Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9799, "bytes_utf8": 9799, "lines": 302, "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": 615, "bytes_utf8": 615, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 15059, "cached_input_tokens": 12032, "output_tokens": 518, "reasoning_output_tokens": 335}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_0086d96e152bf87a",
+    "api_calls": 0,
+    "input_tokens": 15059,
+    "cached_input_tokens": 12032,
+    "output_tokens": 518,
+    "total_tokens": 15577,
+    "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": 12406.36,
+    "sql_execution_elapsed_ms_total": 14.49,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/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": "2026-05-19T15:38:30.603815+00:00",
+  "ended_at": "2026-05-19T15:38:43.010193+00:00",
+  "elapsed_ms": 12406.36,
+  "prompt_metrics": {
+    "chars": 9799,
+    "bytes_utf8": 9799,
+    "lines": 302,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1011,
+    "bytes_utf8": 1011,
+    "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": 615,
+      "bytes_utf8": 615,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 15059,
+      "cached_input_tokens": 12032,
+      "output_tokens": 518,
+      "reasoning_output_tokens": 335
+    }
+  },
+  "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_a/n9/artifacts/v2q_n9_0086d96e152bf87a/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,302 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+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": "n9",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=feature_16, measure_col=feature_1.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "feature_16",
+    "measure_col": "feature_1",
+    "item_col": "feature_2",
+    "top_k": 17,
+    "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": 50.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e3-7c64-79e1-b82a-5913e4e117a5"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"feature_16\\\", \\\"feature_2\\\",\\n       SUM(CAST(\\\"feature_1\\\" AS REAL)) AS \\\"total_measure\\\",\\n       SUM(CAST(\\\"feature_1\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"feature_1\\\" AS REAL))) OVER (PARTITION BY \\\"feature_16\\\") AS \\\"share_within_group\\\"\\nFROM \\\"n9\\\"\\nGROUP BY \\\"feature_16\\\", \\\"feature_2\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the Within-Group Share of Total template with group_col=\\\"feature_16\\\", item_col=\\\"feature_2\\\", and measure_col=\\\"feature_1\\\". Cast \\\"feature_1\\\" from TEXT to REAL for numeric aggregation in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":15059,"cached_input_tokens":12032,"output_tokens":518,"reasoning_output_tokens":335}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0086d96e152bf87a/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"feature_16\", \"feature_2\",\n       SUM(CAST(\"feature_1\" AS REAL)) AS \"total_measure\",\n       SUM(CAST(\"feature_1\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"feature_1\" AS REAL))) OVER (PARTITION BY \"feature_16\") AS \"share_within_group\"\nFROM \"n9\"\nGROUP BY \"feature_16\", \"feature_2\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the Within-Group Share of Total template with group_col=\"feature_16\", item_col=\"feature_2\", and measure_col=\"feature_1\". Cast \"feature_1\" from TEXT to REAL for numeric aggregation in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9558, "bytes_utf8": 9558, "lines": 298, "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": 547, "bytes_utf8": 547, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14991, "cached_input_tokens": 13696, "output_tokens": 536, "reasoning_output_tokens": 385}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_009274372ae62a75",
+    "api_calls": 0,
+    "input_tokens": 14991,
+    "cached_input_tokens": 13696,
+    "output_tokens": 536,
+    "total_tokens": 15527,
+    "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": 14016.45,
+    "sql_execution_elapsed_ms_total": 12.73,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/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": "2026-05-19T15:45:19.968475+00:00",
+  "ended_at": "2026-05-19T15:45:33.984945+00:00",
+  "elapsed_ms": 14016.45,
+  "prompt_metrics": {
+    "chars": 9558,
+    "bytes_utf8": 9558,
+    "lines": 298,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 911,
+    "bytes_utf8": 911,
+    "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": 547,
+      "bytes_utf8": 547,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14991,
+      "cached_input_tokens": 13696,
+      "output_tokens": 536,
+      "reasoning_output_tokens": 385
+    }
+  },
+  "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_a/n9/artifacts/v2q_n9_009274372ae62a75/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,298 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_quantile_tail_slice",
+    "template_name": "Quantile Tail Slice",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n9",
+  "question": "Use template Quantile Tail Slice to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=feature_3.",
+  "planned_template_id": "tpl_m4_quantile_tail_slice",
+  "bindings": {
+    "measure_col": "feature_3",
+    "top_k": 11,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 58.0,
+    "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": "WITH buckets AS (\n    SELECT {measure_col},\n           NTILE({num_tiles}) OVER (ORDER BY {measure_col} DESC) AS tail_bucket\n    FROM {table}\n)\nSELECT {measure_col}\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY {measure_col} DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e9-bb8b-7340-807e-48444dcfb628"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_quantile_tail_slice\\nWITH buckets AS (\\n    SELECT\\n        \\\"feature_3\\\",\\n        NTILE(10) OVER (ORDER BY CAST(\\\"feature_3\\\" AS INTEGER) DESC) AS tail_bucket\\n    FROM \\\"n9\\\"\\n)\\nSELECT \\\"feature_3\\\"\\nFROM buckets\\nWHERE tail_bucket = 1\\nORDER BY CAST(\\\"feature_3\\\" AS INTEGER) DESC;\",\"notes\":\"Applied the Quantile Tail Slice template with measure_col bound to \\\"feature_3\\\" and num_tiles=10. CAST(\\\"feature_3\\\" AS INTEGER) is used for correct numeric ordering because the SQLite schema stores features as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14991,"cached_input_tokens":13696,"output_tokens":536,"reasoning_output_tokens":385}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_009274372ae62a75/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_quantile_tail_slice\nWITH buckets AS (\n    SELECT\n        \"feature_3\",\n        NTILE(10) OVER (ORDER BY CAST(\"feature_3\" AS INTEGER) DESC) AS tail_bucket\n    FROM \"n9\"\n)\nSELECT \"feature_3\"\nFROM buckets\nWHERE tail_bucket = 1\nORDER BY CAST(\"feature_3\" AS INTEGER) DESC;","notes":"Applied the Quantile Tail Slice template with measure_col bound to \"feature_3\" and num_tiles=10. CAST(\"feature_3\" AS INTEGER) is used for correct numeric ordering because the SQLite schema stores features as TEXT."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9805, "bytes_utf8": 9805, "lines": 302, "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": 566, "bytes_utf8": 566, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 15060, "cached_input_tokens": 13696, "output_tokens": 595, "reasoning_output_tokens": 415}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_0209729875e8a0e5",
+    "api_calls": 0,
+    "input_tokens": 15060,
+    "cached_input_tokens": 13696,
+    "output_tokens": 595,
+    "total_tokens": 15655,
+    "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": 12420.34,
+    "sql_execution_elapsed_ms_total": 16.52,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/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": "2026-05-19T15:35:27.009928+00:00",
+  "ended_at": "2026-05-19T15:35:39.430296+00:00",
+  "elapsed_ms": 12420.34,
+  "prompt_metrics": {
+    "chars": 9805,
+    "bytes_utf8": 9805,
+    "lines": 302,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 948,
+    "bytes_utf8": 948,
+    "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": 566,
+      "bytes_utf8": 566,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 15060,
+      "cached_input_tokens": 13696,
+      "output_tokens": 595,
+      "reasoning_output_tokens": 415
+    }
+  },
+  "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_a/n9/artifacts/v2q_n9_0209729875e8a0e5/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,302 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+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": "n9",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=feature_10, measure_col=feature_11.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "feature_10",
+    "measure_col": "feature_11",
+    "item_col": "feature_12",
+    "top_k": 11,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 97.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e0-af15-78d0-9802-233a99263d87"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"feature_10\\\",\\n  \\\"feature_12\\\",\\n  SUM(CAST(\\\"feature_11\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"feature_11\\\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\\\"feature_11\\\" AS REAL))) OVER (PARTITION BY \\\"feature_10\\\"), 0) AS share_within_group\\nFROM \\\"n9\\\"\\nGROUP BY \\\"feature_10\\\", \\\"feature_12\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Uses the Within-Group Share of Total template with \\\"feature_10\\\" as group_col, \\\"feature_12\\\" as item_col, and summed numeric \\\"feature_11\\\" as measure_col.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":15060,"cached_input_tokens":13696,"output_tokens":595,"reasoning_output_tokens":415}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0209729875e8a0e5/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"feature_10\",\n  \"feature_12\",\n  SUM(CAST(\"feature_11\" AS REAL)) AS total_measure,\n  SUM(CAST(\"feature_11\" AS REAL)) * 100.0 / NULLIF(SUM(SUM(CAST(\"feature_11\" AS REAL))) OVER (PARTITION BY \"feature_10\"), 0) AS share_within_group\nFROM \"n9\"\nGROUP BY \"feature_10\", \"feature_12\"\nORDER BY share_within_group DESC;","notes":"Uses the Within-Group Share of Total template with \"feature_10\" as group_col, \"feature_12\" as item_col, and summed numeric \"feature_11\" as measure_col."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_04c4c9066453fea0/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9326, "bytes_utf8": 9326, "lines": 298, "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": 378, "bytes_utf8": 378, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14927, "cached_input_tokens": 12032, "output_tokens": 368, "reasoning_output_tokens": 259}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_04c4c9066453fea0/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_04c4c9066453fea0",
+    "api_calls": 0,
+    "input_tokens": 14927,
+    "cached_input_tokens": 12032,
+    "output_tokens": 368,
+    "total_tokens": 15295,
+    "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": 9150.07,
+    "sql_execution_elapsed_ms_total": 2.83,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_04c4c9066453fea0/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_04c4c9066453fea0/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": "2026-05-19T16:03:42.761627+00:00",
+  "ended_at": "2026-05-19T16:03:51.911727+00:00",
+  "elapsed_ms": 9150.07,
+  "prompt_metrics": {
+    "chars": 9326,
+    "bytes_utf8": 9326,
+    "lines": 298,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 722,
+    "bytes_utf8": 722,
+    "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": 378,
+      "bytes_utf8": 378,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14927,
+      "cached_input_tokens": 12032,
+      "output_tokens": 368,
+      "reasoning_output_tokens": 259
+    }
+  },
+  "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_a/n9/artifacts/v2q_n9_04c4c9066453fea0/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,298 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+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": "n9",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=feature_13.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "feature_13",
+    "top_k": 13,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 68.0,
+    "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_a/n9/artifacts/v2q_n9_04c4c9066453fea0/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-8f52-79f1-a815-18244f127ca9"}
+{"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 CAST(\\\"feature_13\\\" AS REAL) <= 68.0 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"n9\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template with measure_col bound to \\\"feature_13\\\" and measure_threshold fixed at 68.0. CAST to REAL is used because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14927,"cached_input_tokens":12032,"output_tokens":368,"reasoning_output_tokens":259}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_04c4c9066453fea0/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN CAST(\"feature_13\" AS REAL) <= 68.0 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"n9\";","notes":"Applied the Threshold Rarity CDF template with measure_col bound to \"feature_13\" and measure_threshold fixed at 68.0. CAST to REAL is used because the schema stores feature columns as TEXT."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9628, "bytes_utf8": 9628, "lines": 303, "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": 390, "bytes_utf8": 390, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14997, "cached_input_tokens": 12032, "output_tokens": 459, "reasoning_output_tokens": 342}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_0646409f2deb6567",
+    "api_calls": 0,
+    "input_tokens": 14997,
+    "cached_input_tokens": 12032,
+    "output_tokens": 459,
+    "total_tokens": 15456,
+    "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": 10396.28,
+    "sql_execution_elapsed_ms_total": 10.09,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/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": "2026-05-19T16:03:14.263032+00:00",
+  "ended_at": "2026-05-19T16:03:24.659340+00:00",
+  "elapsed_ms": 10396.28,
+  "prompt_metrics": {
+    "chars": 9628,
+    "bytes_utf8": 9628,
+    "lines": 303,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 754,
+    "bytes_utf8": 754,
+    "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": 390,
+      "bytes_utf8": 390,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14997,
+      "cached_input_tokens": 12032,
+      "output_tokens": 459,
+      "reasoning_output_tokens": 342
+    }
+  },
+  "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_a/n9/artifacts/v2q_n9_0646409f2deb6567/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,303 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+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": "n9",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=feature_1, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "feature_1",
+    "condition_col": "class",
+    "condition_value": "4",
+    "positive_value": "2",
+    "negative_value": "4",
+    "top_k": 17,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 74.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fa-1fea-7f60-88e1-c66fff7894ea"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT\\n  \\\"feature_1\\\",\\n  AVG(CASE WHEN \\\"class\\\" = '4' THEN 1.0 ELSE 0.0 END) AS \\\"condition_rate\\\"\\nFROM \\\"n9\\\"\\nGROUP BY \\\"feature_1\\\"\\nORDER BY \\\"condition_rate\\\" DESC;\",\"notes\":\"Uses the planned Grouped Condition Rate template with group_col=\\\"feature_1\\\" and condition_col=\\\"class\\\", treating class='4' as the target condition.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14997,"cached_input_tokens":12032,"output_tokens":459,"reasoning_output_tokens":342}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0646409f2deb6567/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT\n  \"feature_1\",\n  AVG(CASE WHEN \"class\" = '4' THEN 1.0 ELSE 0.0 END) AS \"condition_rate\"\nFROM \"n9\"\nGROUP BY \"feature_1\"\nORDER BY \"condition_rate\" DESC;","notes":"Uses the planned Grouped Condition Rate template with group_col=\"feature_1\" and condition_col=\"class\", treating class='4' as the target condition."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/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": "2026-05-19T16:08:03.179163+00:00",
+  "ended_at": "2026-05-19T16:08:06.541504+00:00",
+  "elapsed_ms": 3362.31,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 9476,
+    "bytes_utf8": 9476,
+    "lines": 300,
+    "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_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:08:07.543642+00:00",
+  "ended_at": "2026-05-19T16:08:10.486197+00:00",
+  "elapsed_ms": 2942.52,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 9476,
+    "bytes_utf8": 9476,
+    "lines": 300,
+    "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_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,300 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n9",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=feature_14, measure_col=feature_5.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "feature_14",
+    "measure_col": "feature_5",
+    "top_k": 17,
+    "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": 69.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,300 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n9",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=feature_14, measure_col=feature_5.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "feature_14",
+    "measure_col": "feature_5",
+    "top_k": 17,
+    "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": 69.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-8865-7db0-9989-44e21f8c4d25"}
+{"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_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-8865-7db0-9989-44e21f8c4d25"}
+{"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_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-996f-7823-ad7b-1442db61e478"}
+{"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_a/n9/artifacts/v2q_n9_0811c2bf2bae56d4/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-996f-7823-ad7b-1442db61e478"}
+{"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_a/n9/artifacts/v2q_n9_0b08cef145edec67/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9794, "bytes_utf8": 9794, "lines": 302, "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": 580, "bytes_utf8": 580, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 15056, "cached_input_tokens": 12032, "output_tokens": 687, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0b08cef145edec67/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": "n9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n9_0b08cef145edec67",
+    "api_calls": 0,
+    "input_tokens": 15056,
+    "cached_input_tokens": 12032,
+    "output_tokens": 687,
+    "total_tokens": 15743,
+    "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": 13548.1,
+    "sql_execution_elapsed_ms_total": 10.57,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0b08cef145edec67/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0b08cef145edec67/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": "2026-05-19T15:38:56.143514+00:00",
+  "ended_at": "2026-05-19T15:39:09.691645+00:00",
+  "elapsed_ms": 13548.1,
+  "prompt_metrics": {
+    "chars": 9794,
+    "bytes_utf8": 9794,
+    "lines": 302,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 962,
+    "bytes_utf8": 962,
+    "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": 580,
+      "bytes_utf8": 580,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 15056,
+      "cached_input_tokens": 12032,
+      "output_tokens": 687,
+      "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_a/n9/artifacts/v2q_n9_0b08cef145edec67/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,302 @@
+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: n9
+- dataset_name: Pen Based Recognition Of Handwritten Digits
+- table_name: n9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 10992
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 3.
+- feature_4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 4.
+- feature_5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 5.
+- feature_6: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 6.
+- feature_7: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 7.
+- feature_8: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 8.
+- feature_9: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 9.
+- feature_10: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 10.
+- feature_11: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 11.
+- feature_12: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 12.
+- feature_13: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 13.
+- feature_14: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 14.
+- feature_15: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 15.
+- feature_16: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for feature 16.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '2', '4', '7', '9'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['feature_1', 'feature_2', 'class'], ['feature_1', 'feature_1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/81/pen-based+recognition+of+handwritten+digits
+
+SQLite schema snapshot:
+{
+  "table_name": "n9",
+  "quoted_table_name": "\"n9\"",
+  "row_count": 10992,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_10",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_11",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_12",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_13",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_14",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_15",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_16",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "0",
+      "feature_2": "58",
+      "feature_3": "18",
+      "feature_4": "26",
+      "feature_5": "49",
+      "feature_6": "0",
+      "feature_7": "86",
+      "feature_8": "18",
+      "feature_9": "100",
+      "feature_10": "50",
+      "feature_11": "86",
+      "feature_12": "82",
+      "feature_13": "45",
+      "feature_14": "100",
+      "feature_15": "17",
+      "feature_16": "77",
+      "class": "0"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "99",
+      "feature_3": "51",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "96",
+      "feature_7": "72",
+      "feature_8": "59",
+      "feature_9": "56",
+      "feature_10": "16",
+      "feature_11": "41",
+      "feature_12": "0",
+      "feature_13": "24",
+      "feature_14": "39",
+      "feature_15": "81",
+      "feature_16": "50",
+      "class": "7"
+    },
+    {
+      "feature_1": "0",
+      "feature_2": "88",
+      "feature_3": "44",
+      "feature_4": "100",
+      "feature_5": "100",
+      "feature_6": "85",
+      "feature_7": "88",
+      "feature_8": "45",
+      "feature_9": "60",
+      "feature_10": "8",
+      "feature_11": "25",
+      "feature_12": "0",
+      "feature_13": "32",
+      "feature_14": "25",
+      "feature_15": "98",
+      "feature_16": "27",
+      "class": "7"
+    },
+    {
+      "feature_1": "63",
+      "feature_2": "100",
+      "feature_3": "31",
+      "feature_4": "80",
+      "feature_5": "0",
+      "feature_6": "49",
+      "feature_7": "70",
+      "feature_8": "50",
+      "feature_9": "100",
+      "feature_10": "67",
+      "feature_11": "68",
+      "feature_12": "90",
+      "feature_13": "45",
+      "feature_14": "45",
+      "feature_15": "29",
+      "feature_16": "0",
+      "class": "4"
+    },
+    {
+      "feature_1": "3",
+      "feature_2": "72",
+      "feature_3": "26",
+      "feature_4": "100",
+      "feature_5": "50",
+      "feature_6": "80",
+      "feature_7": "31",
+      "feature_8": "48",
+      "feature_9": "0",
+      "feature_10": "19",
+      "feature_11": "13",
+      "feature_12": "12",
+      "feature_13": "65",
+      "feature_14": "0",
+      "feature_15": "100",
+      "feature_16": "17",
+      "class": "2"
+    }
+  ]
+}
+
+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": "n9",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=class, measure_col=feature_2.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "feature_2",
+    "item_col": "feature_3",
+    "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": 96.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n9/artifacts/v2q_n9_0b08cef145edec67/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e3-e015-71e2-aa23-12100f0277c5"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"class\\\",\\n  \\\"feature_3\\\",\\n  SUM(CAST(\\\"feature_2\\\" AS REAL)) AS total_measure,\\n  SUM(CAST(\\\"feature_2\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"feature_2\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n9\\\"\\nGROUP BY \\\"class\\\", \\\"feature_3\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"class\\\", item_col=\\\"feature_3\\\", and measure_col=\\\"feature_2\\\". CAST is used because the schema stores feature columns as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":15056,"cached_input_tokens":12032,"output_tokens":687,"reasoning_output_tokens":516}}