Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 8211, "bytes_utf8": 8211, "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": 729, "bytes_utf8": 729, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14418, "cached_input_tokens": 13696, "output_tokens": 602, "reasoning_output_tokens": 406}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/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_0088d25a69094734",
+    "api_calls": 0,
+    "input_tokens": 14418,
+    "cached_input_tokens": 13696,
+    "output_tokens": 602,
+    "total_tokens": 15020,
+    "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": 16686.75,
+    "sql_execution_elapsed_ms_total": 4.93,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/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_0088d25a69094734/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": "2026-05-19T15:47:26.303753+00:00",
+  "ended_at": "2026-05-19T15:47:42.990553+00:00",
+  "elapsed_ms": 16686.75,
+  "prompt_metrics": {
+    "chars": 8211,
+    "bytes_utf8": 8211,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1105,
+    "bytes_utf8": 1105,
+    "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": 729,
+      "bytes_utf8": 729,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14418,
+      "cached_input_tokens": 13696,
+      "output_tokens": 602,
+      "reasoning_output_tokens": 406
+    }
+  },
+  "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_0088d25a69094734/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_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=ever_married, measure_col=bmi.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "ever_married",
+    "measure_col": "bmi",
+    "top_k": 10,
+    "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": 33.1,
+    "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": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40eb-a94e-7bc1-9a63-af4019d4bfdf"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"ever_married\\\", SUM(CAST(NULLIF(\\\"bmi\\\", '') AS REAL)) AS group_value\\n    FROM \\\"m7\\\"\\n    WHERE NULLIF(\\\"bmi\\\", '') IS NOT NULL\\n    GROUP BY \\\"ever_married\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"ever_married\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Uses the planned template with group_col=\\\"ever_married\\\" and measure_col=\\\"bmi\\\". Because \\\"bmi\\\" is stored as TEXT and may contain empty strings, the query casts non-empty values to REAL and excludes blanks before aggregation.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14418,"cached_input_tokens":13696,"output_tokens":602,"reasoning_output_tokens":406}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"ever_married\", SUM(CAST(NULLIF(\"bmi\", '') AS REAL)) AS group_value\n    FROM \"m7\"\n    WHERE NULLIF(\"bmi\", '') IS NOT NULL\n    GROUP BY \"ever_married\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"ever_married\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Uses the planned template with group_col=\"ever_married\" and measure_col=\"bmi\". Because \"bmi\" is stored as TEXT and may contain empty strings, the query casts non-empty values to REAL and excludes blanks before aggregation."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_018abcbfdff638e0/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7915, "bytes_utf8": 7915, "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": 405, "bytes_utf8": 405, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14344, "cached_input_tokens": 12032, "output_tokens": 422, "reasoning_output_tokens": 307}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_018abcbfdff638e0/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_018abcbfdff638e0",
+    "api_calls": 0,
+    "input_tokens": 14344,
+    "cached_input_tokens": 12032,
+    "output_tokens": 422,
+    "total_tokens": 14766,
+    "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": 10173.7,
+    "sql_execution_elapsed_ms_total": 1.89,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_018abcbfdff638e0/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_018abcbfdff638e0/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": "2026-05-19T16:00:08.153199+00:00",
+  "ended_at": "2026-05-19T16:00:18.326925+00:00",
+  "elapsed_ms": 10173.7,
+  "prompt_metrics": {
+    "chars": 7915,
+    "bytes_utf8": 7915,
+    "lines": 243,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 772,
+    "bytes_utf8": 772,
+    "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": 405,
+      "bytes_utf8": 405,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14344,
+      "cached_input_tokens": 12032,
+      "output_tokens": 422,
+      "reasoning_output_tokens": 307
+    }
+  },
+  "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_018abcbfdff638e0/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: 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_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": "m7",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role focused_target_view. Focus on group_col=ever_married, condition_col=ever_married.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "ever_married",
+    "condition_col": "ever_married",
+    "condition_value": "Yes",
+    "positive_value": "Yes",
+    "negative_value": "No",
+    "top_k": 14,
+    "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": 33.1,
+    "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_c/m7/artifacts/v2q_m7_018abcbfdff638e0/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f7-48e2-75b2-938b-fc189d0307aa"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"ever_married\\\",\\n       AVG(CASE WHEN \\\"ever_married\\\" = 'Yes' THEN 1 ELSE 0 END) AS \\\"condition_rate\\\"\\nFROM \\\"m7\\\"\\nGROUP BY \\\"ever_married\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Instantiated the Grouped Condition Rate template with \\\"group_col\\\" and \\\"condition_col\\\" both bound to \\\"ever_married\\\", using condition_value = 'Yes'.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14344,"cached_input_tokens":12032,"output_tokens":422,"reasoning_output_tokens":307}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_018abcbfdff638e0/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"ever_married\",\n       AVG(CASE WHEN \"ever_married\" = 'Yes' THEN 1 ELSE 0 END) AS \"condition_rate\"\nFROM \"m7\"\nGROUP BY \"ever_married\"\nORDER BY \"condition_rate\" DESC;","notes":"Instantiated the Grouped Condition Rate template with \"group_col\" and \"condition_col\" both bound to \"ever_married\", using condition_value = 'Yes'."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 8050, "bytes_utf8": 8050, "lines": 242, "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": 534, "bytes_utf8": 534, "lines": 1, "estimated_tokens": null}, "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_01d4023046378f99/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_01d4023046378f99",
+    "api_calls": 0,
+    "input_tokens": 14393,
+    "cached_input_tokens": 13696,
+    "output_tokens": 673,
+    "total_tokens": 15066,
+    "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": 13040.65,
+    "sql_execution_elapsed_ms_total": 4.66,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/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_01d4023046378f99/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": "2026-05-19T15:37:48.437397+00:00",
+  "ended_at": "2026-05-19T15:38:01.478078+00:00",
+  "elapsed_ms": 13040.65,
+  "prompt_metrics": {
+    "chars": 8050,
+    "bytes_utf8": 8050,
+    "lines": 242,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 919,
+    "bytes_utf8": 919,
+    "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": 534,
+      "bytes_utf8": 534,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14393,
+      "cached_input_tokens": 13696,
+      "output_tokens": 673,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "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_01d4023046378f99/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"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."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/conversation.jsonl

@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7646, "bytes_utf8": 7646, "lines": 238, "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": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 7646, "bytes_utf8": 7646, "lines": 238, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 295, "bytes_utf8": 295, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14289, "cached_input_tokens": 12032, "output_tokens": 173, "reasoning_output_tokens": 88}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/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": 2,
+  "usage_summary": {
+    "dataset_id": "m7",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m7_028af02a750d40c1",
+    "api_calls": 0,
+    "input_tokens": 14289,
+    "cached_input_tokens": 12032,
+    "output_tokens": 173,
+    "total_tokens": 14462,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 25673.99,
+    "sql_execution_elapsed_ms_total": 2.95,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/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_028af02a750d40c1/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": "2026-05-19T16:04:07.735809+00:00",
+  "ended_at": "2026-05-19T16:04:11.085416+00:00",
+  "elapsed_ms": 3349.58,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7646,
+    "bytes_utf8": 7646,
+    "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_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,45 @@
+{
+  "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": "2026-05-19T16:04:12.087163+00:00",
+  "ended_at": "2026-05-19T16:04:34.411620+00:00",
+  "elapsed_ms": 22324.41,
+  "prompt_metrics": {
+    "chars": 7646,
+    "bytes_utf8": 7646,
+    "lines": 238,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 643,
+    "bytes_utf8": 643,
+    "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": 295,
+      "bytes_utf8": 295,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14289,
+      "cached_input_tokens": 12032,
+      "output_tokens": 173,
+      "reasoning_output_tokens": 88
+    }
+  },
+  "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_c/m7/artifacts/v2q_m7_028af02a750d40c1/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: 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_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=gender.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "gender",
+    "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\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/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: 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_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=gender.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "gender",
+    "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\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-f0e2-78e1-8f2a-baf079931529"}
+{"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_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-f0e2-78e1-8f2a-baf079931529"}
+{"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_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-01c1-7383-a22a-6425721e8b34"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"gender\\\",\\n    COUNT(*) AS support\\nFROM \\\"m7\\\"\\nGROUP BY \\\"gender\\\"\\nORDER BY support ASC, \\\"gender\\\"\\nLIMIT 10;\",\"notes\":\"Uses the planned Low-Support Group Count template with group_col bound to gender and top_k=10.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14289,"cached_input_tokens":12032,"output_tokens":173,"reasoning_output_tokens":88}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/cli/sql_response_attempt_2.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"gender\",\n    COUNT(*) AS support\nFROM \"m7\"\nGROUP BY \"gender\"\nORDER BY support ASC, \"gender\"\nLIMIT 10;","notes":"Uses the planned Low-Support Group Count template with group_col bound to gender and top_k=10."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_035483ed5abfa740/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": "2026-05-19T15:57:42.028985+00:00",
+  "ended_at": "2026-05-19T15:57:45.318051+00:00",
+  "elapsed_ms": 3289.03,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7837,
+    "bytes_utf8": 7837,
+    "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_c/m7/artifacts/v2q_m7_035483ed5abfa740/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": "2026-05-19T15:57:46.320064+00:00",
+  "ended_at": "2026-05-19T15:57:49.583216+00:00",
+  "elapsed_ms": 3263.11,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7837,
+    "bytes_utf8": 7837,
+    "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_c/m7/artifacts/v2q_m7_035483ed5abfa740/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_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": "m7",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=ever_married, measure_col=bmi.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "ever_married",
+    "measure_col": "bmi",
+    "top_k": 16,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "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": 31.0,
+    "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_c/m7/artifacts/v2q_m7_035483ed5abfa740/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: 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_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": "m7",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=ever_married, measure_col=bmi.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "ever_married",
+    "measure_col": "bmi",
+    "top_k": 16,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "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": 31.0,
+    "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_c/m7/artifacts/v2q_m7_035483ed5abfa740/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-0e29-7362-8055-716be10ec868"}
+{"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_c/m7/artifacts/v2q_m7_035483ed5abfa740/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-0e29-7362-8055-716be10ec868"}
+{"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_c/m7/artifacts/v2q_m7_035483ed5abfa740/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-1ed3-7981-b588-ed294fbeeb96"}
+{"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_c/m7/artifacts/v2q_m7_035483ed5abfa740/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-1ed3-7981-b588-ed294fbeeb96"}
+{"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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/conversation.jsonl

@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7584, "bytes_utf8": 7584, "lines": 238, "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": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 7584, "bytes_utf8": 7584, "lines": 238, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 546, "bytes_utf8": 546, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14266, "cached_input_tokens": 13696, "output_tokens": 398, "reasoning_output_tokens": 244}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/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": 2,
+  "usage_summary": {
+    "dataset_id": "m7",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m7_03cbd3b43bfb5aca",
+    "api_calls": 0,
+    "input_tokens": 14266,
+    "cached_input_tokens": 13696,
+    "output_tokens": 398,
+    "total_tokens": 14664,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13029.05,
+    "sql_execution_elapsed_ms_total": 3.5,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/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_03cbd3b43bfb5aca/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": "2026-05-19T16:02:27.456735+00:00",
+  "ended_at": "2026-05-19T16:02:30.742329+00:00",
+  "elapsed_ms": 3285.56,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 7584,
+    "bytes_utf8": 7584,
+    "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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,45 @@
+{
+  "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": "2026-05-19T16:02:31.744129+00:00",
+  "ended_at": "2026-05-19T16:02:41.487653+00:00",
+  "elapsed_ms": 9743.49,
+  "prompt_metrics": {
+    "chars": 7584,
+    "bytes_utf8": 7584,
+    "lines": 238,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 902,
+    "bytes_utf8": 902,
+    "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": 546,
+      "bytes_utf8": 546,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14266,
+      "cached_input_tokens": 13696,
+      "output_tokens": 398,
+      "reasoning_output_tokens": 244
+    }
+  },
+  "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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/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: 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_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": "m7",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=age.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "age",
+    "top_k": 14,
+    "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": 61.0,
+    "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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/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: 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_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": "m7",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=age.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "age",
+    "top_k": 14,
+    "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": 61.0,
+    "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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-692b-7ae1-a440-7208c977cb99"}
+{"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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-692b-7ae1-a440-7208c977cb99"}
+{"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_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-79ce-75c3-813c-78cbe5333716"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN NULLIF(TRIM(\\\"age\\\"), '') IS NOT NULL AND CAST(\\\"age\\\" AS REAL) <= 61.0 THEN 1 WHEN NULLIF(TRIM(\\\"age\\\"), '') IS NOT NULL THEN 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"m7\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template with measure_col bound to \\\"age\\\" and measure_threshold fixed at 61.0. Because \\\"age\\\" is stored as TEXT in the schema snapshot, the query casts it to REAL and ignores blank values so the empirical CDF is computed on non-missing ages only.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14266,"cached_input_tokens":13696,"output_tokens":398,"reasoning_output_tokens":244}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/cli/sql_response_attempt_2.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN NULLIF(TRIM(\"age\"), '') IS NOT NULL AND CAST(\"age\" AS REAL) <= 61.0 THEN 1 WHEN NULLIF(TRIM(\"age\"), '') IS NOT NULL THEN 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"m7\";","notes":"Applied the Threshold Rarity CDF template with measure_col bound to \"age\" and measure_threshold fixed at 61.0. Because \"age\" is stored as TEXT in the schema snapshot, the query casts it to REAL and ignores blank values so the empirical CDF is computed on non-missing ages only."}