Add files using upload-large-folder tool

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_051c2b14bf40526c/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 12280, "bytes_utf8": 12280, "lines": 396, "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": 451, "bytes_utf8": 451, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16060, "cached_input_tokens": 12032, "output_tokens": 581, "reasoning_output_tokens": 452}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_051c2b14bf40526c/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": "c15",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c15_051c2b14bf40526c",
+    "api_calls": 0,
+    "input_tokens": 16060,
+    "cached_input_tokens": 12032,
+    "output_tokens": 581,
+    "total_tokens": 16641,
+    "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": 18091.98,
+    "sql_execution_elapsed_ms_total": 342.74,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_051c2b14bf40526c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_051c2b14bf40526c/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": "2026-05-19T15:29:58.294300+00:00",
+  "ended_at": "2026-05-19T15:30:16.386319+00:00",
+  "elapsed_ms": 18091.98,
+  "prompt_metrics": {
+    "chars": 12280,
+    "bytes_utf8": 12280,
+    "lines": 396,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1144,
+    "bytes_utf8": 1144,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 451,
+      "bytes_utf8": 451,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16060,
+      "cached_input_tokens": 12032,
+      "output_tokens": 581,
+      "reasoning_output_tokens": 452
+    }
+  },
+  "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_b/c15/artifacts/v2q_c15_051c2b14bf40526c/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "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": "c15",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=bin_4, measure_col=ord_0.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "bin_4",
+    "measure_col": "ord_0",
+    "top_k": 14,
+    "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": 2.5,
+    "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_b/c15/artifacts/v2q_c15_051c2b14bf40526c/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40db-ab1c-7833-8d25-dc25eef9b530"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990226, Requested 16695. Please try again in 207ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT\\n  \\\"bin_4\\\",\\n  SUM(CAST(NULLIF(\\\"ord_0\\\", '') AS REAL)) AS total_measure\\nFROM \\\"c15\\\"\\nGROUP BY \\\"bin_4\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applies the planned grouped-sum template with group_col=\\\"bin_4\\\" and measure_col=\\\"ord_0\\\". Because \\\"ord_0\\\" is stored as TEXT and may contain empty strings, NULLIF plus CAST to REAL keeps the aggregation SQLite-compatible and skips missing values.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16060,"cached_input_tokens":12032,"output_tokens":581,"reasoning_output_tokens":452}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_051c2b14bf40526c/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT\n  \"bin_4\",\n  SUM(CAST(NULLIF(\"ord_0\", '') AS REAL)) AS total_measure\nFROM \"c15\"\nGROUP BY \"bin_4\"\nORDER BY total_measure DESC;","notes":"Applies the planned grouped-sum template with group_col=\"bin_4\" and measure_col=\"ord_0\". Because \"ord_0\" is stored as TEXT and may contain empty strings, NULLIF plus CAST to REAL keeps the aggregation SQLite-compatible and skips missing values."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_17907663cfa3bb04/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 12835, "bytes_utf8": 12835, "lines": 396, "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": 693, "bytes_utf8": 693, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16196, "cached_input_tokens": 14720, "output_tokens": 573, "reasoning_output_tokens": 373}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_17907663cfa3bb04/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": "c15",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c15_17907663cfa3bb04",
+    "api_calls": 0,
+    "input_tokens": 16196,
+    "cached_input_tokens": 14720,
+    "output_tokens": 573,
+    "total_tokens": 16769,
+    "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": 18323.95,
+    "sql_execution_elapsed_ms_total": 304.85,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_17907663cfa3bb04/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_17907663cfa3bb04/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": "2026-05-19T15:50:36.933323+00:00",
+  "ended_at": "2026-05-19T15:50:55.257299+00:00",
+  "elapsed_ms": 18323.95,
+  "prompt_metrics": {
+    "chars": 12835,
+    "bytes_utf8": 12835,
+    "lines": 396,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1397,
+    "bytes_utf8": 1397,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 693,
+      "bytes_utf8": 693,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16196,
+      "cached_input_tokens": 14720,
+      "output_tokens": 573,
+      "reasoning_output_tokens": 373
+    }
+  },
+  "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_b/c15/artifacts/v2q_c15_17907663cfa3bb04/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "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": "c15",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=bin_0, measure_col=bin_1.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "bin_0",
+    "measure_col": "bin_1",
+    "top_k": 12,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.75,
+    "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_b/c15/artifacts/v2q_c15_17907663cfa3bb04/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40ee-91a9-78b1-9e2b-c88040a842f8"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1987021, Requested 16831. Please try again in 115ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold.\\nWITH grouped AS (\\n    SELECT \\\"bin_0\\\", SUM(CAST(COALESCE(NULLIF(\\\"bin_1\\\", ''), '0') AS REAL)) AS group_value\\n    FROM \\\"c15\\\"\\n    GROUP BY \\\"bin_0\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"bin_0\\\", 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 required template with group_col=\\\"bin_0\\\" and measure_col=\\\"bin_1\\\". Since \\\"bin_1\\\" is stored as TEXT and may contain empty strings, the query coerces it to REAL and treats empty strings as 0 for SQLite-compatible aggregation.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16196,"cached_input_tokens":14720,"output_tokens":573,"reasoning_output_tokens":373}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_17907663cfa3bb04/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold.\nWITH grouped AS (\n    SELECT \"bin_0\", SUM(CAST(COALESCE(NULLIF(\"bin_1\", ''), '0') AS REAL)) AS group_value\n    FROM \"c15\"\n    GROUP BY \"bin_0\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"bin_0\", 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 required template with group_col=\"bin_0\" and measure_col=\"bin_1\". Since \"bin_1\" is stored as TEXT and may contain empty strings, the query coerces it to REAL and treats empty strings as 0 for SQLite-compatible aggregation."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_2fa62883de09aff4/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,399 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "0"
+    }
+  ]
+}
+
+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": "c15",
+  "question": "Use template Grouped Condition Rate to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=ord_2, condition_col=ord_2.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "ord_2",
+    "condition_col": "ord_2",
+    "condition_value": "Cold",
+    "positive_value": "Cold",
+    "negative_value": "Boiling Hot",
+    "top_k": 12,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 2.5,
+    "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_b/c15/artifacts/v2q_c15_2fa62883de09aff4/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fc-f9f7-74c2-9cfd-19ab652d630f"}
+{"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_b/c15/artifacts/v2q_c15_476af806446f170a/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": "2026-05-19T16:12:00.651638+00:00",
+  "ended_at": "2026-05-19T16:12:03.541400+00:00",
+  "elapsed_ms": 2889.73,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 12361,
+    "bytes_utf8": 12361,
+    "lines": 396,
+    "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_b/c15/artifacts/v2q_c15_476af806446f170a/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": "2026-05-19T16:12:04.544414+00:00",
+  "ended_at": "2026-05-19T16:12:07.677329+00:00",
+  "elapsed_ms": 3132.87,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 12361,
+    "bytes_utf8": 12361,
+    "lines": 396,
+    "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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "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": "c15",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=ord_1, measure_col=ord_0.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "ord_1",
+    "measure_col": "ord_0",
+    "top_k": 12,
+    "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": 2.5,
+    "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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "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": "c15",
+  "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=ord_1, measure_col=ord_0.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "ord_1",
+    "measure_col": "ord_0",
+    "top_k": 12,
+    "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": 2.5,
+    "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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_response_attempt_1.raw.txt

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+{"type":"thread.started","thread_id":"019e4102-280e-7250-b7be-56c50dfde346"}
+{"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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_response_attempt_1.txt

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+{"type":"thread.started","thread_id":"019e4102-280e-7250-b7be-56c50dfde346"}
+{"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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4102-373a-7a80-a0b9-b85a1df5141b"}
+{"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_b/c15/artifacts/v2q_c15_476af806446f170a/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4102-373a-7a80-a0b9-b85a1df5141b"}
+{"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_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:14.852222+00:00",
+  "ended_at": "2026-05-19T16:02:18.552181+00:00",
+  "elapsed_ms": 3699.93,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 12461,
+    "bytes_utf8": 12461,
+    "lines": 396,
+    "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_b/c15/artifacts/v2q_c15_50b924e688376c59/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": "2026-05-19T16:02:19.554077+00:00",
+  "ended_at": "2026-05-19T16:02:22.732170+00:00",
+  "elapsed_ms": 3178.06,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 12461,
+    "bytes_utf8": 12461,
+    "lines": 396,
+    "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_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c15",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=bin_1, measure_col=id.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "bin_1",
+    "measure_col": "id",
+    "top_k": 16,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 502184.84,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,396 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c15",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role focused_target_view. Focus on group_col=bin_1, measure_col=id.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "bin_1",
+    "measure_col": "id",
+    "top_k": 16,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 502184.84,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-37dd-7001-b731-b6074cbdba54"}
+{"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_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-37dd-7001-b731-b6074cbdba54"}
+{"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_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-4a39-7160-a944-66859c450e22"}
+{"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_b/c15/artifacts/v2q_c15_50b924e688376c59/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-4a39-7160-a944-66859c450e22"}
+{"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_b/c15/artifacts/v2q_c15_6d70e4104bf31987/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 12685, "bytes_utf8": 12685, "lines": 398, "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": 835, "bytes_utf8": 835, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16179, "cached_input_tokens": 14720, "output_tokens": 2256, "reasoning_output_tokens": 1987}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_6d70e4104bf31987/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": "c15",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c15_6d70e4104bf31987",
+    "api_calls": 0,
+    "input_tokens": 16179,
+    "cached_input_tokens": 14720,
+    "output_tokens": 2256,
+    "total_tokens": 18435,
+    "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": 43190.35,
+    "sql_execution_elapsed_ms_total": 773.77,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_6d70e4104bf31987/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_6d70e4104bf31987/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": "2026-05-19T15:41:19.821501+00:00",
+  "ended_at": "2026-05-19T15:42:03.011882+00:00",
+  "elapsed_ms": 43190.35,
+  "prompt_metrics": {
+    "chars": 12685,
+    "bytes_utf8": 12685,
+    "lines": 398,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1278,
+    "bytes_utf8": 1278,
+    "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": 835,
+      "bytes_utf8": 835,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16179,
+      "cached_input_tokens": 14720,
+      "output_tokens": 2256,
+      "reasoning_output_tokens": 1987
+    }
+  },
+  "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_b/c15/artifacts/v2q_c15_6d70e4104bf31987/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,398 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "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": "c15",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=ord_4, measure_col=ord_0.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "ord_4",
+    "measure_col": "ord_0",
+    "item_col": "nom_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": 2.32,
+    "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_b/c15/artifacts/v2q_c15_6d70e4104bf31987/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e6-1177-7a51-a1f5-b64670ba2a0e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nWITH \\\"agg\\\" AS (\\n  SELECT\\n    \\\"ord_4\\\",\\n    \\\"nom_5\\\",\\n    SUM(CAST(NULLIF(\\\"ord_0\\\", '') AS REAL)) AS \\\"total_measure\\\"\\n  FROM \\\"c15\\\"\\n  WHERE NULLIF(\\\"ord_4\\\", '') IS NOT NULL\\n    AND NULLIF(\\\"nom_5\\\", '') IS NOT NULL\\n    AND NULLIF(\\\"ord_0\\\", '') IS NOT NULL\\n  GROUP BY \\\"ord_4\\\", \\\"nom_5\\\"\\n)\\nSELECT\\n  \\\"ord_4\\\",\\n  \\\"nom_5\\\",\\n  \\\"total_measure\\\",\\n  \\\"total_measure\\\" * 100.0 / SUM(\\\"total_measure\\\") OVER (PARTITION BY \\\"ord_4\\\") AS \\\"share_within_group\\\"\\nFROM \\\"agg\\\"\\nORDER BY \\\"share_within_group\\\" DESC\\nLIMIT 17;\",\"notes\":\"Uses the planned template with group_col=\\\"ord_4\\\", item_col=\\\"nom_5\\\", and measure_col=\\\"ord_0\\\". Since \\\"ord_0\\\" is stored as TEXT, it is cast to REAL after excluding empty strings; empty group and item values are also excluded.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16179,"cached_input_tokens":14720,"output_tokens":2256,"reasoning_output_tokens":1987}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_6d70e4104bf31987/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nWITH \"agg\" AS (\n  SELECT\n    \"ord_4\",\n    \"nom_5\",\n    SUM(CAST(NULLIF(\"ord_0\", '') AS REAL)) AS \"total_measure\"\n  FROM \"c15\"\n  WHERE NULLIF(\"ord_4\", '') IS NOT NULL\n    AND NULLIF(\"nom_5\", '') IS NOT NULL\n    AND NULLIF(\"ord_0\", '') IS NOT NULL\n  GROUP BY \"ord_4\", \"nom_5\"\n)\nSELECT\n  \"ord_4\",\n  \"nom_5\",\n  \"total_measure\",\n  \"total_measure\" * 100.0 / SUM(\"total_measure\") OVER (PARTITION BY \"ord_4\") AS \"share_within_group\"\nFROM \"agg\"\nORDER BY \"share_within_group\" DESC\nLIMIT 17;","notes":"Uses the planned template with group_col=\"ord_4\", item_col=\"nom_5\", and measure_col=\"ord_0\". Since \"ord_0\" is stored as TEXT, it is cast to REAL after excluding empty strings; empty group and item values are also excluded."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_82289009d00c5b9c/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 12224, "bytes_utf8": 12224, "lines": 394, "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": 427, "bytes_utf8": 427, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16047, "cached_input_tokens": 12032, "output_tokens": 552, "reasoning_output_tokens": 426}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_82289009d00c5b9c/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": "c15",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c15_82289009d00c5b9c",
+    "api_calls": 0,
+    "input_tokens": 16047,
+    "cached_input_tokens": 12032,
+    "output_tokens": 552,
+    "total_tokens": 16599,
+    "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": 11285.7,
+    "sql_execution_elapsed_ms_total": 146.92,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_82289009d00c5b9c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_82289009d00c5b9c/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": "2026-05-19T16:08:18.532537+00:00",
+  "ended_at": "2026-05-19T16:08:29.818269+00:00",
+  "elapsed_ms": 11285.7,
+  "prompt_metrics": {
+    "chars": 12224,
+    "bytes_utf8": 12224,
+    "lines": 394,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 775,
+    "bytes_utf8": 775,
+    "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": 427,
+      "bytes_utf8": 427,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16047,
+      "cached_input_tokens": 12032,
+      "output_tokens": 552,
+      "reasoning_output_tokens": 426
+    }
+  },
+  "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_b/c15/artifacts/v2q_c15_82289009d00c5b9c/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,394 @@
+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: c15
+- dataset_name: Cat In The Dat Ii
+- table_name: c15
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 24 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 600000
+- important_fields:
+- id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for id.
+- bin_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 0.
+- bin_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 1.
+- bin_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for bin 2.
+- bin_3: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 3.
+- bin_4: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for bin 4.
+- nom_0: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 0.
+- nom_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 1.
+- nom_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 2.
+- nom_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 3.
+- nom_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for nom 4.
+- nom_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 5.
+- nom_6: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 6.
+- nom_7: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 7.
+- nom_8: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 8.
+- nom_9: role=feature, type=categorical_nominal. tags=['condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for nom 9.
+- ord_0: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for ord 0.
+- ord_1: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 1.
+- ord_2: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 2.
+- ord_3: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 3.
+- ord_4: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 4.
+- ord_5: role=feature, type=categorical_nominal. tags=['condition_candidate', 'missingness_candidate'] desc=Categorical field for ord 5.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for day.
+- month: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'missingness_candidate'] desc=Numeric field for month.
+- target: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['bin_0', 'bin_1', 'target'], ['bin_0', 'bin_0', 'target']]
+- fields_requiring_caution: ['target', 'nom_5', 'nom_6', 'nom_7', 'bin_0', 'bin_1', 'bin_2']
+- source_url: https://www.kaggle.com/c/cat-in-the-dat-ii
+
+SQLite schema snapshot:
+{
+  "table_name": "c15",
+  "quoted_table_name": "\"c15\"",
+  "row_count": 600000,
+  "columns": [
+    {
+      "name": "id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "bin_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_6",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_7",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_8",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "nom_9",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_0",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_4",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "ord_5",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "id": "0",
+      "bin_0": "0.0",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Trapezoid",
+      "nom_2": "Hamster",
+      "nom_3": "Russia",
+      "nom_4": "Bassoon",
+      "nom_5": "de4c57ee2",
+      "nom_6": "a64bc7ddf",
+      "nom_7": "598080a91",
+      "nom_8": "0256c7a4b",
+      "nom_9": "02e7c8990",
+      "ord_0": "3.0",
+      "ord_1": "Contributor",
+      "ord_2": "Hot",
+      "ord_3": "c",
+      "ord_4": "U",
+      "ord_5": "Pw",
+      "day": "6.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "1",
+      "bin_0": "1.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "Y",
+      "nom_0": "Red",
+      "nom_1": "Star",
+      "nom_2": "Axolotl",
+      "nom_3": "",
+      "nom_4": "Theremin",
+      "nom_5": "2bb3c3e5c",
+      "nom_6": "3a3a936e8",
+      "nom_7": "1dddb8473",
+      "nom_8": "52ead350c",
+      "nom_9": "f37df64af",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Warm",
+      "ord_3": "e",
+      "ord_4": "X",
+      "ord_5": "pE",
+      "day": "7.0",
+      "month": "7.0",
+      "target": "0"
+    },
+    {
+      "id": "2",
+      "bin_0": "0.0",
+      "bin_1": "1.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "",
+      "nom_2": "Hamster",
+      "nom_3": "Canada",
+      "nom_4": "Bassoon",
+      "nom_5": "b574c9841",
+      "nom_6": "708248125",
+      "nom_7": "5ddc9a726",
+      "nom_8": "745b909d1",
+      "nom_9": "",
+      "ord_0": "3.0",
+      "ord_1": "",
+      "ord_2": "Freezing",
+      "ord_3": "n",
+      "ord_4": "P",
+      "ord_5": "eN",
+      "day": "5.0",
+      "month": "9.0",
+      "target": "0"
+    },
+    {
+      "id": "3",
+      "bin_0": "",
+      "bin_1": "0.0",
+      "bin_2": "0.0",
+      "bin_3": "F",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Circle",
+      "nom_2": "Hamster",
+      "nom_3": "Finland",
+      "nom_4": "Theremin",
+      "nom_5": "673bdf1f6",
+      "nom_6": "23edb8da3",
+      "nom_7": "3a33ef960",
+      "nom_8": "bdaa56dd1",
+      "nom_9": "f9d456e57",
+      "ord_0": "1.0",
+      "ord_1": "Novice",
+      "ord_2": "Lava Hot",
+      "ord_3": "a",
+      "ord_4": "C",
+      "ord_5": "",
+      "day": "3.0",
+      "month": "3.0",
+      "target": "0"
+    },
+    {
+      "id": "4",
+      "bin_0": "0.0",
+      "bin_1": "",
+      "bin_2": "0.0",
+      "bin_3": "T",
+      "bin_4": "N",
+      "nom_0": "Red",
+      "nom_1": "Triangle",
+      "nom_2": "Hamster",
+      "nom_3": "Costa Rica",
+      "nom_4": "",
+      "nom_5": "777d1ac2c",
+      "nom_6": "3a7975e46",
+      "nom_7": "bc9cc2a94",
+      "nom_8": "",
+      "nom_9": "c5361037c",
+      "ord_0": "3.0",
+      "ord_1": "Grandmaster",
+      "ord_2": "Cold",
+      "ord_3": "h",
+      "ord_4": "C",
+      "ord_5": "OZ",
+      "day": "5.0",
+      "month": "12.0",
+      "target": "0"
+    }
+  ]
+}
+
+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": "c15",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=bin_1.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "bin_1",
+    "top_k": 14,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.75,
+    "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_b/c15/artifacts/v2q_c15_82289009d00c5b9c/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-c46b-7d22-83b0-adf4aa735c58"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_threshold_rarity_cdf\\nSELECT AVG(CASE WHEN NULLIF(\\\"bin_1\\\", '') IS NULL THEN NULL WHEN CAST(NULLIF(\\\"bin_1\\\", '') AS REAL) <= 0.75 THEN 1 ELSE 0 END) AS \\\"empirical_cdf_at_threshold\\\"\\nFROM \\\"c15\\\";\",\"notes\":\"Applied the Threshold Rarity CDF template with measure_col bound to \\\"bin_1\\\" and threshold 0.75. Used NULLIF and CAST to treat blank strings as missing and compare numerically in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16047,"cached_input_tokens":12032,"output_tokens":552,"reasoning_output_tokens":426}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c15/artifacts/v2q_c15_82289009d00c5b9c/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_threshold_rarity_cdf\nSELECT AVG(CASE WHEN NULLIF(\"bin_1\", '') IS NULL THEN NULL WHEN CAST(NULLIF(\"bin_1\", '') AS REAL) <= 0.75 THEN 1 ELSE 0 END) AS \"empirical_cdf_at_threshold\"\nFROM \"c15\";","notes":"Applied the Threshold Rarity CDF template with measure_col bound to \"bin_1\" and threshold 0.75. Used NULLIF and CAST to treat blank strings as missing and compare numerically in SQLite."}