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Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,242 @@
+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: m7
+- dataset_name: Stroke Prediction Dataset
+- table_name: m7
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Residence_type`.
+- task_type: classification
+- target_column: Residence_type
+- main_row_count: 5110
+- 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. tags=['condition_candidate', 'measure'] desc=Numeric field for age.
+- hypertension: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for hypertension.
+- heart_disease: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for heart disease.
+- ever_married: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for ever married.
+- work_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for work type.
+- Residence_type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Residence type.
+- avg_glucose_level: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for avg glucose level.
+- bmi: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate', 'missingness_candidate'] desc=Numeric field for bmi.
+- smoking_status: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for smoking status.
+- stroke: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for stroke.
+- useful_field_combinations: [['gender', 'hypertension', 'Residence_type'], ['gender', 'age', 'Residence_type'], ['gender', 'gender', 'Residence_type']]
+- fields_requiring_caution: ['Residence_type', 'avg_glucose_level', 'bmi']
+- source_url: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
+
+SQLite schema snapshot:
+{
+  "table_name": "m7",
+  "quoted_table_name": "\"m7\"",
+  "row_count": 5110,
+  "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": "hypertension",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "heart_disease",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ever_married",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "work_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Residence_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bmi",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "smoking_status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "stroke",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "9046",
+      "gender": "Male",
+      "age": "67",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "228.69",
+      "bmi": "36.6",
+      "smoking_status": "formerly smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "51676",
+      "gender": "Female",
+      "age": "61",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "202.21",
+      "bmi": "",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "31112",
+      "gender": "Male",
+      "age": "80",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "105.92",
+      "bmi": "32.5",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "60182",
+      "gender": "Female",
+      "age": "49",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "171.23",
+      "bmi": "34.4",
+      "smoking_status": "smokes",
+      "stroke": "1"
+    },
+    {
+      "id": "1665",
+      "gender": "Female",
+      "age": "79",
+      "hypertension": "1",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "174.12",
+      "bmi": "24",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=gender, measure_col=id.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "gender",
+    "measure_col": "id",
+    "item_col": "age",
+    "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": 54682.0,
+    "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}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e2-d7a4-7621-8318-c6cc17ede9fe"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"gender\\\", \\\"age\\\",\\n       SUM(CAST(\\\"id\\\" AS NUMERIC)) AS total_measure,\\n       SUM(CAST(\\\"id\\\" AS NUMERIC)) * 100.0 / SUM(SUM(CAST(\\\"id\\\" AS NUMERIC))) OVER (PARTITION BY \\\"gender\\\") AS share_within_group\\nFROM \\\"m7\\\"\\nGROUP BY \\\"gender\\\", \\\"age\\\"\\nORDER BY share_within_group DESC\\nLIMIT 12;\",\"notes\":\"Uses the requested template with group_col=\\\"gender\\\", item_col=\\\"age\\\", and measure_col=\\\"id\\\". CAST is applied because \\\"id\\\" is stored as TEXT in SQLite schema.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14393,"cached_input_tokens":13696,"output_tokens":673,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7645, "bytes_utf8": 7645, "lines": 240, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 448, "bytes_utf8": 448, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14275, "cached_input_tokens": 4480, "output_tokens": 384, "reasoning_output_tokens": 268}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m7",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m7_0b9ffa08ee57cea6",
+    "api_calls": 0,
+    "input_tokens": 14275,
+    "cached_input_tokens": 4480,
+    "output_tokens": 384,
+    "total_tokens": 14659,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10990.38,
+    "sql_execution_elapsed_ms_total": 3.55,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "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": "2026-05-19T15:28:18.031734+00:00",
+  "ended_at": "2026-05-19T15:28:29.022141+00:00",
+  "elapsed_ms": 10990.38,
+  "prompt_metrics": {
+    "chars": 7645,
+    "bytes_utf8": 7645,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 805,
+    "bytes_utf8": 805,
+    "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": 448,
+      "bytes_utf8": 448,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14275,
+      "cached_input_tokens": 4480,
+      "output_tokens": 384,
+      "reasoning_output_tokens": 268
+    }
+  },
+  "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_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/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: m7
+- dataset_name: Stroke Prediction Dataset
+- table_name: m7
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Residence_type`.
+- task_type: classification
+- target_column: Residence_type
+- main_row_count: 5110
+- 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. tags=['condition_candidate', 'measure'] desc=Numeric field for age.
+- hypertension: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for hypertension.
+- heart_disease: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for heart disease.
+- ever_married: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for ever married.
+- work_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for work type.
+- Residence_type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Residence type.
+- avg_glucose_level: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for avg glucose level.
+- bmi: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate', 'missingness_candidate'] desc=Numeric field for bmi.
+- smoking_status: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for smoking status.
+- stroke: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for stroke.
+- useful_field_combinations: [['gender', 'hypertension', 'Residence_type'], ['gender', 'age', 'Residence_type'], ['gender', 'gender', 'Residence_type']]
+- fields_requiring_caution: ['Residence_type', 'avg_glucose_level', 'bmi']
+- source_url: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
+
+SQLite schema snapshot:
+{
+  "table_name": "m7",
+  "quoted_table_name": "\"m7\"",
+  "row_count": 5110,
+  "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": "hypertension",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "heart_disease",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ever_married",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "work_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Residence_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bmi",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "smoking_status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "stroke",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "9046",
+      "gender": "Male",
+      "age": "67",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "228.69",
+      "bmi": "36.6",
+      "smoking_status": "formerly smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "51676",
+      "gender": "Female",
+      "age": "61",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "202.21",
+      "bmi": "",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "31112",
+      "gender": "Male",
+      "age": "80",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "105.92",
+      "bmi": "32.5",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "60182",
+      "gender": "Female",
+      "age": "49",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "171.23",
+      "bmi": "34.4",
+      "smoking_status": "smokes",
+      "stroke": "1"
+    },
+    {
+      "id": "1665",
+      "gender": "Female",
+      "age": "79",
+      "hypertension": "1",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "174.12",
+      "bmi": "24",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=gender, measure_col=id.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "gender",
+    "measure_col": "id",
+    "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": 54682.0,
+    "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}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-2413-79f3-a8ab-b75bf2a70e7b"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"gender\\\", SUM(CAST(NULLIF(\\\"id\\\", '') AS NUMERIC)) AS total_measure\\nFROM \\\"m7\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the requested grouped numeric sum template with group_col=\\\"gender\\\" and measure_col=\\\"id\\\". Because \\\"id\\\" is stored as TEXT and behaves like an identifier, the query casts it to NUMERIC and treats empty strings as NULL for SQLite-safe summation.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14275,"cached_input_tokens":4480,"output_tokens":384,"reasoning_output_tokens":268}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0b9ffa08ee57cea6/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"gender\", SUM(CAST(NULLIF(\"id\", '') AS NUMERIC)) AS total_measure\nFROM \"m7\"\nGROUP BY \"gender\"\nORDER BY total_measure DESC;","notes":"Used the requested grouped numeric sum template with group_col=\"gender\" and measure_col=\"id\". Because \"id\" is stored as TEXT and behaves like an identifier, the query casts it to NUMERIC and treats empty strings as NULL for SQLite-safe summation."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7893, "bytes_utf8": 7893, "lines": 243, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 484, "bytes_utf8": 484, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14337, "cached_input_tokens": 12032, "output_tokens": 284, "reasoning_output_tokens": 148}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m7",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m7_32bd2177ebd1ed71",
+    "api_calls": 0,
+    "input_tokens": 14337,
+    "cached_input_tokens": 12032,
+    "output_tokens": 284,
+    "total_tokens": 14621,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8533.42,
+    "sql_execution_elapsed_ms_total": 1.69,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "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": "2026-05-19T16:01:30.103042+00:00",
+  "ended_at": "2026-05-19T16:01:38.636497+00:00",
+  "elapsed_ms": 8533.42,
+  "prompt_metrics": {
+    "chars": 7893,
+    "bytes_utf8": 7893,
+    "lines": 243,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 843,
+    "bytes_utf8": 843,
+    "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": 484,
+      "bytes_utf8": 484,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14337,
+      "cached_input_tokens": 12032,
+      "output_tokens": 284,
+      "reasoning_output_tokens": 148
+    }
+  },
+  "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_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f8-88f0-7851-8e9c-e6705eda7196"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"stroke\\\",\\n       AVG(CASE WHEN \\\"stroke\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m7\\\"\\nGROUP BY \\\"stroke\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Used the required template with group_col and condition_col both bound to \\\"stroke\\\" and condition_value bound to '1'. This produces a self-grouped proportion, so groups with \\\"stroke\\\"='1' will have rate 1.0 and groups with \\\"stroke\\\"='0' will have rate 0.0.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14337,"cached_input_tokens":12032,"output_tokens":284,"reasoning_output_tokens":148}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_32bd2177ebd1ed71/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"stroke\",\n       AVG(CASE WHEN \"stroke\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m7\"\nGROUP BY \"stroke\"\nORDER BY condition_rate DESC;","notes":"Used the required template with group_col and condition_col both bound to \"stroke\" and condition_value bound to '1'. This produces a self-grouped proportion, so groups with \"stroke\"='1' will have rate 1.0 and groups with \"stroke\"='0' will have rate 0.0."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=free_stream_velocity, measure_col=chord_length.
+Result preview: [{"free_stream_velocity": "39.6", "total_measure": 65.3288}, {"free_stream_velocity": "71.3", "total_measure": 63.7794}, {"free_stream_velocity": "31.7", "total_measure": 38.354}, {"free_stream_velocity": "55.5", "total_measure": 37.769800000000004}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_tpch_thresholded_group_ranking
+-- query_record_id: v2q_n2_011d8c042904e6cf
+-- problem_id: v2p_n2_16e15faae7e2596c
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "free_stream_velocity", SUM(CAST("chord_length" AS REAL)) AS total_measure
+FROM "n2"
+GROUP BY "free_stream_velocity"
+HAVING SUM(CAST("chord_length" AS REAL)) > 0.2286
+ORDER BY total_measure DESC
+LIMIT 12;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"free_stream_velocity\", SUM(CAST(\"chord_length\" AS REAL)) AS total_measure\nFROM \"n2\"\nGROUP BY \"free_stream_velocity\"\nHAVING SUM(CAST(\"chord_length\" AS REAL)) > 0.2286\nORDER BY total_measure DESC\nLIMIT 12;", "result": "{\"query\": \"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"free_stream_velocity\\\", SUM(CAST(\\\"chord_length\\\" AS REAL)) AS total_measure\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nHAVING SUM(CAST(\\\"chord_length\\\" AS REAL)) > 0.2286\\nORDER BY total_measure DESC\\nLIMIT 12;\", \"columns\": [\"free_stream_velocity\", \"total_measure\"], \"rows\": [{\"free_stream_velocity\": \"39.6\", \"total_measure\": 65.3288}, {\"free_stream_velocity\": \"71.3\", \"total_measure\": 63.7794}, {\"free_stream_velocity\": \"31.7\", \"total_measure\": 38.354}, {\"free_stream_velocity\": \"55.5\", \"total_measure\": 37.769800000000004}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.33}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T16:03:12.471404+00:00",
+  "ended_at": "2026-05-19T16:03:21.538940+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_011d8c042904e6cf",
+    "problem_id": "v2p_n2_16e15faae7e2596c",
+    "dataset_id": "n2",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=free_stream_velocity, measure_col=chord_length.",
+    "bindings": {
+      "group_col": "free_stream_velocity",
+      "measure_col": "chord_length",
+      "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": 0.2286,
+      "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
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=132"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/sql/v2q_n2_011d8c042904e6cf.sql",
+  "usage_summary": {
+    "dataset_id": "n2",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n2_011d8c042904e6cf",
+    "api_calls": 0,
+    "input_tokens": 13738,
+    "cached_input_tokens": 12032,
+    "output_tokens": 336,
+    "total_tokens": 14074,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 9062.31,
+    "sql_execution_elapsed_ms_total": 1.33,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:03:21.535696+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9062.31, "started_at": "2026-05-19T16:03:12.472369+00:00", "ended_at": "2026-05-19T16:03:21.534707+00:00", "prompt_metrics": {"chars": 5791, "bytes_utf8": 5791, "lines": 168, "estimated_tokens": null}, "response_metrics": {"chars": 507, "bytes_utf8": 507, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13738, "cached_input_tokens": 12032, "output_tokens": 336, "reasoning_output_tokens": 203}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"free_stream_velocity\\\", SUM(CAST(\\\"chord_length\\\" AS REAL)) AS total_measure\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nHAVING SUM(CAST(\\\"chord_length\\\" AS REAL)) > 0.2286\\nORDER BY total_measure DESC\\nLIMIT 12;\",\"notes\":\"Applied the planned Thresholded Group Ranking template with group_col=\\\"free_stream_velocity\\\" and measure_col=\\\"chord_length\\\". Cast \\\"chord_length\\\" to REAL because the schema stores numeric fields as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n2",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n2_011d8c042904e6cf",
+  "api_calls": 0,
+  "input_tokens": 13738,
+  "cached_input_tokens": 12032,
+  "output_tokens": 336,
+  "total_tokens": 14074,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 9062.31,
+  "sql_execution_elapsed_ms_total": 1.33,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_011d8c042904e6cf/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_029703ccd6188687/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"frequency": "2000", "support": 105, "avg_response": 6.601904761904763}, {"frequency": "2500", "support": 104, "avg_response": 6.536538461538461}, {"frequency": "1600", "support": 103, "avg_response": 6.683495145631068}, {"frequency": "3150", "support": 103, "avg_response": 6.534951456310679}, {"frequency": "4000", "support": 102, "avg_response": 6.533333333333333}]}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_029703ccd6188687/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_n2_029703ccd6188687
+-- problem_id: v2p_n2_da02e2bc46b9da6d
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "frequency",
+    COUNT(*) AS support,
+    AVG("angle_of_attack") AS avg_response
+FROM "n2"
+GROUP BY "frequency"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_029703ccd6188687/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_c\n-- sql_source_dataset_id: n2\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_n2_029703ccd6188687\n-- problem_id: v2p_n2_da02e2bc46b9da6d\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"frequency\",\n    COUNT(*) AS support,\n    AVG(\"angle_of_attack\") AS avg_response\nFROM \"n2\"\nGROUP BY \"frequency\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_c\\n-- sql_source_dataset_id: n2\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_n2_029703ccd6188687\\n-- problem_id: v2p_n2_da02e2bc46b9da6d\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"frequency\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"angle_of_attack\\\") AS avg_response\\nFROM \\\"n2\\\"\\nGROUP BY \\\"frequency\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"frequency\", \"support\", \"avg_response\"], \"rows\": [{\"frequency\": \"2000\", \"support\": 105, \"avg_response\": 6.601904761904763}, {\"frequency\": \"2500\", \"support\": 104, \"avg_response\": 6.536538461538461}, {\"frequency\": \"1600\", \"support\": 103, \"avg_response\": 6.683495145631068}, {\"frequency\": \"3150\", \"support\": 103, \"avg_response\": 6.534951456310679}, {\"frequency\": \"4000\", \"support\": 102, \"avg_response\": 6.533333333333333}, {\"frequency\": \"1250\", \"support\": 100, \"avg_response\": 6.794}, {\"frequency\": \"1000\", \"support\": 99, \"avg_response\": 6.862626262626263}, {\"frequency\": \"800\", \"support\": 97, \"avg_response\": 6.960824742268041}, {\"frequency\": \"5000\", \"support\": 95, \"avg_response\": 6.023157894736842}, {\"frequency\": \"6300\", \"support\": 89, \"avg_response\": 5.55505617977528}, {\"frequency\": \"630\", \"support\": 88, \"avg_response\": 7.5227272727272725}, {\"frequency\": \"500\", \"support\": 78, \"avg_response\": 8.11794871794872}, {\"frequency\": \"400\", \"support\": 69, \"avg_response\": 8.843478260869565}, {\"frequency\": \"315\", \"support\": 56, \"avg_response\": 9.619642857142859}, {\"frequency\": \"8000\", \"support\": 52, \"avg_response\": 3.494230769230769}, {\"frequency\": \"250\", \"support\": 42, \"avg_response\": 11.34047619047619}, {\"frequency\": \"10000\", \"support\": 42, \"avg_response\": 2.4333333333333336}, {\"frequency\": \"200\", \"support\": 35, \"avg_response\": 12.182857142857141}, {\"frequency\": \"12500\", \"support\": 25, \"avg_response\": 1.208}, {\"frequency\": \"16000\", \"support\": 13, \"avg_response\": 1.3692307692307693}, {\"frequency\": \"20000\", \"support\": 6, \"avg_response\": 2.2666666666666666}], \"row_count_returned\": 21, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.89}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_029703ccd6188687/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T16:06:12.669812+00:00",
+  "ended_at": "2026-05-19T16:06:12.671406+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_029703ccd6188687",
+    "problem_id": "v2p_n2_da02e2bc46b9da6d",
+    "dataset_id": "n2",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=angle_of_attack, key_col=frequency.",
+    "bindings": {
+      "key_col": "frequency",
+      "measure_col": "angle_of_attack",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/sql/v2q_n2_029703ccd6188687.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_029703ccd6188687/usage_summary.json

@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_02d8ee48cbbcf5fc/run_manifest.json

@@ -0,0 +1,72 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T15:59:21.566513+00:00",
+  "ended_at": "2026-05-19T15:59:30.206190+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_02d8ee48cbbcf5fc",
+    "problem_id": "v2p_n2_daa651abe5aacd3e",
+    "dataset_id": "n2",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=free_stream_velocity, condition_col=sound_pressure_level.",
+    "bindings": {
+      "group_col": "free_stream_velocity",
+      "condition_col": "sound_pressure_level",
+      "condition_value": "127.315",
+      "positive_value": "126.540",
+      "negative_value": "127.315",
+      "top_k": 17,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 0.2286,
+      "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
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "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;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=7",
+      "sql_variant_index=2/2",
+      "binding_index=102"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 7,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_02d8ee48cbbcf5fc/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T15:59:24.494472+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 2926.17, "started_at": "2026-05-19T15:59:21.567563+00:00", "ended_at": "2026-05-19T15:59:24.493755+00:00", "prompt_metrics": {"chars": 5905, "bytes_utf8": 5905, "lines": 171, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f6-92f5-77a1-8bdb-dd6471011ac0\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T15:59:30.206014+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 4709.4, "started_at": "2026-05-19T15:59:25.495202+00:00", "ended_at": "2026-05-19T15:59:30.204640+00:00", "prompt_metrics": {"chars": 5905, "bytes_utf8": 5905, "lines": 171, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f6-a251-7bc2-a3a9-c180a35be804\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=free_stream_velocity, measure_col=free_stream_velocity.
+Result preview: [{"free_stream_velocity": "55.5", "angle_of_attack": "0", "total_measure": 4606.5, "share_within_group": 29.96389891696751}, {"free_stream_velocity": "31.7", "angle_of_attack": "0", "total_measure": 2662.7999999999997, "share_within_group": 29.893238434163706}, {"free_stream_velocity": "39.6", "angle_of_attack": "0", "total_measure": 3326.4, "share_within_group": 17.5}, {"free_stream_velocity": "71.3", "angle_of_attack": "0", "total_measure": 5561.4, "share_within_group": 16.774193548387096}, {"free_stream_velocity": "71.3", "angle_of_attack": "4", "total_measure": 2495.5, "share_within_group": 7.526881720430108}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/generated_sql.sql

@@ -0,0 +1,22 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_n2_04386f02a7447eed
+-- problem_id: v2p_n2_5b1757c796c11dbf
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "free_stream_velocity",
+  "angle_of_attack",
+  SUM(CAST("free_stream_velocity" AS REAL)) AS total_measure,
+  SUM(CAST("free_stream_velocity" AS REAL)) * 100.0
+    / SUM(SUM(CAST("free_stream_velocity" AS REAL))) OVER (PARTITION BY "free_stream_velocity") AS share_within_group
+FROM "n2"
+GROUP BY "free_stream_velocity", "angle_of_attack"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"free_stream_velocity\",\n  \"angle_of_attack\",\n  SUM(CAST(\"free_stream_velocity\" AS REAL)) AS total_measure,\n  SUM(CAST(\"free_stream_velocity\" AS REAL)) * 100.0\n    / SUM(SUM(CAST(\"free_stream_velocity\" AS REAL))) OVER (PARTITION BY \"free_stream_velocity\") AS share_within_group\nFROM \"n2\"\nGROUP BY \"free_stream_velocity\", \"angle_of_attack\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"free_stream_velocity\\\",\\n  \\\"angle_of_attack\\\",\\n  SUM(CAST(\\\"free_stream_velocity\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"free_stream_velocity\\\" AS REAL)) * 100.0\\n    / SUM(SUM(CAST(\\\"free_stream_velocity\\\" AS REAL))) OVER (PARTITION BY \\\"free_stream_velocity\\\") AS share_within_group\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\", \\\"angle_of_attack\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"free_stream_velocity\", \"angle_of_attack\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"0\", \"total_measure\": 4606.5, \"share_within_group\": 29.96389891696751}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"0\", \"total_measure\": 2662.7999999999997, \"share_within_group\": 29.893238434163706}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"0\", \"total_measure\": 3326.4, \"share_within_group\": 17.5}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"0\", \"total_measure\": 5561.4, \"share_within_group\": 16.774193548387096}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"4\", \"total_measure\": 2495.5, \"share_within_group\": 7.526881720430108}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"4\", \"total_measure\": 1148.4, \"share_within_group\": 6.041666666666668}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"12.3\", \"total_measure\": 888.0, \"share_within_group\": 5.776173285198556}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"15.4\", \"total_measure\": 888.0, \"share_within_group\": 5.776173285198556}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"17.4\", \"total_measure\": 888.0, \"share_within_group\": 5.776173285198556}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"7.3\", \"total_measure\": 888.0, \"share_within_group\": 5.776173285198556}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"9.9\", \"total_measure\": 888.0, \"share_within_group\": 5.776173285198556}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"12.3\", \"total_measure\": 507.2, \"share_within_group\": 5.693950177935943}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"15.4\", \"total_measure\": 507.2, \"share_within_group\": 5.693950177935943}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"7.3\", \"total_measure\": 507.2, \"share_within_group\": 5.693950177935943}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"9.5\", \"total_measure\": 507.2, \"share_within_group\": 5.693950177935943}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"9.9\", \"total_measure\": 507.2, \"share_within_group\": 5.693950177935943}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"3\", \"total_measure\": 832.5, \"share_within_group\": 5.415162454873646}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"17.4\", \"total_measure\": 475.5, \"share_within_group\": 5.338078291814948}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"4\", \"total_measure\": 475.5, \"share_within_group\": 5.338078291814948}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"4\", \"total_measure\": 777.0, \"share_within_group\": 5.054151624548736}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"5.4\", \"total_measure\": 777.0, \"share_within_group\": 5.054151624548736}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"2\", \"total_measure\": 443.8, \"share_within_group\": 4.982206405693951}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"3\", \"total_measure\": 443.8, \"share_within_group\": 4.982206405693951}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"5.4\", \"total_measure\": 443.8, \"share_within_group\": 4.982206405693951}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"2\", \"total_measure\": 721.5, \"share_within_group\": 4.693140794223827}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"3.3\", \"total_measure\": 721.5, \"share_within_group\": 4.693140794223827}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"9.5\", \"total_measure\": 721.5, \"share_within_group\": 4.693140794223827}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"4.8\", \"total_measure\": 412.09999999999997, \"share_within_group\": 4.626334519572954}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"4.8\", \"total_measure\": 666.0, \"share_within_group\": 4.332129963898917}, {\"free_stream_velocity\": \"55.5\", \"angle_of_attack\": \"8.4\", \"total_measure\": 666.0, \"share_within_group\": 4.332129963898917}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"3.3\", \"total_measure\": 380.4, \"share_within_group\": 4.270462633451958}, {\"free_stream_velocity\": \"31.7\", \"angle_of_attack\": \"8.4\", \"total_measure\": 348.7, \"share_within_group\": 3.914590747330961}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"12.7\", \"total_measure\": 1212.1, \"share_within_group\": 3.655913978494623}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"15.4\", \"total_measure\": 1212.1, \"share_within_group\": 3.655913978494623}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"17.4\", \"total_measure\": 1212.1, \"share_within_group\": 3.655913978494623}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"1.5\", \"total_measure\": 673.2, \"share_within_group\": 3.5416666666666665}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"12.7\", \"total_measure\": 673.2, \"share_within_group\": 3.5416666666666665}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"7.2\", \"total_measure\": 673.2, \"share_within_group\": 3.5416666666666665}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"12.3\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"12.6\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"7.2\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"7.3\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"8.9\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"71.3\", \"angle_of_attack\": \"9.9\", \"total_measure\": 1140.8, \"share_within_group\": 3.4408602150537635}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"12.3\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"12.6\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"15.4\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"15.6\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"7.3\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}, {\"free_stream_velocity\": \"39.6\", \"angle_of_attack\": \"8.9\", \"total_measure\": 633.6, \"share_within_group\": 3.3333333333333335}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 1.56}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/run_manifest.json

@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T15:34:17.831441+00:00",
+  "ended_at": "2026-05-19T15:34:33.723658+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_04386f02a7447eed",
+    "problem_id": "v2p_n2_5b1757c796c11dbf",
+    "dataset_id": "n2",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=free_stream_velocity, measure_col=free_stream_velocity.",
+    "bindings": {
+      "group_col": "free_stream_velocity",
+      "measure_col": "free_stream_velocity",
+      "item_col": "angle_of_attack",
+      "top_k": 13,
+      "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": 71.3,
+      "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
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=1/2",
+      "binding_index=28"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/sql/v2q_n2_04386f02a7447eed.sql",
+  "usage_summary": {
+    "dataset_id": "n2",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n2_04386f02a7447eed",
+    "api_calls": 0,
+    "input_tokens": 13812,
+    "cached_input_tokens": 12032,
+    "output_tokens": 701,
+    "total_tokens": 14513,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15884.85,
+    "sql_execution_elapsed_ms_total": 1.56,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:34:33.719264+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 15884.85, "started_at": "2026-05-19T15:34:17.832969+00:00", "ended_at": "2026-05-19T15:34:33.717859+00:00", "prompt_metrics": {"chars": 6055, "bytes_utf8": 6055, "lines": 170, "estimated_tokens": null}, "response_metrics": {"chars": 724, "bytes_utf8": 724, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13812, "cached_input_tokens": 12032, "output_tokens": 701, "reasoning_output_tokens": 516}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"free_stream_velocity\\\",\\n  \\\"angle_of_attack\\\",\\n  SUM(CAST(\\\"free_stream_velocity\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"free_stream_velocity\\\" AS REAL)) * 100.0\\n    / SUM(SUM(CAST(\\\"free_stream_velocity\\\" AS REAL))) OVER (PARTITION BY \\\"free_stream_velocity\\\") AS share_within_group\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\", \\\"angle_of_attack\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the provided Within-Group Share of Total template with group_col=\\\"free_stream_velocity\\\", item_col=\\\"angle_of_attack\\\", and measure_col=\\\"free_stream_velocity\\\". CAST to REAL is applied because the schema stores numeric fields as TEXT in SQLite.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n2",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n2_04386f02a7447eed",
+  "api_calls": 0,
+  "input_tokens": 13812,
+  "cached_input_tokens": 12032,
+  "output_tokens": 701,
+  "total_tokens": 14513,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 15884.85,
+  "sql_execution_elapsed_ms_total": 1.56,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_04386f02a7447eed/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=free_stream_velocity, measure_col=chord_length.
+Result preview: [{"free_stream_velocity": "39.6", "total_measure": 65.3288}, {"free_stream_velocity": "71.3", "total_measure": 63.7794}, {"free_stream_velocity": "31.7", "total_measure": 38.354}, {"free_stream_velocity": "55.5", "total_measure": 37.769800000000004}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_n2_0ab63b1ff4fc51a2
+-- problem_id: v2p_n2_885b6227f2c50511
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "free_stream_velocity",
+  SUM(CAST("chord_length" AS REAL)) AS "total_measure"
+FROM "n2"
+GROUP BY "free_stream_velocity"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_h2o_group_sum\nSELECT\n  \"free_stream_velocity\",\n  SUM(CAST(\"chord_length\" AS REAL)) AS \"total_measure\"\nFROM \"n2\"\nGROUP BY \"free_stream_velocity\"\nORDER BY \"total_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_h2o_group_sum\\nSELECT\\n  \\\"free_stream_velocity\\\",\\n  SUM(CAST(\\\"chord_length\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nORDER BY \\\"total_measure\\\" DESC;\", \"columns\": [\"free_stream_velocity\", \"total_measure\"], \"rows\": [{\"free_stream_velocity\": \"39.6\", \"total_measure\": 65.3288}, {\"free_stream_velocity\": \"71.3\", \"total_measure\": 63.7794}, {\"free_stream_velocity\": \"31.7\", \"total_measure\": 38.354}, {\"free_stream_velocity\": \"55.5\", \"total_measure\": 37.769800000000004}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.04}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T15:28:58.877954+00:00",
+  "ended_at": "2026-05-19T15:29:09.681679+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_0ab63b1ff4fc51a2",
+    "problem_id": "v2p_n2_885b6227f2c50511",
+    "dataset_id": "n2",
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "internal_profile_stability",
+    "intended_facet_id": "subgroup_conditional_contrast",
+    "variant_semantic_role": "collapsed_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=free_stream_velocity, measure_col=chord_length.",
+    "bindings": {
+      "group_col": "free_stream_velocity",
+      "measure_col": "chord_length",
+      "top_k": 12,
+      "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": 0.2286,
+      "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
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift,subgroup_rank_order,subgroup_conditional_contrast",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=2"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 1,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/sql/v2q_n2_0ab63b1ff4fc51a2.sql",
+  "usage_summary": {
+    "dataset_id": "n2",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n2_0ab63b1ff4fc51a2",
+    "api_calls": 0,
+    "input_tokens": 13693,
+    "cached_input_tokens": 12288,
+    "output_tokens": 287,
+    "total_tokens": 13980,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10798.93,
+    "sql_execution_elapsed_ms_total": 1.04,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:29:09.679394+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10798.93, "started_at": "2026-05-19T15:28:58.879584+00:00", "ended_at": "2026-05-19T15:29:09.678547+00:00", "prompt_metrics": {"chars": 5624, "bytes_utf8": 5624, "lines": 168, "estimated_tokens": null}, "response_metrics": {"chars": 437, "bytes_utf8": 437, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13693, "cached_input_tokens": 12288, "output_tokens": 287, "reasoning_output_tokens": 173}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n  \\\"free_stream_velocity\\\",\\n  SUM(CAST(\\\"chord_length\\\" AS REAL)) AS \\\"total_measure\\\"\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the provided Grouped Numeric Sum template with group_col=\\\"free_stream_velocity\\\" and measure_col=\\\"chord_length\\\". CAST to REAL is used because the schema stores numeric fields as TEXT in SQLite.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n2",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n2_0ab63b1ff4fc51a2",
+  "api_calls": 0,
+  "input_tokens": 13693,
+  "cached_input_tokens": 12288,
+  "output_tokens": 287,
+  "total_tokens": 13980,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10798.93,
+  "sql_execution_elapsed_ms_total": 1.04,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0ab63b1ff4fc51a2/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=free_stream_velocity.
+Result preview: [{"free_stream_velocity": "39.6", "row_count": 480}, {"free_stream_velocity": "71.3", "row_count": 465}, {"free_stream_velocity": "31.7", "row_count": 281}, {"free_stream_velocity": "55.5", "row_count": 277}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: subgroup_structure
+-- canonical_subitem_id: subgroup_size_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_clickbench_group_count
+-- query_record_id: v2q_n2_0b6b8c84c9f66e3a
+-- problem_id: v2p_n2_8aa5cc48452fdde3
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "free_stream_velocity", COUNT(*) AS "row_count"
+FROM "n2"
+GROUP BY "free_stream_velocity"
+ORDER BY "row_count" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_clickbench_group_count\nSELECT \"free_stream_velocity\", COUNT(*) AS \"row_count\"\nFROM \"n2\"\nGROUP BY \"free_stream_velocity\"\nORDER BY \"row_count\" DESC;", "result": "{\"query\": \"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"free_stream_velocity\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nORDER BY \\\"row_count\\\" DESC;\", \"columns\": [\"free_stream_velocity\", \"row_count\"], \"rows\": [{\"free_stream_velocity\": \"39.6\", \"row_count\": 480}, {\"free_stream_velocity\": \"71.3\", \"row_count\": 465}, {\"free_stream_velocity\": \"31.7\", \"row_count\": 281}, {\"free_stream_velocity\": \"55.5\", \"row_count\": 277}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 0.65}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "n2",
+  "started_at": "2026-05-19T15:31:21.933892+00:00",
+  "ended_at": "2026-05-19T15:31:32.320059+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n2_0b6b8c84c9f66e3a",
+    "problem_id": "v2p_n2_8aa5cc48452fdde3",
+    "dataset_id": "n2",
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "family_id": "subgroup_structure",
+    "canonical_subitem_id": "subgroup_size_stability",
+    "intended_facet_id": "subgroup_distribution_shift",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=free_stream_velocity.",
+    "bindings": {
+      "group_col": "free_stream_velocity",
+      "top_k": 14,
+      "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": 0.015576,
+      "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
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "notes": [
+      "default_facets=subgroup_distribution_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/1",
+      "binding_index=14"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 2,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/sql/v2q_n2_0b6b8c84c9f66e3a.sql",
+  "usage_summary": {
+    "dataset_id": "n2",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n2_0b6b8c84c9f66e3a",
+    "api_calls": 0,
+    "input_tokens": 13664,
+    "cached_input_tokens": 12672,
+    "output_tokens": 405,
+    "total_tokens": 14069,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10382.82,
+    "sql_execution_elapsed_ms_total": 0.65,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:31:32.318198+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10382.82, "started_at": "2026-05-19T15:31:21.934553+00:00", "ended_at": "2026-05-19T15:31:32.317398+00:00", "prompt_metrics": {"chars": 5528, "bytes_utf8": 5528, "lines": 166, "estimated_tokens": null}, "response_metrics": {"chars": 310, "bytes_utf8": 310, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13664, "cached_input_tokens": 12672, "output_tokens": 405, "reasoning_output_tokens": 329}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"free_stream_velocity\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"n2\\\"\\nGROUP BY \\\"free_stream_velocity\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Counts rows for each free_stream_velocity subgroup to inspect count distribution and subgroup-size stability.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n2",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n2_0b6b8c84c9f66e3a",
+  "api_calls": 0,
+  "input_tokens": 13664,
+  "cached_input_tokens": 12672,
+  "output_tokens": 405,
+  "total_tokens": 14069,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10382.82,
+  "sql_execution_elapsed_ms_total": 0.65,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0b6b8c84c9f66e3a/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0da34a3af662e8b6/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=free_stream_velocity, measure_col=free_stream_velocity.
+Result preview: [{"free_stream_velocity": "71.3", "avg_measure": 71.3}, {"free_stream_velocity": "55.5", "avg_measure": 55.5}, {"free_stream_velocity": "39.6", "avg_measure": 39.6}, {"free_stream_velocity": "31.7", "avg_measure": 31.699999999999996}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0da34a3af662e8b6/generated_sql.sql

@@ -0,0 +1,17 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: n2
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n2_0da34a3af662e8b6
+-- problem_id: v2p_n2_6818321f6fd38114
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT "free_stream_velocity",
+       AVG(CAST("free_stream_velocity" AS REAL)) OVER (PARTITION BY "free_stream_velocity") AS avg_measure
+FROM "n2"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/n2/artifacts/v2q_n2_0da34a3af662e8b6/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"free_stream_velocity\",\n       AVG(CAST(\"free_stream_velocity\" AS REAL)) OVER (PARTITION BY \"free_stream_velocity\") AS avg_measure\nFROM \"n2\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"free_stream_velocity\\\",\\n       AVG(CAST(\\\"free_stream_velocity\\\" AS REAL)) OVER (PARTITION BY \\\"free_stream_velocity\\\") AS avg_measure\\nFROM \\\"n2\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"free_stream_velocity\", \"avg_measure\"], \"rows\": [{\"free_stream_velocity\": \"71.3\", \"avg_measure\": 71.3}, {\"free_stream_velocity\": \"55.5\", \"avg_measure\": 55.5}, {\"free_stream_velocity\": \"39.6\", \"avg_measure\": 39.6}, {\"free_stream_velocity\": \"31.7\", \"avg_measure\": 31.699999999999996}], \"row_count_returned\": 4, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.45}"}