Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: 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.
+Result preview: [{"ever_married": "Yes", "group_value": 98845.8}, {"ever_married": "No", "group_value": 42991.1}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/generated_sql.sql

@@ -0,0 +1,27 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: m7
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m7_0088d25a69094734
+-- problem_id: v2p_m7_5073218d3b829874
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "ever_married", SUM(CAST(NULLIF("bmi", '') AS REAL)) AS group_value
+    FROM "m7"
+    WHERE NULLIF("bmi", '') IS NOT NULL
+    GROUP BY "ever_married"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."ever_married", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.1
+ORDER BY g.group_value DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_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;", "result": "{\"query\": \"-- 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;\", \"columns\": [\"ever_married\", \"group_value\"], \"rows\": [{\"ever_married\": \"Yes\", \"group_value\": 98845.8}, {\"ever_married\": \"No\", \"group_value\": 42991.1}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.93}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0088d25a69094734/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T15:47:26.302126+00:00",
+  "ended_at": "2026-05-19T15:47:42.999400+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_0088d25a69094734",
+    "problem_id": "v2p_m7_5073218d3b829874",
+    "dataset_id": "m7",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "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.",
+    "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
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "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;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/2",
+      "binding_index=75"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/sql/v2q_m7_0088d25a69094734.sql",
+  "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/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:47:42.991926+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 16686.75, "started_at": "2026-05-19T15:47:26.303753+00:00", "ended_at": "2026-05-19T15:47:42.990553+00:00", "prompt_metrics": {"chars": 8211, "bytes_utf8": 8211, "lines": 240, "estimated_tokens": null}, "response_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}, "stderr_preview": "", "stdout_preview": "{\"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_0088d25a69094734/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "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_01d4023046378f99/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=gender, measure_col=id.
+Result preview: [{"gender": "Other", "age": "26", "total_measure": 56156, "share_within_group": 100.0}, {"gender": "Female", "age": "78", "total_measure": 2376968, "share_within_group": 2.1763085038765317}, {"gender": "Female", "age": "45", "total_measure": 2282593, "share_within_group": 2.089900476905471}, {"gender": "Male", "age": "53", "total_measure": 1602110, "share_within_group": 2.0717892946151726}, {"gender": "Male", "age": "57", "total_measure": 1595453, "share_within_group": 2.063180708853737}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/generated_sql.sql

@@ -0,0 +1,20 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: m7
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m7_01d4023046378f99
+-- problem_id: v2p_m7_71c2404dcc21d335
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "gender", "age",
+       SUM(CAST("id" AS NUMERIC)) AS total_measure,
+       SUM(CAST("id" AS NUMERIC)) * 100.0 / SUM(SUM(CAST("id" AS NUMERIC))) OVER (PARTITION BY "gender") AS share_within_group
+FROM "m7"
+GROUP BY "gender", "age"
+ORDER BY share_within_group DESC
+LIMIT 12;

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"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;", "result": "{\"query\": \"-- 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;\", \"columns\": [\"gender\", \"age\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"gender\": \"Other\", \"age\": \"26\", \"total_measure\": 56156, \"share_within_group\": 100.0}, {\"gender\": \"Female\", \"age\": \"78\", \"total_measure\": 2376968, \"share_within_group\": 2.1763085038765317}, {\"gender\": \"Female\", \"age\": \"45\", \"total_measure\": 2282593, \"share_within_group\": 2.089900476905471}, {\"gender\": \"Male\", \"age\": \"53\", \"total_measure\": 1602110, \"share_within_group\": 2.0717892946151726}, {\"gender\": \"Male\", \"age\": \"57\", \"total_measure\": 1595453, \"share_within_group\": 2.063180708853737}, {\"gender\": \"Male\", \"age\": \"54\", \"total_measure\": 1521130, \"share_within_group\": 1.9670689588842072}, {\"gender\": \"Male\", \"age\": \"61\", \"total_measure\": 1514976, \"share_within_group\": 1.9591108340868701}, {\"gender\": \"Female\", \"age\": \"79\", \"total_measure\": 2081640, \"share_within_group\": 1.9059115789566978}, {\"gender\": \"Female\", \"age\": \"52\", \"total_measure\": 2042678, \"share_within_group\": 1.8702386830960729}, {\"gender\": \"Female\", \"age\": \"57\", \"total_measure\": 2023427, \"share_within_group\": 1.8526128189665907}, {\"gender\": \"Male\", \"age\": \"55\", \"total_measure\": 1414898, \"share_within_group\": 1.8296936723273798}, {\"gender\": \"Male\", \"age\": \"52\", \"total_measure\": 1398333, \"share_within_group\": 1.8082724280524545}], \"row_count_returned\": 12, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 4.66}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_01d4023046378f99/run_manifest.json

@@ -0,0 +1,91 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T15:37:48.435871+00:00",
+  "ended_at": "2026-05-19T15:38:01.484924+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_01d4023046378f99",
+    "problem_id": "v2p_m7_71c2404dcc21d335",
+    "dataset_id": "m7",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=gender, measure_col=id.",
+    "bindings": {
+      "group_col": "gender",
+      "measure_col": "id",
+      "item_col": "age",
+      "top_k": 12,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 54682.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=9",
+      "sql_variant_index=1/2",
+      "binding_index=32"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 3,
+    "problem_index_within_template": 9,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/sql/v2q_m7_01d4023046378f99.sql",
+  "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/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:38:01.478870+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 13040.65, "started_at": "2026-05-19T15:37:48.437397+00:00", "ended_at": "2026-05-19T15:38:01.478078+00:00", "prompt_metrics": {"chars": 8050, "bytes_utf8": 8050, "lines": 242, "estimated_tokens": null}, "response_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}, "stderr_preview": "", "stdout_preview": "{\"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_01d4023046378f99/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "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_028af02a750d40c1/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=gender.
+Result preview: [{"gender": "Other", "support": 1}, {"gender": "Male", "support": 2115}, {"gender": "Female", "support": 2994}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/generated_sql.sql

@@ -0,0 +1,20 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: m7
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_tail_low_support_group_count_v2
+-- query_record_id: v2q_m7_028af02a750d40c1
+-- problem_id: v2p_m7_c949d66cd97a73ee
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+    "gender",
+    COUNT(*) AS support
+FROM "m7"
+GROUP BY "gender"
+ORDER BY support ASC, "gender"
+LIMIT 10;

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- 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;", "result": "{\"query\": \"-- 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;\", \"columns\": [\"gender\", \"support\"], \"rows\": [{\"gender\": \"Other\", \"support\": 1}, {\"gender\": \"Male\", \"support\": 2115}, {\"gender\": \"Female\", \"support\": 2994}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.95}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_028af02a750d40c1/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T16:04:07.734939+00:00",
+  "ended_at": "2026-05-19T16:04:34.417826+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_028af02a750d40c1",
+    "problem_id": "v2p_m7_c949d66cd97a73ee",
+    "dataset_id": "m7",
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=gender.",
+    "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
+    },
+    "binding_roles": [
+      "group_col"
+    ],
+    "coverage_target_min": "5",
+    "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};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=120"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/sql/v2q_m7_028af02a750d40c1.sql",
+  "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/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:04:11.086252+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3349.58, "started_at": "2026-05-19T16:04:07.735809+00:00", "ended_at": "2026-05-19T16:04:11.085416+00:00", "prompt_metrics": {"chars": 7646, "bytes_utf8": 7646, "lines": 238, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40fa-f0e2-78e1-8f2a-baf079931529\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:04:34.413261+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 22324.41, "started_at": "2026-05-19T16:04:12.087163+00:00", "ended_at": "2026-05-19T16:04:34.411620+00:00", "prompt_metrics": {"chars": 7646, "bytes_utf8": 7646, "lines": 238, "estimated_tokens": null}, "response_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}, "stderr_preview": "", "stdout_preview": "{\"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_028af02a750d40c1/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "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_035483ed5abfa740/run_manifest.json

@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T15:57:42.028091+00:00",
+  "ended_at": "2026-05-19T15:57:49.584048+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_035483ed5abfa740",
+    "problem_id": "v2p_m7_61cf28105c17fcf9",
+    "dataset_id": "m7",
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_concentration_consistency",
+    "intended_facet_id": "rare_target_concentration",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=ever_married, measure_col=bmi.",
+    "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
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "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;",
+    "notes": [
+      "default_facets=rare_target_concentration",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=2/2",
+      "binding_index=91"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_035483ed5abfa740/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T15:57:45.318716+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3289.03, "started_at": "2026-05-19T15:57:42.028985+00:00", "ended_at": "2026-05-19T15:57:45.318051+00:00", "prompt_metrics": {"chars": 7837, "bytes_utf8": 7837, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f5-0e29-7362-8055-716be10ec868\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T15:57:49.583930+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3263.11, "started_at": "2026-05-19T15:57:46.320064+00:00", "ended_at": "2026-05-19T15:57:49.583216+00:00", "prompt_metrics": {"chars": 7837, "bytes_utf8": 7837, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f5-1ed3-7981-b588-ed294fbeeb96\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=age.
+Result preview: [{"empirical_cdf_at_threshold": 0.7596868884540118}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/generated_sql.sql

@@ -0,0 +1,15 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: m7
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_threshold_rarity_cdf
+-- query_record_id: v2q_m7_03cbd3b43bfb5aca
+-- problem_id: v2p_m7_24ee7c37e3723747
+-- realization_mode: agent
+-- source_kind: agent
+SELECT 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"
+FROM "m7";

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 2, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- 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\";", "result": "{\"query\": \"-- 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\\\";\", \"columns\": [\"empirical_cdf_at_threshold\"], \"rows\": [{\"empirical_cdf_at_threshold\": 0.7596868884540118}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.5}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/run_manifest.json

@@ -0,0 +1,87 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T16:02:27.455388+00:00",
+  "ended_at": "2026-05-19T16:02:41.495562+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_03cbd3b43bfb5aca",
+    "problem_id": "v2p_m7_24ee7c37e3723747",
+    "dataset_id": "m7",
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=age.",
+    "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
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=1/1",
+      "binding_index=109"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 10,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/sql/v2q_m7_03cbd3b43bfb5aca.sql",
+  "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/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:02:30.743182+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3285.56, "started_at": "2026-05-19T16:02:27.456735+00:00", "ended_at": "2026-05-19T16:02:30.742329+00:00", "prompt_metrics": {"chars": 7584, "bytes_utf8": 7584, "lines": 238, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f9-692b-7ae1-a440-7208c977cb99\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:02:41.489231+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9743.49, "started_at": "2026-05-19T16:02:31.744129+00:00", "ended_at": "2026-05-19T16:02:41.487653+00:00", "prompt_metrics": {"chars": 7584, "bytes_utf8": 7584, "lines": 238, "estimated_tokens": null}, "response_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}, "stderr_preview": "", "stdout_preview": "{\"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.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_03cbd3b43bfb5aca/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "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_040d5de4a5685606/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 8054, "bytes_utf8": 8054, "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": 624, "bytes_utf8": 624, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14394, "cached_input_tokens": 13696, "output_tokens": 692, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_040d5de4a5685606/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_040d5de4a5685606",
+    "api_calls": 0,
+    "input_tokens": 14394,
+    "cached_input_tokens": 13696,
+    "output_tokens": 692,
+    "total_tokens": 15086,
+    "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": 13425.71,
+    "sql_execution_elapsed_ms_total": 11.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_040d5de4a5685606/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_040d5de4a5685606/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": "2026-05-19T15:36:12.796459+00:00",
+  "ended_at": "2026-05-19T15:36:26.222213+00:00",
+  "elapsed_ms": 13425.71,
+  "prompt_metrics": {
+    "chars": 8054,
+    "bytes_utf8": 8054,
+    "lines": 242,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1020,
+    "bytes_utf8": 1020,
+    "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": 624,
+      "bytes_utf8": 624,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14394,
+      "cached_input_tokens": 13696,
+      "output_tokens": 692,
+      "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_040d5de4a5685606/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,242 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m7
+- dataset_name: Stroke Prediction Dataset
+- table_name: m7
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Residence_type`.
+- task_type: classification
+- target_column: Residence_type
+- main_row_count: 5110
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for gender.
+- age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Numeric field for age.
+- hypertension: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for hypertension.
+- heart_disease: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for heart disease.
+- ever_married: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for ever married.
+- work_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for work type.
+- Residence_type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Residence type.
+- avg_glucose_level: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for avg glucose level.
+- bmi: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate', 'missingness_candidate'] desc=Numeric field for bmi.
+- smoking_status: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for smoking status.
+- stroke: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for stroke.
+- useful_field_combinations: [['gender', 'hypertension', 'Residence_type'], ['gender', 'age', 'Residence_type'], ['gender', 'gender', 'Residence_type']]
+- fields_requiring_caution: ['Residence_type', 'avg_glucose_level', 'bmi']
+- source_url: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
+
+SQLite schema snapshot:
+{
+  "table_name": "m7",
+  "quoted_table_name": "\"m7\"",
+  "row_count": 5110,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "hypertension",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "heart_disease",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ever_married",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "work_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Residence_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bmi",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "smoking_status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "stroke",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "9046",
+      "gender": "Male",
+      "age": "67",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "228.69",
+      "bmi": "36.6",
+      "smoking_status": "formerly smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "51676",
+      "gender": "Female",
+      "age": "61",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "202.21",
+      "bmi": "",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "31112",
+      "gender": "Male",
+      "age": "80",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "105.92",
+      "bmi": "32.5",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "60182",
+      "gender": "Female",
+      "age": "49",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "171.23",
+      "bmi": "34.4",
+      "smoking_status": "smokes",
+      "stroke": "1"
+    },
+    {
+      "id": "1665",
+      "gender": "Female",
+      "age": "79",
+      "hypertension": "1",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "174.12",
+      "bmi": "24",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=work_type, measure_col=id.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "work_type",
+    "measure_col": "id",
+    "item_col": "age",
+    "top_k": 18,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 48651.76,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_040d5de4a5685606/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e1-61f2-7d22-bd2d-93a4a2043dc6"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"work_type\\\", \\\"age\\\",\\n       SUM(CAST(NULLIF(\\\"id\\\", '') AS REAL)) AS \\\"total_measure\\\",\\n       SUM(CAST(NULLIF(\\\"id\\\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\\\"id\\\", '') AS REAL))) OVER (PARTITION BY \\\"work_type\\\") AS \\\"share_within_group\\\"\\nFROM \\\"m7\\\"\\nGROUP BY \\\"work_type\\\", \\\"age\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the provided within-group share template with group_col=\\\"work_type\\\", item_col=\\\"age\\\", and measure_col=\\\"id\\\". Casted \\\"id\\\" from TEXT to REAL and treated empty strings as NULL for SQLite-safe aggregation.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14394,"cached_input_tokens":13696,"output_tokens":692,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_040d5de4a5685606/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"work_type\", \"age\",\n       SUM(CAST(NULLIF(\"id\", '') AS REAL)) AS \"total_measure\",\n       SUM(CAST(NULLIF(\"id\", '') AS REAL)) * 100.0 / SUM(SUM(CAST(NULLIF(\"id\", '') AS REAL))) OVER (PARTITION BY \"work_type\") AS \"share_within_group\"\nFROM \"m7\"\nGROUP BY \"work_type\", \"age\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the provided within-group share template with group_col=\"work_type\", item_col=\"age\", and measure_col=\"id\". Casted \"id\" from TEXT to REAL and treated empty strings as NULL for SQLite-safe aggregation."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_05b6fd3ca209a6ec/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7841, "bytes_utf8": 7841, "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": 842, "bytes_utf8": 842, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14315, "cached_input_tokens": 13696, "output_tokens": 1861, "reasoning_output_tokens": 1617}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_05b6fd3ca209a6ec/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_05b6fd3ca209a6ec",
+    "api_calls": 0,
+    "input_tokens": 14315,
+    "cached_input_tokens": 13696,
+    "output_tokens": 1861,
+    "total_tokens": 16176,
+    "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": 31393.73,
+    "sql_execution_elapsed_ms_total": 11.95,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_05b6fd3ca209a6ec/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_05b6fd3ca209a6ec/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": "2026-05-19T15:58:31.845906+00:00",
+  "ended_at": "2026-05-19T15:59:03.239672+00:00",
+  "elapsed_ms": 31393.73,
+  "prompt_metrics": {
+    "chars": 7841,
+    "bytes_utf8": 7841,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1275,
+    "bytes_utf8": 1275,
+    "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": 842,
+      "bytes_utf8": 842,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14315,
+      "cached_input_tokens": 13696,
+      "output_tokens": 1861,
+      "reasoning_output_tokens": 1617
+    }
+  },
+  "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_05b6fd3ca209a6ec/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=Residence_type, measure_col=age.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "Residence_type",
+    "measure_col": "age",
+    "top_k": 18,
+    "top_n": 5,
+    "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": 55.0,
+    "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_05b6fd3ca209a6ec/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f5-d0bd-7340-ab0e-5fbf1aaaa477"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ranked\\\" AS (\\n  SELECT\\n    \\\"Residence_type\\\",\\n    CAST(\\\"age\\\" AS REAL) AS \\\"age_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"Residence_type\\\"\\n      ORDER BY CAST(\\\"age\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"Residence_type\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"m7\\\"\\n  WHERE \\\"age\\\" IS NOT NULL AND \\\"age\\\" <> ''\\n)\\nSELECT\\n  \\\"Residence_type\\\",\\n  MIN(\\\"age_value\\\") AS \\\"percentile_measure\\\"\\nFROM \\\"ranked\\\"\\nWHERE \\\"rn\\\" >= CAST((0.95 * \\\"cnt\\\") + 0.999999999 AS INTEGER)\\nGROUP BY \\\"Residence_type\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this emulates the 95th percentile with a nearest-rank window-function approach, casting \\\"age\\\" from TEXT to REAL and excluding blank ages.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14315,"cached_input_tokens":13696,"output_tokens":1861,"reasoning_output_tokens":1617}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_05b6fd3ca209a6ec/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ranked\" AS (\n  SELECT\n    \"Residence_type\",\n    CAST(\"age\" AS REAL) AS \"age_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"Residence_type\"\n      ORDER BY CAST(\"age\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"Residence_type\"\n    ) AS \"cnt\"\n  FROM \"m7\"\n  WHERE \"age\" IS NOT NULL AND \"age\" <> ''\n)\nSELECT\n  \"Residence_type\",\n  MIN(\"age_value\") AS \"percentile_measure\"\nFROM \"ranked\"\nWHERE \"rn\" >= CAST((0.95 * \"cnt\") + 0.999999999 AS INTEGER)\nGROUP BY \"Residence_type\"\nORDER BY \"percentile_measure\" DESC;","notes":"SQLite does not support PERCENTILE_CONT, so this emulates the 95th percentile with a nearest-rank window-function approach, casting \"age\" from TEXT to REAL and excluding blank ages."}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=heart_disease, condition_col=heart_disease.
+Result preview: [{"heart_disease": "1", "condition_rate": 1.0}, {"heart_disease": "0", "condition_rate": 0.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_c
+-- sql_source_dataset_id: m7
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_m7_0687a47cc2d15b75
+-- problem_id: v2p_m7_b452b1f8ff040e3b
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "heart_disease",
+       AVG(CASE WHEN "heart_disease" = '1' THEN 1 ELSE 0 END) AS condition_rate
+FROM "m7"
+GROUP BY "heart_disease"
+ORDER BY condition_rate DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"heart_disease\",\n       AVG(CASE WHEN \"heart_disease\" = '1' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m7\"\nGROUP BY \"heart_disease\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"heart_disease\\\",\\n       AVG(CASE WHEN \\\"heart_disease\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m7\\\"\\nGROUP BY \\\"heart_disease\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"heart_disease\", \"condition_rate\"], \"rows\": [{\"heart_disease\": \"1\", \"condition_rate\": 1.0}, {\"heart_disease\": \"0\", \"condition_rate\": 0.0}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.68}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/run_manifest.json

@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_c",
+  "dataset_id": "m7",
+  "started_at": "2026-05-19T15:59:58.402847+00:00",
+  "ended_at": "2026-05-19T16:00:08.151401+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m7_0687a47cc2d15b75",
+    "problem_id": "v2p_m7_b452b1f8ff040e3b",
+    "dataset_id": "m7",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=heart_disease, condition_col=heart_disease.",
+    "bindings": {
+      "group_col": "heart_disease",
+      "condition_col": "heart_disease",
+      "condition_value": "1",
+      "positive_value": "0",
+      "negative_value": "1",
+      "top_k": 18,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 114.09,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=2/2",
+      "binding_index=98"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/sql/v2q_m7_0687a47cc2d15b75.sql",
+  "usage_summary": {
+    "dataset_id": "m7",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m7_0687a47cc2d15b75",
+    "api_calls": 0,
+    "input_tokens": 14344,
+    "cached_input_tokens": 13696,
+    "output_tokens": 382,
+    "total_tokens": 14726,
+    "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": 9742.79,
+    "sql_execution_elapsed_ms_total": 1.68,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/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_0687a47cc2d15b75/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:00:08.148477+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9742.79, "started_at": "2026-05-19T15:59:58.404848+00:00", "ended_at": "2026-05-19T16:00:08.147677+00:00", "prompt_metrics": {"chars": 7927, "bytes_utf8": 7927, "lines": 243, "estimated_tokens": null}, "response_metrics": {"chars": 428, "bytes_utf8": 428, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14344, "cached_input_tokens": 13696, "output_tokens": 382, "reasoning_output_tokens": 268}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"heart_disease\\\",\\n       AVG(CASE WHEN \\\"heart_disease\\\" = '1' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m7\\\"\\nGROUP BY \\\"heart_disease\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the requested Grouped Condition Rate template with both group_col and condition_col bound to \\\"heart_disease\\\". The comparison uses '1' because the schema stores values as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m7",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m7_0687a47cc2d15b75",
+  "api_calls": 0,
+  "input_tokens": 14344,
+  "cached_input_tokens": 13696,
+  "output_tokens": 382,
+  "total_tokens": 14726,
+  "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": 9742.79,
+  "sql_execution_elapsed_ms_total": 1.68,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_0687a47cc2d15b75/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_06e65bec3c3ff169/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7658, "bytes_utf8": 7658, "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": 366, "bytes_utf8": 366, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14277, "cached_input_tokens": 0, "output_tokens": 302, "reasoning_output_tokens": 200}}

Query/sql/v2/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_06e65bec3c3ff169/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_06e65bec3c3ff169",
+    "api_calls": 0,
+    "input_tokens": 14277,
+    "cached_input_tokens": 0,
+    "output_tokens": 302,
+    "total_tokens": 14579,
+    "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": 8175.15,
+    "sql_execution_elapsed_ms_total": 1.97,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_c/m7/artifacts/v2q_m7_06e65bec3c3ff169/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_06e65bec3c3ff169/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:46.744325+00:00",
+  "ended_at": "2026-05-19T15:28:54.919507+00:00",
+  "elapsed_ms": 8175.15,
+  "prompt_metrics": {
+    "chars": 7658,
+    "bytes_utf8": 7658,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 728,
+    "bytes_utf8": 728,
+    "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": 366,
+      "bytes_utf8": 366,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14277,
+      "cached_input_tokens": 0,
+      "output_tokens": 302,
+      "reasoning_output_tokens": 200
+    }
+  },
+  "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_06e65bec3c3ff169/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m7
+- dataset_name: Stroke Prediction Dataset
+- table_name: m7
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `Residence_type`.
+- task_type: classification
+- target_column: Residence_type
+- main_row_count: 5110
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for gender.
+- age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Numeric field for age.
+- hypertension: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for hypertension.
+- heart_disease: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for heart disease.
+- ever_married: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for ever married.
+- work_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for work type.
+- Residence_type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for Residence type.
+- avg_glucose_level: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for avg glucose level.
+- bmi: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate', 'missingness_candidate'] desc=Numeric field for bmi.
+- smoking_status: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for smoking status.
+- stroke: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for stroke.
+- useful_field_combinations: [['gender', 'hypertension', 'Residence_type'], ['gender', 'age', 'Residence_type'], ['gender', 'gender', 'Residence_type']]
+- fields_requiring_caution: ['Residence_type', 'avg_glucose_level', 'bmi']
+- source_url: https://www.kaggle.com/datasets/fedesoriano/stroke-prediction-dataset
+
+SQLite schema snapshot:
+{
+  "table_name": "m7",
+  "quoted_table_name": "\"m7\"",
+  "row_count": 5110,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "hypertension",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "heart_disease",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ever_married",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "work_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Residence_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "avg_glucose_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bmi",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "smoking_status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "stroke",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "9046",
+      "gender": "Male",
+      "age": "67",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "228.69",
+      "bmi": "36.6",
+      "smoking_status": "formerly smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "51676",
+      "gender": "Female",
+      "age": "61",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "202.21",
+      "bmi": "",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "31112",
+      "gender": "Male",
+      "age": "80",
+      "hypertension": "0",
+      "heart_disease": "1",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "105.92",
+      "bmi": "32.5",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    },
+    {
+      "id": "60182",
+      "gender": "Female",
+      "age": "49",
+      "hypertension": "0",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Private",
+      "Residence_type": "Urban",
+      "avg_glucose_level": "171.23",
+      "bmi": "34.4",
+      "smoking_status": "smokes",
+      "stroke": "1"
+    },
+    {
+      "id": "1665",
+      "gender": "Female",
+      "age": "79",
+      "hypertension": "1",
+      "heart_disease": "0",
+      "ever_married": "Yes",
+      "work_type": "Self-employed",
+      "Residence_type": "Rural",
+      "avg_glucose_level": "174.12",
+      "bmi": "24",
+      "smoking_status": "never smoked",
+      "stroke": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m7",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=hypertension, measure_col=age.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "hypertension",
+    "measure_col": "age",
+    "top_k": 16,
+    "top_n": 5,
+    "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": 55.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}