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Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0067b8f960b0972c/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": "2026-05-19T16:11:49.811016+00:00",
+  "ended_at": "2026-05-19T16:11:53.003238+00:00",
+  "elapsed_ms": 3192.2,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 6457,
+    "bytes_utf8": 6457,
+    "lines": 228,
+    "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/c10/artifacts/v2q_c10_0067b8f960b0972c/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": "2026-05-19T16:11:54.005568+00:00",
+  "ended_at": "2026-05-19T16:11:59.742743+00:00",
+  "elapsed_ms": 5737.15,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 6457,
+    "bytes_utf8": 6457,
+    "lines": 228,
+    "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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,228 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=s4, measure_col=s5.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "s4",
+    "measure_col": "s5",
+    "top_k": 13,
+    "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": 3.25,
+    "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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,228 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=s4, measure_col=s5.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "s4",
+    "measure_col": "s5",
+    "top_k": 13,
+    "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": 3.25,
+    "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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-fdcf-71e1-bf62-079cfdc10afe"}
+{"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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-fdcf-71e1-bf62-079cfdc10afe"}
+{"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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4102-0e28-7ee2-a02e-02bed5441ebd"}
+{"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/c10/artifacts/v2q_c10_0067b8f960b0972c/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4102-0e28-7ee2-a02e-02bed5441ebd"}
+{"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/c10/artifacts/v2q_c10_0319093e40f8634e/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 6292, "bytes_utf8": 6292, "lines": 226, "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": 303, "bytes_utf8": 303, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14121, "cached_input_tokens": 12032, "output_tokens": 299, "reasoning_output_tokens": 212}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0319093e40f8634e/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": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_0319093e40f8634e",
+    "api_calls": 0,
+    "input_tokens": 14121,
+    "cached_input_tokens": 12032,
+    "output_tokens": 299,
+    "total_tokens": 14420,
+    "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": 8319.34,
+    "sql_execution_elapsed_ms_total": 456.69,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0319093e40f8634e/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0319093e40f8634e/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": "2026-05-19T15:32:37.292551+00:00",
+  "ended_at": "2026-05-19T15:32:45.611917+00:00",
+  "elapsed_ms": 8319.34,
+  "prompt_metrics": {
+    "chars": 6292,
+    "bytes_utf8": 6292,
+    "lines": 226,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 657,
+    "bytes_utf8": 657,
+    "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": 303,
+      "bytes_utf8": 303,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14121,
+      "cached_input_tokens": 12032,
+      "output_tokens": 299,
+      "reasoning_output_tokens": 212
+    }
+  },
+  "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/c10/artifacts/v2q_c10_0319093e40f8634e/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,226 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=s3.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "s3",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 10.0,
+    "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}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0319093e40f8634e/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40de-1855-7b70-9ff0-300699644cb5"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"s3\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"c10\\\"\\nGROUP BY \\\"s3\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the Grouped Count by Category template with group_col bound to \\\"s3\\\" to inspect the count distribution of rows across s3 subgroups.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14121,"cached_input_tokens":12032,"output_tokens":299,"reasoning_output_tokens":212}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0319093e40f8634e/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"s3\", COUNT(*) AS \"row_count\"\nFROM \"c10\"\nGROUP BY \"s3\"\nORDER BY \"row_count\" DESC;","notes":"Uses the Grouped Count by Category template with group_col bound to \"s3\" to inspect the count distribution of rows across s3 subgroups."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=c5, condition_col=s1.
+Result preview: [{"c5": "5", "condition_rate": 0.25218627438536817}, {"c5": "1", "condition_rate": 0.2514261755625294}, {"c5": "6", "condition_rate": 0.25141884943055576}, {"c5": "7", "condition_rate": 0.2508055785518691}, {"c5": "4", "condition_rate": 0.2503409090909091}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_c10_0653c9fa08e673b7
+-- problem_id: v2p_c10_fac06ec52865343f
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "c5",
+       AVG(CASE WHEN "s1" = '2' THEN 1 ELSE 0 END) AS condition_rate
+FROM "c10"
+GROUP BY "c5"
+ORDER BY condition_rate DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"c5\",\n       AVG(CASE WHEN \"s1\" = '2' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"c10\"\nGROUP BY \"c5\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"c5\\\",\\n       AVG(CASE WHEN \\\"s1\\\" = '2' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c10\\\"\\nGROUP BY \\\"c5\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"c5\", \"condition_rate\"], \"rows\": [{\"c5\": \"5\", \"condition_rate\": 0.25218627438536817}, {\"c5\": \"1\", \"condition_rate\": 0.2514261755625294}, {\"c5\": \"6\", \"condition_rate\": 0.25141884943055576}, {\"c5\": \"7\", \"condition_rate\": 0.2508055785518691}, {\"c5\": \"4\", \"condition_rate\": 0.2503409090909091}, {\"c5\": \"2\", \"condition_rate\": 0.24958742236611697}, {\"c5\": \"8\", \"condition_rate\": 0.24953188259109313}, {\"c5\": \"13\", \"condition_rate\": 0.24951897959963812}, {\"c5\": \"12\", \"condition_rate\": 0.24946681480913885}, {\"c5\": \"3\", \"condition_rate\": 0.24852793155510822}, {\"c5\": \"9\", \"condition_rate\": 0.24758838383838383}, {\"c5\": \"11\", \"condition_rate\": 0.24630919665111523}, {\"c5\": \"10\", \"condition_rate\": 0.246236886118291}], \"row_count_returned\": 13, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 540.69}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/run_manifest.json

@@ -0,0 +1,92 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "c10",
+  "started_at": "2026-05-19T16:04:27.112391+00:00",
+  "ended_at": "2026-05-19T16:04:35.766481+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c10_0653c9fa08e673b7",
+    "problem_id": "v2p_c10_fac06ec52865343f",
+    "dataset_id": "c10",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=c5, condition_col=s1.",
+    "bindings": {
+      "group_col": "c5",
+      "condition_col": "s1",
+      "condition_value": "2",
+      "positive_value": "1",
+      "negative_value": "2",
+      "top_k": 17,
+      "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": 10.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=2/2",
+      "binding_index=97"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 9,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0653c9fa08e673b7.sql",
+  "usage_summary": {
+    "dataset_id": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_0653c9fa08e673b7",
+    "api_calls": 0,
+    "input_tokens": 14209,
+    "cached_input_tokens": 13696,
+    "output_tokens": 227,
+    "total_tokens": 14436,
+    "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": 8095.46,
+    "sql_execution_elapsed_ms_total": 540.69,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:04:35.223869+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 8095.46, "started_at": "2026-05-19T16:04:27.127634+00:00", "ended_at": "2026-05-19T16:04:35.223127+00:00", "prompt_metrics": {"chars": 6614, "bytes_utf8": 6614, "lines": 231, "estimated_tokens": null}, "response_metrics": {"chars": 350, "bytes_utf8": 350, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14209, "cached_input_tokens": 13696, "output_tokens": 227, "reasoning_output_tokens": 122}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"c5\\\",\\n       AVG(CASE WHEN \\\"s1\\\" = '2' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"c10\\\"\\nGROUP BY \\\"c5\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Applied the provided Grouped Condition Rate template with group_col=\\\"c5\\\" and condition_col=\\\"s1\\\", using condition_value '2' as bound.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c10",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c10_0653c9fa08e673b7",
+  "api_calls": 0,
+  "input_tokens": 14209,
+  "cached_input_tokens": 13696,
+  "output_tokens": 227,
+  "total_tokens": 14436,
+  "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": 8095.46,
+  "sql_execution_elapsed_ms_total": 540.69,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0653c9fa08e673b7/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 6372, "bytes_utf8": 6372, "lines": 228, "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": 336, "bytes_utf8": 336, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14147, "cached_input_tokens": 12032, "output_tokens": 621, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/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": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_0691ccbf5c6b7649",
+    "api_calls": 0,
+    "input_tokens": 14147,
+    "cached_input_tokens": 12032,
+    "output_tokens": 621,
+    "total_tokens": 14768,
+    "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": 16326.57,
+    "sql_execution_elapsed_ms_total": 447.2,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/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": "2026-05-19T15:31:01.427445+00:00",
+  "ended_at": "2026-05-19T15:31:17.754042+00:00",
+  "elapsed_ms": 16326.57,
+  "prompt_metrics": {
+    "chars": 6372,
+    "bytes_utf8": 6372,
+    "lines": 228,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 702,
+    "bytes_utf8": 702,
+    "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": 336,
+      "bytes_utf8": 336,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14147,
+      "cached_input_tokens": 12032,
+      "output_tokens": 621,
+      "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/c10/artifacts/v2q_c10_0691ccbf5c6b7649/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,228 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=s4, measure_col=s4.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "s4",
+    "measure_col": "s4",
+    "top_k": 16,
+    "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": 2.98,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40dc-a1c9-7193-a7ee-5e5e2612ee8a"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"s4\\\", SUM(CAST(\\\"s4\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"c10\\\"\\nGROUP BY \\\"s4\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Applied the Grouped Numeric Sum template with group_col=\\\"s4\\\" and measure_col=\\\"s4\\\". CAST is used because the schema stores \\\"s4\\\" as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14147,"cached_input_tokens":12032,"output_tokens":621,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0691ccbf5c6b7649/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"s4\", SUM(CAST(\"s4\" AS NUMERIC)) AS \"total_measure\"\nFROM \"c10\"\nGROUP BY \"s4\"\nORDER BY \"total_measure\" DESC;","notes":"Applied the Grouped Numeric Sum template with group_col=\"s4\" and measure_col=\"s4\". CAST is used because the schema stores \"s4\" as TEXT in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 7183, "bytes_utf8": 7183, "lines": 231, "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": 646, "bytes_utf8": 646, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14355, "cached_input_tokens": 12032, "output_tokens": 701, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/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": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_08a4f48c038856ff",
+    "api_calls": 0,
+    "input_tokens": 14355,
+    "cached_input_tokens": 12032,
+    "output_tokens": 701,
+    "total_tokens": 15056,
+    "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": 15739.89,
+    "sql_execution_elapsed_ms_total": 605.04,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/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": "2026-05-19T15:41:38.804759+00:00",
+  "ended_at": "2026-05-19T15:41:54.544678+00:00",
+  "elapsed_ms": 15739.89,
+  "prompt_metrics": {
+    "chars": 7183,
+    "bytes_utf8": 7183,
+    "lines": 231,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1015,
+    "bytes_utf8": 1015,
+    "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": 646,
+      "bytes_utf8": 646,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14355,
+      "cached_input_tokens": 12032,
+      "output_tokens": 701,
+      "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/c10/artifacts/v2q_c10_08a4f48c038856ff/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,231 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_group_ratio_two_conditions",
+    "template_name": "Grouped Ratio of Two Conditions",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;",
+    "required_roles": [
+      "group_col",
+      "condition_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Grouped Ratio of Two Conditions to probe direction_consistency with semantic role contrastive_conditional_view. Focus on group_col=c2, condition_col=class.",
+  "planned_template_id": "tpl_m4_group_ratio_two_conditions",
+  "bindings": {
+    "group_col": "c2",
+    "condition_col": "class",
+    "condition_value": "0",
+    "positive_value": "0",
+    "negative_value": "1",
+    "top_k": 11,
+    "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": 3.25,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col},\n           SUM(CASE WHEN {condition_col} = {positive_value} THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN {condition_col} = {negative_value} THEN 1 ELSE 0 END) AS denominator_count\n    FROM {table}\n    GROUP BY {group_col}\n)\nSELECT {group_col},\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e6-5b82-7db3-a34d-8ddf411f82c9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_ratio_two_conditions\\nWITH grouped AS (\\n    SELECT \\\"c2\\\",\\n           SUM(CASE WHEN \\\"class\\\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\\n           SUM(CASE WHEN \\\"class\\\" = '1' THEN 1 ELSE 0 END) AS denominator_count\\n    FROM \\\"c10\\\"\\n    GROUP BY \\\"c2\\\"\\n)\\nSELECT \\\"c2\\\",\\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\\nFROM grouped\\nORDER BY condition_ratio DESC;\",\"notes\":\"Uses the grouped ratio template with group_col=\\\"c2\\\" and condition_col=\\\"class\\\", computing the ratio of class '0' to class '1' for each c2 value. NULLIF prevents division-by-zero.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14355,"cached_input_tokens":12032,"output_tokens":701,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_08a4f48c038856ff/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_group_ratio_two_conditions\nWITH grouped AS (\n    SELECT \"c2\",\n           SUM(CASE WHEN \"class\" = '0' THEN 1 ELSE 0 END) AS numerator_count,\n           SUM(CASE WHEN \"class\" = '1' THEN 1 ELSE 0 END) AS denominator_count\n    FROM \"c10\"\n    GROUP BY \"c2\"\n)\nSELECT \"c2\",\n       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio\nFROM grouped\nORDER BY condition_ratio DESC;","notes":"Uses the grouped ratio template with group_col=\"c2\" and condition_col=\"class\", computing the ratio of class '0' to class '1' for each c2 value. NULLIF prevents division-by-zero."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 6925, "bytes_utf8": 6925, "lines": 228, "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": 561, "bytes_utf8": 561, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14283, "cached_input_tokens": 13696, "output_tokens": 495, "reasoning_output_tokens": 334}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/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": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_0a2d21dabad951e9",
+    "api_calls": 0,
+    "input_tokens": 14283,
+    "cached_input_tokens": 13696,
+    "output_tokens": 495,
+    "total_tokens": 14778,
+    "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": 10496.82,
+    "sql_execution_elapsed_ms_total": 523.62,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/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": "2026-05-19T15:51:38.859424+00:00",
+  "ended_at": "2026-05-19T15:51:49.356284+00:00",
+  "elapsed_ms": 10496.82,
+  "prompt_metrics": {
+    "chars": 6925,
+    "bytes_utf8": 6925,
+    "lines": 228,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 928,
+    "bytes_utf8": 928,
+    "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": 561,
+      "bytes_utf8": 561,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14283,
+      "cached_input_tokens": 13696,
+      "output_tokens": 495,
+      "reasoning_output_tokens": 334
+    }
+  },
+  "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/c10/artifacts/v2q_c10_0a2d21dabad951e9/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,228 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c10",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=c2, measure_col=s1.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "c2",
+    "measure_col": "s1",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 3.25,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ef-8384-7cd0-921a-89500401bc78"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"c2\\\", SUM(CAST(\\\"s1\\\" AS NUMERIC)) AS group_value\\n    FROM \\\"c10\\\"\\n    GROUP BY \\\"c2\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"c2\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Used the provided template with group_col=\\\"c2\\\" and measure_col=\\\"s1\\\". CAST was added because the SQLite schema stores these fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14283,"cached_input_tokens":13696,"output_tokens":495,"reasoning_output_tokens":334}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_0a2d21dabad951e9/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"c2\", SUM(CAST(\"s1\" AS NUMERIC)) AS group_value\n    FROM \"c10\"\n    GROUP BY \"c2\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"c2\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;","notes":"Used the provided template with group_col=\"c2\" and measure_col=\"s1\". CAST was added because the SQLite schema stores these fields as TEXT."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_150e25373331ab7e/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 6770, "bytes_utf8": 6770, "lines": 230, "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": 505, "bytes_utf8": 505, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14265, "cached_input_tokens": 12032, "output_tokens": 665, "reasoning_output_tokens": 502}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_150e25373331ab7e/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": "c10",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c10_150e25373331ab7e",
+    "api_calls": 0,
+    "input_tokens": 14265,
+    "cached_input_tokens": 12032,
+    "output_tokens": 665,
+    "total_tokens": 14930,
+    "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": 14434.69,
+    "sql_execution_elapsed_ms_total": 941.36,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_150e25373331ab7e/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_150e25373331ab7e/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": "2026-05-19T15:34:24.773942+00:00",
+  "ended_at": "2026-05-19T15:34:39.208658+00:00",
+  "elapsed_ms": 14434.69,
+  "prompt_metrics": {
+    "chars": 6770,
+    "bytes_utf8": 6770,
+    "lines": 230,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 897,
+    "bytes_utf8": 897,
+    "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": 505,
+      "bytes_utf8": 505,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14265,
+      "cached_input_tokens": 12032,
+      "output_tokens": 665,
+      "reasoning_output_tokens": 502
+    }
+  },
+  "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/c10/artifacts/v2q_c10_150e25373331ab7e/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,230 @@
+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: c10
+- dataset_name: Poker Hand
+- table_name: c10
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 10 feature columns and target `class`.
+- task_type: classification
+- target_column: class
+- main_row_count: 1025010
+- important_fields:
+- s1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s1.
+- c1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c1.
+- s2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s2.
+- c2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c2.
+- s3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s3.
+- c3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c3.
+- s4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s4.
+- c4: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c4.
+- s5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for s5.
+- c5: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for c5.
+- class: role=target, type=categorical_ordinal_target. ordered=['0', '1', '2', '3', '6'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for class.
+- useful_field_combinations: [['s1', 'c1', 'class'], ['s1', 's1', 'class']]
+- fields_requiring_caution: ['class']
+- source_url: https://archive.ics.uci.edu/dataset/158/poker+hand
+
+SQLite schema snapshot:
+{
+  "table_name": "c10",
+  "quoted_table_name": "\"c10\"",
+  "row_count": 1025010,
+  "columns": [
+    {
+      "name": "s1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "s5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "c5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "s1": "1",
+      "c1": "1",
+      "s2": "1",
+      "c2": "13",
+      "s3": "2",
+      "c3": "4",
+      "s4": "2",
+      "c4": "3",
+      "s5": "1",
+      "c5": "12",
+      "class": "0"
+    },
+    {
+      "s1": "3",
+      "c1": "12",
+      "s2": "3",
+      "c2": "2",
+      "s3": "3",
+      "c3": "11",
+      "s4": "4",
+      "c4": "5",
+      "s5": "2",
+      "c5": "5",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "9",
+      "s2": "4",
+      "c2": "6",
+      "s3": "1",
+      "c3": "4",
+      "s4": "3",
+      "c4": "2",
+      "s5": "3",
+      "c5": "9",
+      "class": "1"
+    },
+    {
+      "s1": "1",
+      "c1": "4",
+      "s2": "3",
+      "c2": "13",
+      "s3": "2",
+      "c3": "13",
+      "s4": "2",
+      "c4": "1",
+      "s5": "3",
+      "c5": "6",
+      "class": "1"
+    },
+    {
+      "s1": "3",
+      "c1": "10",
+      "s2": "2",
+      "c2": "7",
+      "s3": "1",
+      "c3": "2",
+      "s4": "2",
+      "c4": "11",
+      "s5": "4",
+      "c5": "9",
+      "class": "0"
+    }
+  ]
+}
+
+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": "c10",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=c2, measure_col=c3.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "c2",
+    "measure_col": "c3",
+    "item_col": "s3",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 10.0,
+    "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/c10/artifacts/v2q_c10_150e25373331ab7e/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40df-bc1b-7211-80e6-a05e62307474"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"c2\\\", \\\"s3\\\",\\n       SUM(CAST(\\\"c3\\\" AS REAL)) AS \\\"total_measure\\\",\\n       SUM(CAST(\\\"c3\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"c3\\\" AS REAL))) OVER (PARTITION BY \\\"c2\\\") AS \\\"share_within_group\\\"\\nFROM \\\"c10\\\"\\nGROUP BY \\\"c2\\\", \\\"s3\\\"\\nORDER BY \\\"share_within_group\\\" DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"c2\\\", item_col=\\\"s3\\\", and measure_col=\\\"c3\\\". CAST is used because the schema stores these fields as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14265,"cached_input_tokens":12032,"output_tokens":665,"reasoning_output_tokens":502}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/artifacts/v2q_c10_150e25373331ab7e/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"c2\", \"s3\",\n       SUM(CAST(\"c3\" AS REAL)) AS \"total_measure\",\n       SUM(CAST(\"c3\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"c3\" AS REAL))) OVER (PARTITION BY \"c2\") AS \"share_within_group\"\nFROM \"c10\"\nGROUP BY \"c2\", \"s3\"\nORDER BY \"share_within_group\" DESC;","notes":"Applied the provided template with group_col=\"c2\", item_col=\"s3\", and measure_col=\"c3\". CAST is used because the schema stores these fields as TEXT."}