json.loads(args.metadata_json) if args.metadata_json else {}
@@ -33,8 +35,9 @@ def main() -> None:
     with connect(args.dsn, args.schema) as conn:
         updated = update_job_status(
             conn,
-            args.job_id,
+            job_id,
             status=args.status,
+            expected_status=args.expected_status,
             input_count=args.input_count,
             output_count=args.output_count,
             log_uri=args.log_uri,
@@ -47,6 +50,7 @@ def main() -> None:
         {
             'worker': 'mark_job_status',
             'schema': args.schema,
+            'job_id': job_id,
             'update': updated,
         },
         args.output,

@@ -31,6 +31,7 @@ def main() -> None:
             conn,
             job.extraction_job_id,
             status='running',
+            expected_status='pending',
             input_count=scope['ready_asset_count'],
             metadata_patch={
                 'worker': 'run_chromaprint_job',
@@ -47,6 +48,7 @@ def main() -> None:
                 conn,
                 job.extraction_job_id,
                 status='completed',
+                expected_status='running',
                 output_count=0,
                 metadata_patch={
                     'worker': 'run_chromaprint_job',

@@ -34,6 +34,11 @@ def main() -> None:
         job = fetch_job_context(conn, args.job_id)
         if job.model_name == 'chromaprint':
             raise SystemExit(f'feature_extraction_job={args.job_id} is not an embedding job')
+        if job.feature_name != 'semantic_embedding' or job.feature_level != 'window':
+            raise SystemExit(
+                f'feature_extraction_job={args.job_id} does not match embedding feature contract: '
+                f'{job.feature_name}/{job.feature_level}'
+            )
         if args.model_name and job.model_name != args.model_name:
             raise SystemExit(f'model mismatch: job={job.model_name} cli={args.model_name}')
         if args.model_version and job.model_version != args.model_version:
@@ -44,6 +49,7 @@ def main() -> None:
             conn,
             job.extraction_job_id,
             status='running',
+            expected_status='pending',
             input_count=scope['active_window_count'] or scope['ready_asset_count'],
             metadata_patch={
                 'worker': 'run_embedding_job',
@@ -61,6 +67,7 @@ def main() -> None:
                 conn,
                 job.extraction_job_id,
                 status='completed',
+                expected_status='running',
                 output_count=0,
                 metadata_patch={
                     'worker': 'run_embedding_job',

 ## 2026-06-04
 
+- 新增 `bootstrap_phase1_reference_members_live.py` 与 `phase1_reference_member_bootstrap_report.json`，把 `acr_test` 中 `recording.is_reference=true` 的 20 条录音真实挂到 `phase1_hot_reference_v1`，使 worker dry-run 的 scope 从 `0` 提升为 `20 recordings / 20 assets / 20 windows`。
+- 根据 architect 复核修正 worker contract：`mark_job_status.py` 现支持真正的“CLI 覆盖 env”并限制状态白名单；`_job_common.update_job_status()` 新增前置状态约束并防止 `finished_at` 被重复覆盖；`bootstrap_phase1_extraction_jobs_live.py` 在恢复 pending 时会清空旧时间戳与计数；`run_embedding_job.py` 对 embedding job 契约做了更严格校验。
+- 修正 `plan_phase1_extraction_jobs_live.py`：新增 schema 校验，命令模板显式锚定 `cd /workspace/acr-engine &&`，并把 `--complete-dry-run` 与 `--expected-status pending` 带入生成的命令，避免 planner 产物“看起来能跑但实际上缺关键上下文/步骤”。
+- 新增 `phase1_worker_double_claim_guard_report.json`，通过对同一 chromaprint job 连续执行两次 dry-run 验证前置状态保护已生效：第二次执行被 `expected_status=pending` 明确拒绝。
+- 把 `validate_schema()` 统一推广到 `bootstrap_phase1_model_registry_live.py`、`bootstrap_phase1_reference_members_live.py`、`bootstrap_phase1_extraction_jobs_live.py` 与 `live_pgvector_music20_eval.py`，补齐整个 PostgreSQL live CLI 链的 schema 参数保护。
 - 新增 [Phase-1 Worker Contract](./phase1-worker-contract.md) 与 `acr-engine/workers/_job_common.py`、`mark_job_status.py`、`run_chromaprint_job.py`、`run_embedding_job.py`，把 Phase-1 从“只有 planner 命令模板”推进到“worker 可以真实消费 PostgreSQL 的 `feature_extraction_job` 并执行 `pending -> running -> completed` dry-run 状态流转”的阶段。
-- 新增 `phase1_worker_chromaprint_dry_run.json`、`phase1_worker_embedding_dry_run.json` 与 `phase1_worker_mark_pending_report.json`，并在 live PostgreSQL `acr_test` 上验证了 worker 状态流转；同时确认当前 `phase1_hot_reference_v1` 还没有实际 members，因此 scope 计数为 `0`，这是数据未装载而不是 worker 失败。
+- 新增 `phase1_worker_chromaprint_dry_run.json`、`phase1_worker_embedding_dry_run.json` 与 `phase1_worker_mark_pending_report.json`，并在 live PostgreSQL `acr_test` 上验证了 worker 状态流转；初次 dry-run 曾暴露 `phase1_hot_reference_v1` 缺少实际 members，随后已在同日补齐到 `20` 个 members。
 - 修正 `plan_phase1_extraction_jobs_live.py` 的命令模板，把 `PG_DSN=\"${PG_DSN:?set PG_DSN}\"` 显式写入 `command_suggestions / primary_command`，避免 planner 产物看起来可跑但实际缺少数据库连接串。
 - 更新 `plan_phase1_extraction_jobs_live.py` 与 `phase1_extraction_plan_report.json`，把 Phase-1 execution plan 从“仅有排序计划”推进到“附带 `command_suggestions / primary_command` 的可复制执行命令模板”。
 - 新增 `acr-engine/scripts/plan_phase1_extraction_jobs_live.py` 与 `acr-engine/data/pgvector_eval/music20/phase1_extraction_plan_report.json`，支持从 PostgreSQL 的 `feature_extraction_job` 真实读取 pending jobs，并联表生成按 lane / priority 排序的 Phase-1 execution plan。

@@ -223,6 +223,7 @@ flowchart TD
 
 当前已经不只是 registry/bootstrap 了，还补上了最小真实 worker 执行面：
 
+- `acr-engine/scripts/bootstrap_phase1_reference_members_live.py`
 - `acr-engine/workers/mark_job_status.py`
 - `acr-engine/workers/run_chromaprint_job.py`
 - `acr-engine/workers/run_embedding_job.py`
@@ -437,15 +438,15 @@ cd /workspace/acr-engine
 #### exact lane
 
 ```bash
-EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \
-python workers/run_chromaprint_job.py
+cd /workspace/acr-engine && PG_DSN="${PG_DSN:?set PG_DSN}" EXTRACTION_JOB_ID=1 FEATURE_SET_ID=2 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test OUTPUT_TARGET=audio_fingerprint \
+/usr/local/miniconda3/bin/python workers/run_chromaprint_job.py --complete-dry-run
 ```
 
 #### semantic lane
 
 ```bash
-EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \
-python workers/run_embedding_job.py
+cd /workspace/acr-engine && PG_DSN="${PG_DSN:?set PG_DSN}" EXTRACTION_JOB_ID=2 FEATURE_SET_ID=3 TARGET_SCOPE='reference_set:phase1_hot_reference_v1' PG_SCHEMA=acr_test MODEL_NAME=mert MODEL_VERSION=v1-95m VECTOR_TABLE=audio_embedding_vector_768 OUTPUT_TARGET=audio_embedding \
+/usr/local/miniconda3/bin/python workers/run_embedding_job.py --complete-dry-run
 ```
 
 这意味着下个 session 不需要先手工拼环境变量和 job 绑定关系，而可以直接从 planner 报告里复制命令模板。

@@ -26,6 +26,7 @@
 
 位于：
 
+- `acr-engine/scripts/bootstrap_phase1_reference_members_live.py`
 - `acr-engine/workers/mark_job_status.py`
 - `acr-engine/workers/run_chromaprint_job.py`
 - `acr-engine/workers/run_embedding_job.py`
@@ -40,6 +41,21 @@
 | `run_embedding_job.py` | semantic lane worker |
 | `_job_common.py` | 共享的 job 读取、scope 解析、状态回写逻辑 |
 
+### 配套 bootstrap
+
+为了让 worker 不再面对空 scope，这轮还补上了：
+
+- `acr-engine/scripts/bootstrap_phase1_reference_members_live.py`
+
+它会把当前 `recording.is_reference = true` 的录音挂到：
+
+- `phase1_hot_reference_v1`
+
+这样 worker 可以真实看到：
+- `recording_count`
+- `ready_asset_count`
+- `active_window_count`
+
 ---
 
 ## 2. 当前状态机
@@ -56,6 +72,30 @@ flowchart LR
 - `pending -> running`
 - `running -> completed`（dry-run 模式）
 
+### 当前状态保护
+
+- worker 认领 job 时要求前置状态为 `pending`
+- worker 完成 job 时要求前置状态为 `running`
+- `mark_job_status.py` 只接受：
+  - `pending`
+  - `running`
+  - `completed`
+  - `failed`
+- `finished_at` 只在首次完成时落值，不再被重复覆盖
+
+### 已验证的 guard 行为
+
+当前已真实验证：
+
+1. 同一 chromaprint job 第一次 dry-run：
+   - 成功 `pending -> running -> completed`
+2. 不做 reset，直接第二次执行同一 job：
+   - 被前置状态保护拒绝
+
+对应证据：
+
+- `acr-engine/data/pgvector_eval/music20/phase1_worker_double_claim_guard_report.json`
+
 ### 设计意图
 
 先把 **作业契约与状态流转** 固定住，再把真正的模型推理塞进去。  
@@ -94,11 +134,31 @@ flowchart LR
 `plan_phase1_extraction_jobs_live.py` 现在会显式生成：
 
 ```bash
-PG_DSN="${PG_DSN:?set PG_DSN}" ...
+cd /workspace/acr-engine && PG_DSN="${PG_DSN:?set PG_DSN}" ...
 ```
 
 这样复制命令时，如果调用方忘了提供数据库连接串，会立刻失败，而不是静默跑空。
 
+当前 planner 还会显式使用：
+
+```bash
+/usr/local/miniconda3/bin/python
+```
+
+原因是当前环境里 `python` 不在 PATH 上，但这个解释器路径已被验证可用。
+
+对于当前 dry-run worker，planner 的主命令模板也会显式带上：
+
+```bash
+--complete-dry-run
+```
+
+这样 `primary_command` 就能直接复现：
+
+```text
+pending -> running -> completed
+```
+
 ---
 
 ## 4. PostgreSQL 读取契约

@@ -69,9 +69,11 @@
 | registry bootstrap 幂等性报告 | `acr-engine/data/pgvector_eval/music20/phase1_registry_bootstrap_idempotency_report.json` |
 | extraction job bootstrap 报告 | `acr-engine/data/pgvector_eval/music20/phase1_extraction_jobs_report.json` |
 | extraction plan 报告 | `acr-engine/data/pgvector_eval/music20/phase1_extraction_plan_report.json` |
+| reference member bootstrap 报告 | `acr-engine/data/pgvector_eval/music20/phase1_reference_member_bootstrap_report.json` |
 | chromaprint worker dry-run 报告 | `acr-engine/data/pgvector_eval/music20/phase1_worker_chromaprint_dry_run.json` |
 | embedding worker dry-run 报告 | `acr-engine/data/pgvector_eval/music20/phase1_worker_embedding_dry_run.json` |
 | job status 手工回写报告 | `acr-engine/data/pgvector_eval/music20/phase1_worker_mark_pending_report.json` |
+| double-claim guard 报告 | `acr-engine/data/pgvector_eval/music20/phase1_worker_double_claim_guard_report.json` |
 | 历史对照报告 | `acr-engine/data/pgvector_eval/music20/songid_eval_report.json` |
 
 ---
@@ -309,6 +311,13 @@ flowchart TD
    - 把 job 状态恢复为 `pending`
    - 保证后续 session 可以从同一批 jobs 继续推进
 
+4. `plan_phase1_extraction_jobs_live.py`
+   - 当前生成的主命令模板已显式带：
+     - `cd /workspace/acr-engine &&`
+     - `PG_DSN="${PG_DSN:?set PG_DSN}"`
+     - `--complete-dry-run`
+   - 因此 `primary_command` 已经可以直接复现当前 dry-run 状态流转
+
 ### 为什么先做 dry-run
 
 因为当前第一优先级是把下面这些东西固定住：
@@ -324,21 +333,51 @@ flowchart TD
 
 接进去，整体风险更低。
 
-### 当前 live 结果的一个关键解释
+### 当前 live 结果的关键更新
+
+本轮已经新增：
 
-本轮 worker dry-run 里，`phase1_hot_reference_v1` 已经存在，但在 `acr_test` schema 里**还没有实际 member**，所以：
+- `acr-engine/scripts/bootstrap_phase1_reference_members_live.py`
 
-- `recording_count=0`
-- `ready_asset_count=0`
-- `active_window_count=0`
+并已把 `acr_test.phase1_hot_reference_v1` 真实挂上 `20` 条 reference recordings，因此当前 worker dry-run 看到的 scope 已变成：
 
-这不是 worker 异常，而是当前 Phase-1 live 数据面还没把业务 reference recordings 真实装进去。
-因此这轮验证证明的是：
+- `recording_count=20`
+- `ready_asset_count=20`
+- `active_window_count=20`
+
+这说明当前验证已经从“空 scope 状态机演示”推进到：
 
 - planner -> worker 命令兼容
 - worker -> PostgreSQL 状态流转可用
+- reference_set -> recording/asset/window scope 解析可用
+
+仍然要注意：
+
+- 这依然是 **dry-run**
+- 还**不是**真实特征抽取吞吐验证
+
+### 当前并发/重试保护验证
+
+本轮还额外做了一个故意的重复执行测试：
+
+1. 先让 `feature_extraction_job=1` 从 `pending -> running -> completed`
+2. 不做 reset，直接再次执行同一个 chromaprint dry-run worker
+3. 预期第二次执行失败，因为 worker 认领 job 时要求：
+   - `expected_status = pending`
+
+实际结果见：
+
+- `phase1_worker_double_claim_guard_report.json`
+
+关键证据：
+
+- `double_claim_exit_code = 1`
+- `stderr = failed to update feature_extraction_job=1 with expected_status=pending`
+
+这证明当前最小 worker contract 已经具备：
 
-还**不是**真实特征抽取吞吐验证。
+- 基础 claim guard
+- 基础重复执行保护
 - `type_7`
 
 因此：

@@ -189,12 +189,13 @@ sed -n '1,320p' acr-engine/sql/acr_pg_schema_v2.sql
 - extraction plan 报告里已包含 `command_suggestions / primary_command`，下次可直接从 plan 抄 worker 命令模板
 - Phase-1 worker 入口已真实落地：`run_chromaprint_job.py / run_embedding_job.py / mark_job_status.py`
 - 下一阶段已经不是“补 planner”，而是把 dry-run worker 替换为真实 extractor，并把 `audio_fingerprint / audio_embedding` 写入做成幂等执行
-- 最新 live worker 证据表明：`phase1_hot_reference_v1` 当前在 `acr_test` 里还没有实际 members，所以 dry-run 已验证状态机，但 scope 计数仍是 `0`
+- `phase1_hot_reference_v1` 在 `acr_test` 里已经真实补齐 `20` 个 reference members，因此 worker dry-run 当前看到的 scope 已是 `20 recordings / 20 assets / 20 windows`
+- worker contract 现在已有基础前置状态保护；重复执行同一 chromaprint dry-run job 会被 `expected_status=pending` 明确拒绝，证据见 `phase1_worker_double_claim_guard_report.json`
 
 ### 未验证 / 仍是缺口
 - **未实际跑 MERT / MuQ encoder-only 特征抽取**
 - **worker 目前仍以 dry-run 为主，尚未写真实 `audio_fingerprint / audio_embedding`**
-- **未落 reference set 的真实业务数据**
+- **还未落更大规模的生产 reference set 真实业务数据（当前仅验证了 `acr_test` 下的 20-song live members）**
 - **未定义最终线上分数融合细则**
 - **type_8 / type_16 还没有进入当前 live JSONL 的 PostgreSQL 实测链**
 - **当前容器里缺少 `/workspace/downloads`，因此暂时无法直接从业务样本目录继续补 type_8 / type_16 live query**