Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:21:47.869671+00:00",
+  "ended_at": "2026-05-19T16:21:51.190454+00:00",
+  "elapsed_ms": 3320.75,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7987,
+    "bytes_utf8": 7987,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:21:52.192310+00:00",
+  "ended_at": "2026-05-19T16:21:55.554260+00:00",
+  "elapsed_ms": 3361.92,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7987,
+    "bytes_utf8": 7987,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Vehicle_Damage, measure_col=Region_Code.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "Vehicle_Damage",
+    "measure_col": "Region_Code",
+    "top_k": 11,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 24.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,240 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=Vehicle_Damage, measure_col=Region_Code.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "Vehicle_Damage",
+    "measure_col": "Region_Code",
+    "top_k": 11,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 24.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410b-1de8-7df0-824b-f726ca8d36dd"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410b-1de8-7df0-824b-f726ca8d36dd"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410b-2edc-73e0-ab92-d2a7f1e69bfc"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00387b03f2f91c96/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410b-2edc-73e0-ab92-d2a7f1e69bfc"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:15:47.003404+00:00",
+  "ended_at": "2026-05-19T16:15:50.965032+00:00",
+  "elapsed_ms": 3961.56,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 8128,
+    "bytes_utf8": 8128,
+    "lines": 244,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:15:51.967582+00:00",
+  "ended_at": "2026-05-19T16:15:55.590285+00:00",
+  "elapsed_ms": 3622.66,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 8128,
+    "bytes_utf8": 8128,
+    "lines": 244,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,244 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_c2_filtered_group_count_2d",
+    "template_name": "Filtered Two-Dimensional Group Count",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col",
+      "group_col_2",
+      "predicate_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Gender, group_col_2=Policy_Sales_Channel.",
+  "planned_template_id": "tpl_c2_filtered_group_count_2d",
+  "bindings": {
+    "group_col": "Gender",
+    "group_col_2": "Policy_Sales_Channel",
+    "predicate_col": "Driving_License",
+    "predicate_op": "=",
+    "predicate_value": "0",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 51539.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,244 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_c2_filtered_group_count_2d",
+    "template_name": "Filtered Two-Dimensional Group Count",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col",
+      "group_col_2",
+      "predicate_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=Gender, group_col_2=Policy_Sales_Channel.",
+  "planned_template_id": "tpl_c2_filtered_group_count_2d",
+  "bindings": {
+    "group_col": "Gender",
+    "group_col_2": "Policy_Sales_Channel",
+    "predicate_col": "Driving_License",
+    "predicate_op": "=",
+    "predicate_value": "0",
+    "top_k": 11,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 51539.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4105-9c4e-77d3-a9d0-8748927f980b"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4105-9c4e-77d3-a9d0-8748927f980b"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4105-afaf-7ef3-acfd-048370a78cbc"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00a142c01d928022/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4105-afaf-7ef3-acfd-048370a78cbc"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:32:10.608188+00:00",
+  "ended_at": "2026-05-19T16:32:14.119351+00:00",
+  "elapsed_ms": 3511.12,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7878,
+    "bytes_utf8": 7878,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:32:15.122733+00:00",
+  "ended_at": "2026-05-19T16:32:18.471078+00:00",
+  "elapsed_ms": 3348.26,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7878,
+    "bytes_utf8": 7878,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Gender, measure_col=Region_Code.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "Gender",
+    "measure_col": "Region_Code",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 24.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,240 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Gender, measure_col=Region_Code.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "Gender",
+    "measure_col": "Region_Code",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 24.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4114-9ef6-7a93-a38f-e4b60da42af1"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4114-9ef6-7a93-a38f-e4b60da42af1"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4114-b091-7f62-a2cd-6896546ae913"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_00ff11295d175ab8/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4114-b091-7f62-a2cd-6896546ae913"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:27:14.214166+00:00",
+  "ended_at": "2026-05-19T16:27:17.377897+00:00",
+  "elapsed_ms": 3163.69,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7752,
+    "bytes_utf8": 7752,
+    "lines": 238,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:27:18.381090+00:00",
+  "ended_at": "2026-05-19T16:27:21.551405+00:00",
+  "elapsed_ms": 3170.25,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7752,
+    "bytes_utf8": 7752,
+    "lines": 238,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,238 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Policy_Sales_Channel.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "Policy_Sales_Channel",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 152.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,238 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=Policy_Sales_Channel.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "Policy_Sales_Channel",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 152.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4110-1917-70f1-8201-1e30fa4f2a79"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4110-1917-70f1-8201-1e30fa4f2a79"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4110-2971-7ba1-b7f8-c5b43b771885"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_03a3abdf1faf674f/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4110-2971-7ba1-b7f8-c5b43b771885"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:24:33.507636+00:00",
+  "ended_at": "2026-05-19T16:24:36.986074+00:00",
+  "elapsed_ms": 3478.39,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 8064,
+    "bytes_utf8": 8064,
+    "lines": 243,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:24:37.988642+00:00",
+  "ended_at": "2026-05-19T16:24:42.554915+00:00",
+  "elapsed_ms": 4566.2,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 8064,
+    "bytes_utf8": 8064,
+    "lines": 243,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,243 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Policy_Sales_Channel, condition_col=Driving_License.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "Policy_Sales_Channel",
+    "condition_col": "Driving_License",
+    "condition_value": "1",
+    "positive_value": "1",
+    "negative_value": "0",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 49.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,243 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m11
+- dataset_name: Health Insurance Cross Sell Prediction
+- table_name: m11
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Previously_Insured`.
+- task_type: classification
+- target_column: Previously_Insured
+- main_row_count: 381109
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- Gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Gender.
+- Age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Age.
+- Driving_License: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Driving License.
+- Region_Code: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Region Code.
+- Previously_Insured: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Previously Insured.
+- Vehicle_Age: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Age.
+- Vehicle_Damage: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Vehicle Damage.
+- Annual_Premium: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Annual Premium.
+- Policy_Sales_Channel: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for Policy Sales Channel.
+- Vintage: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for Vintage.
+- Response: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for Response.
+- useful_field_combinations: [['Gender', 'Age', 'Previously_Insured'], ['Gender', 'Gender', 'Previously_Insured']]
+- fields_requiring_caution: ['Previously_Insured', 'Annual_Premium', 'Vintage']
+- source_url: https://www.kaggle.com/datasets/anmolkumar/health-insurance-cross-sell-prediction
+
+SQLite schema snapshot:
+{
+  "table_name": "m11",
+  "quoted_table_name": "\"m11\"",
+  "row_count": 381109,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Driving_License",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Region_Code",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Previously_Insured",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vehicle_Damage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Annual_Premium",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Policy_Sales_Channel",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Vintage",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "1",
+      "Gender": "Male",
+      "Age": "44",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "40454.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "217",
+      "Response": "1"
+    },
+    {
+      "id": "2",
+      "Gender": "Male",
+      "Age": "76",
+      "Driving_License": "1",
+      "Region_Code": "3.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "1-2 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "33536.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "183",
+      "Response": "0"
+    },
+    {
+      "id": "3",
+      "Gender": "Male",
+      "Age": "47",
+      "Driving_License": "1",
+      "Region_Code": "28.0",
+      "Previously_Insured": "0",
+      "Vehicle_Age": "> 2 Years",
+      "Vehicle_Damage": "Yes",
+      "Annual_Premium": "38294.0",
+      "Policy_Sales_Channel": "26.0",
+      "Vintage": "27",
+      "Response": "1"
+    },
+    {
+      "id": "4",
+      "Gender": "Male",
+      "Age": "21",
+      "Driving_License": "1",
+      "Region_Code": "11.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "28619.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "203",
+      "Response": "0"
+    },
+    {
+      "id": "5",
+      "Gender": "Female",
+      "Age": "29",
+      "Driving_License": "1",
+      "Region_Code": "41.0",
+      "Previously_Insured": "1",
+      "Vehicle_Age": "< 1 Year",
+      "Vehicle_Damage": "No",
+      "Annual_Premium": "27496.0",
+      "Policy_Sales_Channel": "152.0",
+      "Vintage": "39",
+      "Response": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m11",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=Policy_Sales_Channel, condition_col=Driving_License.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "Policy_Sales_Channel",
+    "condition_col": "Driving_License",
+    "condition_value": "1",
+    "positive_value": "1",
+    "negative_value": "0",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 49.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410d-a588-78a2-8e2d-0c7438a2ef27"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410d-a588-78a2-8e2d-0c7438a2ef27"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410d-b6c9-7282-9656-15a3b5c57b5f"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_f/m11/artifacts/v2q_m11_07a9acc2da849254/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e410d-b6c9-7282-9656-15a3b5c57b5f"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}