Upload Query/sql/v3 runs batch c

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/conversation.jsonl

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+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9209, "bytes_utf8": 9209, "lines": 303, "estimated_tokens": 2572}}
+{"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": 419, "bytes_utf8": 419, "lines": 1, "estimated_tokens": 103}, "usage": {"input_tokens": 22663, "cached_input_tokens": 6528, "output_tokens": 119}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/session_summary.json

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+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_00e4d15956c3f470",
+    "api_calls": 0,
+    "input_tokens": 22663,
+    "cached_input_tokens": 6528,
+    "output_tokens": 119,
+    "total_tokens": 22782,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2572,
+    "estimated_output_tokens": 103,
+    "estimated_total_tokens": 2675,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12273.46,
+    "sql_execution_elapsed_ms_total": 41.0,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_00e4d15956c3f470\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/sql_attempt_1.metadata.json

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+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-04T08:24:51.447923+00:00",
+  "ended_at": "2026-05-04T08:25:03.721759+00:00",
+  "elapsed_ms": 12273.46,
+  "prompt_metrics": {
+    "chars": 9209,
+    "bytes_utf8": 9209,
+    "lines": 303,
+    "estimated_tokens": 2572
+  },
+  "stdout_metrics": {
+    "chars": 743,
+    "bytes_utf8": 743,
+    "lines": 4,
+    "estimated_tokens": 204
+  },
+  "stderr_metrics": {
+    "chars": 23206,
+    "bytes_utf8": 23206,
+    "lines": 57,
+    "estimated_tokens": 13245
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 419,
+      "bytes_utf8": 419,
+      "lines": 1,
+      "estimated_tokens": 103
+    },
+    "usage": {
+      "input_tokens": 22663,
+      "cached_input_tokens": 6528,
+      "output_tokens": 119
+    }
+  },
+  "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/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/sql_prompt_attempt_1.txt

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+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: m8
+- dataset_name: Bank Marketing
+- table_name: m8
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
+- task_type: classification
+- target_column: y
+- main_row_count: 45211
+- important_fields:
+- age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
+- job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
+- marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
+- education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
+- default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
+- balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
+- housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
+- loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
+- contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
+- month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
+- duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
+- campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
+- pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
+- previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
+- poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
+- y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
+- useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
+- fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
+- source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
+
+SQLite schema snapshot:
+{
+  "table_name": "m8",
+  "quoted_table_name": "\"m8\"",
+  "row_count": 45211,
+  "columns": [
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "marital",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "default",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "housing",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "loan",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "contact",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "campaign",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "pdays",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "previous",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "poutcome",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "y",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "age": "58",
+      "job": "management",
+      "marital": "married",
+      "education": "tertiary",
+      "default": "no",
+      "balance": "2143",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "261",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "44",
+      "job": "technician",
+      "marital": "single",
+      "education": "secondary",
+      "default": "no",
+      "balance": "29",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "151",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "entrepreneur",
+      "marital": "married",
+      "education": "secondary",
+      "default": "no",
+      "balance": "2",
+      "housing": "yes",
+      "loan": "yes",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "76",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "47",
+      "job": "blue-collar",
+      "marital": "married",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1506",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "92",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "unknown",
+      "marital": "single",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1",
+      "housing": "no",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "198",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    }
+  ]
+}
+
+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": "m8",
+  "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=day, condition_col=contact.",
+  "planned_template_id": "tpl_m4_group_condition_rate",
+  "bindings": {
+    "group_col": "day",
+    "condition_col": "contact",
+    "condition_value": "unknown",
+    "positive_value": "cellular",
+    "negative_value": "unknown",
+    "top_k": 18,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 3.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;"
+}
+
+Repair context:
+{}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/sql_response_attempt_1.raw.txt

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+{"type":"thread.started","thread_id":"019df217-1284-76a2-9185-9661410dddac"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"day\\\",\\n       AVG(CASE WHEN \\\"contact\\\" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m8\\\"\\nGROUP BY \\\"day\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the requested Grouped Condition Rate template with group_col bound to \\\"day\\\" and condition_col bound to \\\"contact\\\", measuring the within-day proportion of rows where contact = 'unknown'.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":22663,"cached_input_tokens":6528,"output_tokens":119}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/sql_response_attempt_1.txt

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+{"sql":"-- template_id: tpl_m4_group_condition_rate\nSELECT \"day\",\n       AVG(CASE WHEN \"contact\" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m8\"\nGROUP BY \"day\"\nORDER BY condition_rate DESC;","notes":"Uses the requested Grouped Condition Rate template with group_col bound to \"day\" and condition_col bound to \"contact\", measuring the within-day proportion of rows where contact = 'unknown'."}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/cli/sql_stderr_attempt_1.txt

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+2026-05-04T08:24:51.667822Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell
+2026-05-04T08:24:54.230138Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <style global>body{font-family:Arial,Helvetica,sans-serif}.container{align-items:center;display:flex;flex-direction:column;gap:2rem;height:100%;justify-content:center;width:100%}@keyframes enlarge-appear{0%{opacity:0;transform:scale(75%) rotate(-90deg)}to{opacity:1;transform:scale(100%) rotate(0deg)}}.logo{color:#8e8ea0}.scale-appear{animation:enlarge-appear .4s ease-out}@media (min-width:768px){.scale-appear{height:48px;width:48px}}.data:empty{display:none}.data{border-radius:5px;color:#8e8ea0;text-align:center}@media (prefers-color-scheme:dark){body{background-color:#343541}.logo{color:#acacbe}}</style>
+  <meta http-equiv="refresh" content="360"></head>
+  <body>
+    <div class="container">
+      <div class="logo">
+        <svg
+          width="41"
+          height="41"
+          viewBox="0 0 41 41"
+          fill="none"
+          xmlns="http://www.w3.org/2000/svg"
+          strokeWidth="2"
+          class="scale-appear"
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+

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=day, condition_col=contact.
+Result preview: [{"day": "27", "condition_rate": 0.4977698483496878}, {"day": "23", "condition_rate": 0.4952076677316294}, {"day": "9", "condition_rate": 0.4951953875720692}, {"day": "16", "condition_rate": 0.4812720848056537}, {"day": "26", "condition_rate": 0.47439613526570046}]

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v3
+-- sql_source_label: v3_current
+-- sql_source_run_id: v3_cli_20260504_071000_c
+-- sql_source_dataset_id: m8
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_m4_group_condition_rate
+-- query_record_id: v2q_m8_00e4d15956c3f470
+-- problem_id: v2p_m8_e76782afe7ce1afa
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "day",
+       AVG(CASE WHEN "contact" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate
+FROM "m8"
+GROUP BY "day"
+ORDER BY condition_rate DESC;

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_group_condition_rate\nSELECT \"day\",\n       AVG(CASE WHEN \"contact\" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate\nFROM \"m8\"\nGROUP BY \"day\"\nORDER BY condition_rate DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"day\\\",\\n       AVG(CASE WHEN \\\"contact\\\" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m8\\\"\\nGROUP BY \\\"day\\\"\\nORDER BY condition_rate DESC;\", \"columns\": [\"day\", \"condition_rate\"], \"rows\": [{\"day\": \"27\", \"condition_rate\": 0.4977698483496878}, {\"day\": \"23\", \"condition_rate\": 0.4952076677316294}, {\"day\": \"9\", \"condition_rate\": 0.4951953875720692}, {\"day\": \"16\", \"condition_rate\": 0.4812720848056537}, {\"day\": \"26\", \"condition_rate\": 0.47439613526570046}, {\"day\": \"6\", \"condition_rate\": 0.422360248447205}, {\"day\": \"3\", \"condition_rate\": 0.3994439295644115}, {\"day\": \"5\", \"condition_rate\": 0.38272251308900523}, {\"day\": \"20\", \"condition_rate\": 0.36664244186046513}, {\"day\": \"19\", \"condition_rate\": 0.3597040409789414}, {\"day\": \"2\", \"condition_rate\": 0.32405259087393656}, {\"day\": \"30\", \"condition_rate\": 0.3103448275862069}, {\"day\": \"12\", \"condition_rate\": 0.3006862133499688}, {\"day\": \"15\", \"condition_rate\": 0.2859659424544921}, {\"day\": \"28\", \"condition_rate\": 0.2825136612021858}, {\"day\": \"13\", \"condition_rate\": 0.27003154574132493}, {\"day\": \"29\", \"condition_rate\": 0.25673352435530084}, {\"day\": \"8\", \"condition_rate\": 0.250814332247557}, {\"day\": \"14\", \"condition_rate\": 0.24567099567099568}, {\"day\": \"4\", \"condition_rate\": 0.23252595155709344}, {\"day\": \"1\", \"condition_rate\": 0.2236024844720497}, {\"day\": \"21\", \"condition_rate\": 0.22211253701875616}, {\"day\": \"7\", \"condition_rate\": 0.22069345074298294}, {\"day\": \"17\", \"condition_rate\": 0.18824136152656007}, {\"day\": \"11\", \"condition_rate\": 0.16091954022988506}, {\"day\": \"18\", \"condition_rate\": 0.12738301559792028}, {\"day\": \"24\", \"condition_rate\": 0.07158836689038031}, {\"day\": \"25\", \"condition_rate\": 0.04285714285714286}, {\"day\": \"10\", \"condition_rate\": 0.015267175572519083}, {\"day\": \"22\", \"condition_rate\": 0.013259668508287293}, {\"day\": \"31\", \"condition_rate\": 0.00933125972006221}], \"row_count_returned\": 31, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 41.0}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/run_manifest.json

@@ -0,0 +1,92 @@
+{
+  "run_id": "v3_cli_20260504_071000_c",
+  "dataset_id": "m8",
+  "started_at": "2026-05-04T08:24:51.438051+00:00",
+  "ended_at": "2026-05-04T08:25:03.798323+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m8_00e4d15956c3f470",
+    "problem_id": "v2p_m8_e76782afe7ce1afa",
+    "dataset_id": "m8",
+    "template_id": "tpl_m4_group_condition_rate",
+    "template_name": "Grouped Condition Rate",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Condition Rate to probe direction_consistency with semantic role within_group_proportion. Focus on group_col=day, condition_col=contact.",
+    "bindings": {
+      "group_col": "day",
+      "condition_col": "contact",
+      "condition_value": "unknown",
+      "positive_value": "cellular",
+      "negative_value": "unknown",
+      "top_k": 18,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 3.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "condition_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       AVG(CASE WHEN {condition_col} = {condition_value} THEN 1 ELSE 0 END) AS condition_rate\nFROM {table}\nGROUP BY {group_col}\nORDER BY condition_rate DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=cli",
+      "problem_index_within_template=6",
+      "sql_variant_index=2/2",
+      "binding_index=53"
+    ],
+    "template_selection_mode": "cli",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v3",
+  "sql_source_version": "v3",
+  "sql_source_label": "v3_current",
+  "generated_sql_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\sql\\v2q_m8_00e4d15956c3f470.sql",
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_00e4d15956c3f470",
+    "api_calls": 0,
+    "input_tokens": 22663,
+    "cached_input_tokens": 6528,
+    "output_tokens": 119,
+    "total_tokens": 22782,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2572,
+    "estimated_output_tokens": 103,
+    "estimated_total_tokens": 2675,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12273.46,
+    "sql_execution_elapsed_ms_total": 41.0,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_00e4d15956c3f470\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-04T08:25:03.747949+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 12273.46, "started_at": "2026-05-04T08:24:51.447923+00:00", "ended_at": "2026-05-04T08:25:03.721759+00:00", "prompt_metrics": {"chars": 9209, "bytes_utf8": 9209, "lines": 303, "estimated_tokens": 2572}, "response_metrics": {"chars": 419, "bytes_utf8": 419, "lines": 1, "estimated_tokens": 103}, "usage": {"input_tokens": 22663, "cached_input_tokens": 6528, "output_tokens": 119}, "stderr_preview": "2026-05-04T08:24:51.667822Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell\n2026-05-04T08:24:54.230138Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>\n  <head>\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n    <style global>body{font-family:Arial,Helvetica,sans-serif}.container{align-items:center;display:flex;flex-direction:column;gap:2rem;height:100%;justify-content:center;width:100%}@keyframes enlarge-appear{0%{opacity:0;transform:scale(75%) rotate(-90deg)}to{opacity:1;transform:scale(100%) rotate(0deg)}}.logo{color:#8e8ea0}.scale-appear{animation:enlarge-appear .4s ease-out}@media (min-width:768px){.scale-appear{height:48px;width:48px}}.data:empty{display:none}.data{border-radius:5px;color:#8e8ea0;text-align:center}@media (prefers-color-scheme:dark){body{background-color:#343541}.logo{color:#acacbe}}</style>\n  <meta http-equiv=\"refresh\" conte", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_group_condition_rate\\nSELECT \\\"day\\\",\\n       AVG(CASE WHEN \\\"contact\\\" = 'unknown' THEN 1 ELSE 0 END) AS condition_rate\\nFROM \\\"m8\\\"\\nGROUP BY \\\"day\\\"\\nORDER BY condition_rate DESC;\",\"notes\":\"Uses the requested Grouped Condition Rate template with group_col bound to \\\"day\\\" and condition_col bound to \\\"contact\\\", measuring the within-day proportion of rows where contact = 'unknown'.\"}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_00e4d15956c3f470/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m8",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m8_00e4d15956c3f470",
+  "api_calls": 0,
+  "input_tokens": 22663,
+  "cached_input_tokens": 6528,
+  "output_tokens": 119,
+  "total_tokens": 22782,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 2572,
+  "estimated_output_tokens": 103,
+  "estimated_total_tokens": 2675,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 12273.46,
+  "sql_execution_elapsed_ms_total": 41.0,
+  "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_00e4d15956c3f470\\cli\\conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9360, "bytes_utf8": 9360, "lines": 302, "estimated_tokens": 2628}}
+{"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": 533, "bytes_utf8": 533, "lines": 1, "estimated_tokens": 146}, "usage": {"input_tokens": 22719, "cached_input_tokens": 19840, "output_tokens": 189}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_01204003e818f9c3",
+    "api_calls": 0,
+    "input_tokens": 22719,
+    "cached_input_tokens": 19840,
+    "output_tokens": 189,
+    "total_tokens": 22908,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2628,
+    "estimated_output_tokens": 146,
+    "estimated_total_tokens": 2774,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12285.48,
+    "sql_execution_elapsed_ms_total": 54.62,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_01204003e818f9c3\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,44 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-04T08:32:42.514578+00:00",
+  "ended_at": "2026-05-04T08:32:54.800114+00:00",
+  "elapsed_ms": 12285.48,
+  "prompt_metrics": {
+    "chars": 9360,
+    "bytes_utf8": 9360,
+    "lines": 302,
+    "estimated_tokens": 2628
+  },
+  "stdout_metrics": {
+    "chars": 887,
+    "bytes_utf8": 887,
+    "lines": 4,
+    "estimated_tokens": 271
+  },
+  "stderr_metrics": {
+    "chars": 23206,
+    "bytes_utf8": 23206,
+    "lines": 57,
+    "estimated_tokens": 13193
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 533,
+      "bytes_utf8": 533,
+      "lines": 1,
+      "estimated_tokens": 146
+    },
+    "usage": {
+      "input_tokens": 22719,
+      "cached_input_tokens": 19840,
+      "output_tokens": 189
+    }
+  },
+  "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/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,302 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m8
+- dataset_name: Bank Marketing
+- table_name: m8
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
+- task_type: classification
+- target_column: y
+- main_row_count: 45211
+- important_fields:
+- age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
+- job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
+- marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
+- education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
+- default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
+- balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
+- housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
+- loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
+- contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
+- month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
+- duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
+- campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
+- pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
+- previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
+- poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
+- y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
+- useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
+- fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
+- source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
+
+SQLite schema snapshot:
+{
+  "table_name": "m8",
+  "quoted_table_name": "\"m8\"",
+  "row_count": 45211,
+  "columns": [
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "marital",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "default",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "housing",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "loan",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "contact",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "campaign",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "pdays",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "previous",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "poutcome",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "y",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "age": "58",
+      "job": "management",
+      "marital": "married",
+      "education": "tertiary",
+      "default": "no",
+      "balance": "2143",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "261",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "44",
+      "job": "technician",
+      "marital": "single",
+      "education": "secondary",
+      "default": "no",
+      "balance": "29",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "151",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "entrepreneur",
+      "marital": "married",
+      "education": "secondary",
+      "default": "no",
+      "balance": "2",
+      "housing": "yes",
+      "loan": "yes",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "76",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "47",
+      "job": "blue-collar",
+      "marital": "married",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1506",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "92",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "unknown",
+      "marital": "single",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1",
+      "housing": "no",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "198",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    }
+  ]
+}
+
+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": "m8",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=loan, measure_col=campaign.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "loan",
+    "measure_col": "campaign",
+    "item_col": "pdays",
+    "top_k": 16,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 2.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019df21e-42a3-7703-809b-2594d2e02438"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"loan\\\", \\\"pdays\\\",\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"campaign\\\" AS REAL))) OVER (PARTITION BY \\\"loan\\\") AS share_within_group\\nFROM \\\"m8\\\"\\nGROUP BY \\\"loan\\\", \\\"pdays\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the provided template with group_col=\\\"loan\\\", item_col=\\\"pdays\\\", and measure_col=\\\"campaign\\\". Cast \\\"campaign\\\" to REAL because the schema stores it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":22719,"cached_input_tokens":19840,"output_tokens":189}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"loan\", \"pdays\",\n       SUM(CAST(\"campaign\" AS REAL)) AS total_measure,\n       SUM(CAST(\"campaign\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"campaign\" AS REAL))) OVER (PARTITION BY \"loan\") AS share_within_group\nFROM \"m8\"\nGROUP BY \"loan\", \"pdays\"\nORDER BY share_within_group DESC;","notes":"Used the provided template with group_col=\"loan\", item_col=\"pdays\", and measure_col=\"campaign\". Cast \"campaign\" to REAL because the schema stores it as TEXT."}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/cli/sql_stderr_attempt_1.txt

@@ -0,0 +1,57 @@
+2026-05-04T08:32:42.719373Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell
+2026-05-04T08:32:45.171579Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>
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+

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=loan, measure_col=campaign.
+Result preview: [{"loan": "yes", "pdays": "-1", "total_measure": 18192.0, "share_within_group": 88.59452615174833}, {"loan": "no", "pdays": "-1", "total_measure": 89786.0, "share_within_group": 85.98379651797514}, {"loan": "no", "pdays": "182", "total_measure": 260.0, "share_within_group": 0.24898967650495107}, {"loan": "yes", "pdays": "182", "total_measure": 47.0, "share_within_group": 0.22888867244569983}, {"loan": "no", "pdays": "92", "total_measure": 213.0, "share_within_group": 0.20398000421367146}] Results were truncated.

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v3
+-- sql_source_label: v3_current
+-- sql_source_run_id: v3_cli_20260504_071000_c
+-- sql_source_dataset_id: m8
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m8_01204003e818f9c3
+-- problem_id: v2p_m8_af9c4d1fb8bc7f0a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "loan", "pdays",
+       SUM(CAST("campaign" AS REAL)) AS total_measure,
+       SUM(CAST("campaign" AS REAL)) * 100.0 / SUM(SUM(CAST("campaign" AS REAL))) OVER (PARTITION BY "loan") AS share_within_group
+FROM "m8"
+GROUP BY "loan", "pdays"
+ORDER BY share_within_group DESC;

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpcds_within_group_share\nSELECT \"loan\", \"pdays\",\n       SUM(CAST(\"campaign\" AS REAL)) AS total_measure,\n       SUM(CAST(\"campaign\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"campaign\" AS REAL))) OVER (PARTITION BY \"loan\") AS share_within_group\nFROM \"m8\"\nGROUP BY \"loan\", \"pdays\"\nORDER BY share_within_group DESC;", "result": "{\"query\": \"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"loan\\\", \\\"pdays\\\",\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"campaign\\\" AS REAL))) OVER (PARTITION BY \\\"loan\\\") AS share_within_group\\nFROM \\\"m8\\\"\\nGROUP BY \\\"loan\\\", \\\"pdays\\\"\\nORDER BY share_within_group DESC;\", \"columns\": [\"loan\", \"pdays\", \"total_measure\", \"share_within_group\"], \"rows\": [{\"loan\": \"yes\", \"pdays\": \"-1\", \"total_measure\": 18192.0, \"share_within_group\": 88.59452615174833}, {\"loan\": \"no\", \"pdays\": \"-1\", \"total_measure\": 89786.0, \"share_within_group\": 85.98379651797514}, {\"loan\": \"no\", \"pdays\": \"182\", \"total_measure\": 260.0, \"share_within_group\": 0.24898967650495107}, {\"loan\": \"yes\", \"pdays\": \"182\", \"total_measure\": 47.0, \"share_within_group\": 0.22888867244569983}, {\"loan\": \"no\", \"pdays\": \"92\", \"total_measure\": 213.0, \"share_within_group\": 0.20398000421367146}, {\"loan\": \"no\", \"pdays\": \"183\", \"total_measure\": 201.0, \"share_within_group\": 0.19248817299036602}, {\"loan\": \"no\", \"pdays\": \"181\", \"total_measure\": 198.0, \"share_within_group\": 0.18961521518453966}, {\"loan\": \"yes\", \"pdays\": \"350\", \"total_measure\": 38.0, \"share_within_group\": 0.18505892665822538}, {\"loan\": \"no\", \"pdays\": \"91\", \"total_measure\": 184.0, \"share_within_group\": 0.17620807875734998}, {\"loan\": \"yes\", \"pdays\": \"343\", \"total_measure\": 34.0, \"share_within_group\": 0.16557903964157009}, {\"loan\": \"no\", \"pdays\": \"175\", \"total_measure\": 169.0, \"share_within_group\": 0.16184328972821818}, {\"loan\": \"no\", \"pdays\": \"185\", \"total_measure\": 151.0, \"share_within_group\": 0.14460554289326005}, {\"loan\": \"no\", \"pdays\": \"370\", \"total_measure\": 148.0, \"share_within_group\": 0.1417325850874337}, {\"loan\": \"yes\", \"pdays\": \"196\", \"total_measure\": 29.0, \"share_within_group\": 0.14122918087075095}, {\"loan\": \"no\", \"pdays\": \"94\", \"total_measure\": 146.0, \"share_within_group\": 0.13981727988354944}, {\"loan\": \"no\", \"pdays\": \"95\", \"total_measure\": 143.0, \"share_within_group\": 0.13694432207772309}, {\"loan\": \"yes\", \"pdays\": \"167\", \"total_measure\": 28.0, \"share_within_group\": 0.1363592091165871}, {\"loan\": \"no\", \"pdays\": \"364\", \"total_measure\": 139.0, \"share_within_group\": 0.13311371166995462}, {\"loan\": \"yes\", \"pdays\": \"349\", \"total_measure\": 27.0, \"share_within_group\": 0.1314892373624233}, {\"loan\": \"no\", \"pdays\": \"184\", \"total_measure\": 136.0, \"share_within_group\": 0.13024075386412826}, {\"loan\": \"no\", \"pdays\": \"350\", \"total_measure\": 131.0, \"share_within_group\": 0.12545249085441765}, {\"loan\": \"yes\", \"pdays\": \"113\", \"total_measure\": 25.0, \"share_within_group\": 0.12174929385409565}, {\"loan\": \"yes\", \"pdays\": \"188\", \"total_measure\": 25.0, \"share_within_group\": 0.12174929385409565}, {\"loan\": \"no\", \"pdays\": \"93\", \"total_measure\": 127.0, \"share_within_group\": 0.12162188044664918}, {\"loan\": \"no\", \"pdays\": \"349\", \"total_measure\": 124.0, \"share_within_group\": 0.11874892264082282}, {\"loan\": \"no\", \"pdays\": \"365\", \"total_measure\": 124.0, \"share_within_group\": 0.11874892264082282}, {\"loan\": \"yes\", \"pdays\": \"172\", \"total_measure\": 24.0, \"share_within_group\": 0.11687932209993182}, {\"loan\": \"no\", \"pdays\": \"371\", \"total_measure\": 119.0, \"share_within_group\": 0.11396065963111222}, {\"loan\": \"no\", \"pdays\": \"174\", \"total_measure\": 117.0, \"share_within_group\": 0.11204535442722798}, {\"loan\": \"yes\", \"pdays\": \"150\", \"total_measure\": 23.0, \"share_within_group\": 0.11200935034576799}, {\"loan\": \"yes\", \"pdays\": \"352\", \"total_measure\": 23.0, \"share_within_group\": 0.11200935034576799}, {\"loan\": \"no\", \"pdays\": \"186\", \"total_measure\": 116.0, \"share_within_group\": 0.11108770182528586}, {\"loan\": \"no\", \"pdays\": \"189\", \"total_measure\": 116.0, \"share_within_group\": 0.11108770182528586}, {\"loan\": \"yes\", \"pdays\": \"183\", \"total_measure\": 22.0, \"share_within_group\": 0.10713937859160416}, {\"loan\": \"yes\", \"pdays\": \"190\", \"total_measure\": 22.0, \"share_within_group\": 0.10713937859160416}, {\"loan\": \"no\", \"pdays\": \"367\", \"total_measure\": 109.0, \"share_within_group\": 0.10438413361169102}, {\"loan\": \"no\", \"pdays\": \"97\", \"total_measure\": 108.0, \"share_within_group\": 0.1034264810097489}, {\"loan\": \"yes\", \"pdays\": \"252\", \"total_measure\": 21.0, \"share_within_group\": 0.10226940683744035}, {\"loan\": \"no\", \"pdays\": \"178\", \"total_measure\": 106.0, \"share_within_group\": 0.10151117580586466}, {\"loan\": \"no\", \"pdays\": \"188\", \"total_measure\": 106.0, \"share_within_group\": 0.10151117580586466}, {\"loan\": \"no\", \"pdays\": \"360\", \"total_measure\": 106.0, \"share_within_group\": 0.10151117580586466}, {\"loan\": \"no\", \"pdays\": \"342\", \"total_measure\": 103.0, \"share_within_group\": 0.0986382180000383}, {\"loan\": \"yes\", \"pdays\": \"351\", \"total_measure\": 20.0, \"share_within_group\": 0.09739943508327652}, {\"loan\": \"yes\", \"pdays\": \"363\", \"total_measure\": 20.0, \"share_within_group\": 0.09739943508327652}, {\"loan\": \"no\", \"pdays\": \"179\", \"total_measure\": 98.0, \"share_within_group\": 0.09384995499032771}, {\"loan\": \"no\", \"pdays\": \"357\", \"total_measure\": 98.0, \"share_within_group\": 0.09384995499032771}, {\"loan\": \"no\", \"pdays\": \"363\", \"total_measure\": 97.0, \"share_within_group\": 0.09289230238838558}, {\"loan\": \"no\", \"pdays\": \"90\", \"total_measure\": 97.0, \"share_within_group\": 0.09289230238838558}, {\"loan\": \"yes\", \"pdays\": \"353\", \"total_measure\": 19.0, \"share_within_group\": 0.09252946332911269}, {\"loan\": \"yes\", \"pdays\": \"7\", \"total_measure\": 19.0, \"share_within_group\": 0.09252946332911269}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 54.62}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/run_manifest.json

@@ -0,0 +1,91 @@
+{
+  "run_id": "v3_cli_20260504_071000_c",
+  "dataset_id": "m8",
+  "started_at": "2026-05-04T08:32:42.499034+00:00",
+  "ended_at": "2026-05-04T08:32:54.881960+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m8_01204003e818f9c3",
+    "problem_id": "v2p_m8_af9c4d1fb8bc7f0a",
+    "dataset_id": "m8",
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "dependency_strength_similarity",
+    "intended_facet_id": "pairwise_conditional_dependency",
+    "variant_semantic_role": "within_group_proportion",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=loan, measure_col=campaign.",
+    "bindings": {
+      "group_col": "loan",
+      "measure_col": "campaign",
+      "item_col": "pdays",
+      "top_k": 16,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.05,
+      "baseline_multiplier": 1.75,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 4,
+      "measure_threshold": 2.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "notes": [
+      "default_facets=pairwise_conditional_dependency",
+      "template_selection_mode=cli",
+      "problem_index_within_template=10",
+      "sql_variant_index=2/2",
+      "binding_index=81"
+    ],
+    "template_selection_mode": "cli",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 10,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v3",
+  "sql_source_version": "v3",
+  "sql_source_label": "v3_current",
+  "generated_sql_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\sql\\v2q_m8_01204003e818f9c3.sql",
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_01204003e818f9c3",
+    "api_calls": 0,
+    "input_tokens": 22719,
+    "cached_input_tokens": 19840,
+    "output_tokens": 189,
+    "total_tokens": 22908,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2628,
+    "estimated_output_tokens": 146,
+    "estimated_total_tokens": 2774,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 12285.48,
+    "sql_execution_elapsed_ms_total": 54.62,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_01204003e818f9c3\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-04T08:32:54.818186+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 12285.48, "started_at": "2026-05-04T08:32:42.514578+00:00", "ended_at": "2026-05-04T08:32:54.800114+00:00", "prompt_metrics": {"chars": 9360, "bytes_utf8": 9360, "lines": 302, "estimated_tokens": 2628}, "response_metrics": {"chars": 533, "bytes_utf8": 533, "lines": 1, "estimated_tokens": 146}, "usage": {"input_tokens": 22719, "cached_input_tokens": 19840, "output_tokens": 189}, "stderr_preview": "2026-05-04T08:32:42.719373Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell\n2026-05-04T08:32:45.171579Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>\n  <head>\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n    <style global>body{font-family:Arial,Helvetica,sans-serif}.container{align-items:center;display:flex;flex-direction:column;gap:2rem;height:100%;justify-content:center;width:100%}@keyframes enlarge-appear{0%{opacity:0;transform:scale(75%) rotate(-90deg)}to{opacity:1;transform:scale(100%) rotate(0deg)}}.logo{color:#8e8ea0}.scale-appear{animation:enlarge-appear .4s ease-out}@media (min-width:768px){.scale-appear{height:48px;width:48px}}.data:empty{display:none}.data{border-radius:5px;color:#8e8ea0;text-align:center}@media (prefers-color-scheme:dark){body{background-color:#343541}.logo{color:#acacbe}}</style>\n  <meta http-equiv=\"refresh\" conte", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"loan\\\", \\\"pdays\\\",\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"campaign\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"campaign\\\" AS REAL))) OVER (PARTITION BY \\\"loan\\\") AS share_within_group\\nFROM \\\"m8\\\"\\nGROUP BY \\\"loan\\\", \\\"pdays\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Used the provided template with group_col=\\\"loan\\\", item_col=\\\"pdays\\\", and measure_col=\\\"campaign\\\". Cast \\\"campaign\\\" to REAL because the schema stores it as TEXT.\"}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_01204003e818f9c3/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m8",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m8_01204003e818f9c3",
+  "api_calls": 0,
+  "input_tokens": 22719,
+  "cached_input_tokens": 19840,
+  "output_tokens": 189,
+  "total_tokens": 22908,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 2628,
+  "estimated_output_tokens": 146,
+  "estimated_total_tokens": 2774,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 12285.48,
+  "sql_execution_elapsed_ms_total": 54.62,
+  "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_01204003e818f9c3\\cli\\conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_032e290a496b0343/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"campaign": "1", "support": 17544, "avg_response": 40.66478568171455}, {"campaign": "2", "support": 12505, "avg_response": 40.93362654938025}, {"campaign": "3", "support": 5521, "avg_response": 40.936424560767975}, {"campaign": "4", "support": 3522, "avg_response": 41.59965928449744}, {"campaign": "5", "support": 1764, "avg_response": 41.438208616780045}]}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_032e290a496b0343/generated_sql.sql

@@ -0,0 +1,21 @@
+-- sql_source_version: v3
+-- sql_source_label: v3_current
+-- sql_source_run_id: v3_cli_20260504_071000_c
+-- sql_source_dataset_id: m8
+-- family_id: cardinality_structure
+-- canonical_subitem_id: high_cardinality_response_stability
+-- intended_facet_id: target_cardinality_cross_section
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_cardinality_high_card_response_stability
+-- query_record_id: v2q_m8_032e290a496b0343
+-- problem_id: v2p_m8_5b425ab6f1221d73
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    "campaign",
+    COUNT(*) AS support,
+    AVG("age") AS avg_response
+FROM "m8"
+GROUP BY "campaign"
+HAVING COUNT(*) >= 5.0
+ORDER BY support DESC, avg_response DESC;

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_032e290a496b0343/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v3\n-- sql_source_label: v3_current\n-- sql_source_run_id: v3_cli_20260504_071000_c\n-- sql_source_dataset_id: m8\n-- family_id: cardinality_structure\n-- canonical_subitem_id: high_cardinality_response_stability\n-- intended_facet_id: target_cardinality_cross_section\n-- variant_semantic_role: focused_target_view\n-- template_id: tpl_cardinality_high_card_response_stability\n-- query_record_id: v2q_m8_032e290a496b0343\n-- problem_id: v2p_m8_5b425ab6f1221d73\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    \"campaign\",\n    COUNT(*) AS support,\n    AVG(\"age\") AS avg_response\nFROM \"m8\"\nGROUP BY \"campaign\"\nHAVING COUNT(*) >= 5.0\nORDER BY support DESC, avg_response DESC;", "result": "{\"query\": \"-- sql_source_version: v3\\n-- sql_source_label: v3_current\\n-- sql_source_run_id: v3_cli_20260504_071000_c\\n-- sql_source_dataset_id: m8\\n-- family_id: cardinality_structure\\n-- canonical_subitem_id: high_cardinality_response_stability\\n-- intended_facet_id: target_cardinality_cross_section\\n-- variant_semantic_role: focused_target_view\\n-- template_id: tpl_cardinality_high_card_response_stability\\n-- query_record_id: v2q_m8_032e290a496b0343\\n-- problem_id: v2p_m8_5b425ab6f1221d73\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    \\\"campaign\\\",\\n    COUNT(*) AS support,\\n    AVG(\\\"age\\\") AS avg_response\\nFROM \\\"m8\\\"\\nGROUP BY \\\"campaign\\\"\\nHAVING COUNT(*) >= 5.0\\nORDER BY support DESC, avg_response DESC;\", \"columns\": [\"campaign\", \"support\", \"avg_response\"], \"rows\": [{\"campaign\": \"1\", \"support\": 17544, \"avg_response\": 40.66478568171455}, {\"campaign\": \"2\", \"support\": 12505, \"avg_response\": 40.93362654938025}, {\"campaign\": \"3\", \"support\": 5521, \"avg_response\": 40.936424560767975}, {\"campaign\": \"4\", \"support\": 3522, \"avg_response\": 41.59965928449744}, {\"campaign\": \"5\", \"support\": 1764, \"avg_response\": 41.438208616780045}, {\"campaign\": \"6\", \"support\": 1291, \"avg_response\": 41.79705654531371}, {\"campaign\": \"7\", \"support\": 735, \"avg_response\": 41.75918367346939}, {\"campaign\": \"8\", \"support\": 540, \"avg_response\": 41.620370370370374}, {\"campaign\": \"9\", \"support\": 327, \"avg_response\": 40.35474006116208}, {\"campaign\": \"10\", \"support\": 266, \"avg_response\": 41.72932330827068}, {\"campaign\": \"11\", \"support\": 201, \"avg_response\": 42.288557213930346}, {\"campaign\": \"12\", \"support\": 155, \"avg_response\": 40.96774193548387}, {\"campaign\": \"13\", \"support\": 133, \"avg_response\": 39.96992481203007}, {\"campaign\": \"14\", \"support\": 93, \"avg_response\": 39.763440860215056}, {\"campaign\": \"15\", \"support\": 84, \"avg_response\": 40.476190476190474}, {\"campaign\": \"16\", \"support\": 79, \"avg_response\": 40.164556962025316}, {\"campaign\": \"17\", \"support\": 69, \"avg_response\": 41.44927536231884}, {\"campaign\": \"18\", \"support\": 51, \"avg_response\": 38.72549019607843}, {\"campaign\": \"19\", \"support\": 44, \"avg_response\": 41.02272727272727}, {\"campaign\": \"20\", \"support\": 43, \"avg_response\": 43.23255813953488}, {\"campaign\": \"21\", \"support\": 35, \"avg_response\": 38.714285714285715}, {\"campaign\": \"22\", \"support\": 23, \"avg_response\": 43.04347826086956}, {\"campaign\": \"25\", \"support\": 22, \"avg_response\": 40.86363636363637}, {\"campaign\": \"23\", \"support\": 22, \"avg_response\": 40.5}, {\"campaign\": \"24\", \"support\": 20, \"avg_response\": 36.45}, {\"campaign\": \"29\", \"support\": 16, \"avg_response\": 40.8125}, {\"campaign\": \"28\", \"support\": 16, \"avg_response\": 37.4375}, {\"campaign\": \"26\", \"support\": 13, \"avg_response\": 35.07692307692308}, {\"campaign\": \"31\", \"support\": 12, \"avg_response\": 32.25}, {\"campaign\": \"27\", \"support\": 10, \"avg_response\": 36.4}, {\"campaign\": \"32\", \"support\": 9, \"avg_response\": 43.22222222222222}, {\"campaign\": \"30\", \"support\": 8, \"avg_response\": 41.75}, {\"campaign\": \"33\", \"support\": 6, \"avg_response\": 39.666666666666664}, {\"campaign\": \"34\", \"support\": 5, \"avg_response\": 38.0}], \"row_count_returned\": 34, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 30.39}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_032e290a496b0343/run_manifest.json

@@ -0,0 +1,60 @@
+{
+  "run_id": "v3_cli_20260504_071000_c",
+  "dataset_id": "m8",
+  "started_at": "2026-05-04T08:57:11.720399+00:00",
+  "ended_at": "2026-05-04T08:57:11.754377+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m8_032e290a496b0343",
+    "problem_id": "v2p_m8_5b425ab6f1221d73",
+    "dataset_id": "m8",
+    "template_id": "tpl_cardinality_high_card_response_stability",
+    "template_name": "High-Cardinality Response Stability",
+    "family_id": "cardinality_structure",
+    "canonical_subitem_id": "high_cardinality_response_stability",
+    "intended_facet_id": "target_cardinality_cross_section",
+    "variant_semantic_role": "focused_target_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": true,
+    "question": "Use template High-Cardinality Response Stability to probe high_cardinality_response_stability with semantic role focused_target_view. Focus on measure_col=age, key_col=campaign.",
+    "bindings": {
+      "key_col": "campaign",
+      "measure_col": "age",
+      "min_support": 5
+    },
+    "binding_roles": [
+      "key_col",
+      "target_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    {key_col},\n    COUNT(*) AS support,\n    AVG({measure_col}) AS avg_response\nFROM {table}\nGROUP BY {key_col}\nHAVING COUNT(*) >= {min_support}\nORDER BY support DESC, avg_response DESC;",
+    "notes": [
+      "default_facets=target_cardinality_cross_section",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=13",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 13,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v3",
+  "sql_source_version": "v3",
+  "sql_source_label": "v3_current",
+  "generated_sql_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\sql\\v2q_m8_032e290a496b0343.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_032e290a496b0343/usage_summary.json

@@ -0,0 +1,9 @@
+{
+  "engine": "template",
+  "input_tokens": 0,
+  "cached_input_tokens": 0,
+  "output_tokens": 0,
+  "total_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "none"
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9126, "bytes_utf8": 9126, "lines": 300, "estimated_tokens": 2553}}
+{"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": 444, "bytes_utf8": 444, "lines": 1, "estimated_tokens": 114}, "usage": {"input_tokens": 22642, "cached_input_tokens": 21888, "output_tokens": 130}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_07b5d670671f42eb",
+    "api_calls": 0,
+    "input_tokens": 22642,
+    "cached_input_tokens": 21888,
+    "output_tokens": 130,
+    "total_tokens": 22772,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2553,
+    "estimated_output_tokens": 114,
+    "estimated_total_tokens": 2667,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10505.06,
+    "sql_execution_elapsed_ms_total": 45.91,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_07b5d670671f42eb\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,44 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-04T08:53:22.220527+00:00",
+  "ended_at": "2026-05-04T08:53:32.725855+00:00",
+  "elapsed_ms": 10505.06,
+  "prompt_metrics": {
+    "chars": 9126,
+    "bytes_utf8": 9126,
+    "lines": 300,
+    "estimated_tokens": 2553
+  },
+  "stdout_metrics": {
+    "chars": 778,
+    "bytes_utf8": 778,
+    "lines": 4,
+    "estimated_tokens": 225
+  },
+  "stderr_metrics": {
+    "chars": 23184,
+    "bytes_utf8": 23184,
+    "lines": 57,
+    "estimated_tokens": 13175
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 444,
+      "bytes_utf8": 444,
+      "lines": 1,
+      "estimated_tokens": 114
+    },
+    "usage": {
+      "input_tokens": 22642,
+      "cached_input_tokens": 21888,
+      "output_tokens": 130
+    }
+  },
+  "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/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,300 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m8
+- dataset_name: Bank Marketing
+- table_name: m8
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
+- task_type: classification
+- target_column: y
+- main_row_count: 45211
+- important_fields:
+- age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
+- job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
+- marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
+- education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
+- default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
+- balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
+- housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
+- loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
+- contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
+- month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
+- duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
+- campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
+- pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
+- previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
+- poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
+- y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
+- useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
+- fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
+- source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
+
+SQLite schema snapshot:
+{
+  "table_name": "m8",
+  "quoted_table_name": "\"m8\"",
+  "row_count": 45211,
+  "columns": [
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "marital",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "default",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "housing",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "loan",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "contact",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "campaign",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "pdays",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "previous",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "poutcome",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "y",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "age": "58",
+      "job": "management",
+      "marital": "married",
+      "education": "tertiary",
+      "default": "no",
+      "balance": "2143",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "261",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "44",
+      "job": "technician",
+      "marital": "single",
+      "education": "secondary",
+      "default": "no",
+      "balance": "29",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "151",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "entrepreneur",
+      "marital": "married",
+      "education": "secondary",
+      "default": "no",
+      "balance": "2",
+      "housing": "yes",
+      "loan": "yes",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "76",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "47",
+      "job": "blue-collar",
+      "marital": "married",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1506",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "92",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "unknown",
+      "marital": "single",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1",
+      "housing": "no",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "198",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m8",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=housing, measure_col=previous.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "housing",
+    "measure_col": "previous",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019df231-2d57-7290-b215-5588748eb9e2"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"housing\\\", SUM(CAST(\\\"previous\\\" AS NUMERIC)) AS total_measure\\nFROM \\\"m8\\\"\\nGROUP BY \\\"housing\\\"\\nHAVING SUM(CAST(\\\"previous\\\" AS NUMERIC)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 10;\",\"notes\":\"Used the provided Thresholded Group Ranking template with group_col=\\\"housing\\\" and measure_col=\\\"previous\\\". Cast \\\"previous\\\" to NUMERIC because the schema stores it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":22642,"cached_input_tokens":21888,"output_tokens":130}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"housing\", SUM(CAST(\"previous\" AS NUMERIC)) AS total_measure\nFROM \"m8\"\nGROUP BY \"housing\"\nHAVING SUM(CAST(\"previous\" AS NUMERIC)) > 0.0\nORDER BY total_measure DESC\nLIMIT 10;","notes":"Used the provided Thresholded Group Ranking template with group_col=\"housing\" and measure_col=\"previous\". Cast \"previous\" to NUMERIC because the schema stores it as TEXT."}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/cli/sql_stderr_attempt_1.txt

@@ -0,0 +1,57 @@
+2026-05-04T08:53:22.479820Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell
+2026-05-04T08:53:24.861608Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>
+  <head>
+    <meta name="viewport" content="width=device-width, initial-scale=1" />
+    <style global>body{font-family:Arial,Helvetica,sans-serif}.container{align-items:center;display:flex;flex-direction:column;gap:2rem;height:100%;justify-content:center;width:100%}@keyframes enlarge-appear{0%{opacity:0;transform:scale(75%) rotate(-90deg)}to{opacity:1;transform:scale(100%) rotate(0deg)}}.logo{color:#8e8ea0}.scale-appear{animation:enlarge-appear .4s ease-out}@media (min-width:768px){.scale-appear{height:48px;width:48px}}.data:empty{display:none}.data{border-radius:5px;color:#8e8ea0;text-align:center}@media (prefers-color-scheme:dark){body{background-color:#343541}.logo{color:#acacbe}}</style>
+  <meta http-equiv="refresh" content="360"></head>
+  <body>
+    <div class="container">
+      <div class="logo">
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+

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=housing, measure_col=previous.
+Result preview: [{"housing": "yes", "total_measure": 16502}, {"housing": "no", "total_measure": 9735}]

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v3
+-- sql_source_label: v3_current
+-- sql_source_run_id: v3_cli_20260504_071000_c
+-- sql_source_dataset_id: m8
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_tpch_thresholded_group_ranking
+-- query_record_id: v2q_m8_07b5d670671f42eb
+-- problem_id: v2p_m8_68a3f1f68fc4ec9e
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "housing", SUM(CAST("previous" AS NUMERIC)) AS total_measure
+FROM "m8"
+GROUP BY "housing"
+HAVING SUM(CAST("previous" AS NUMERIC)) > 0.0
+ORDER BY total_measure DESC
+LIMIT 10;

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"housing\", SUM(CAST(\"previous\" AS NUMERIC)) AS total_measure\nFROM \"m8\"\nGROUP BY \"housing\"\nHAVING SUM(CAST(\"previous\" AS NUMERIC)) > 0.0\nORDER BY total_measure DESC\nLIMIT 10;", "result": "{\"query\": \"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"housing\\\", SUM(CAST(\\\"previous\\\" AS NUMERIC)) AS total_measure\\nFROM \\\"m8\\\"\\nGROUP BY \\\"housing\\\"\\nHAVING SUM(CAST(\\\"previous\\\" AS NUMERIC)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 10;\", \"columns\": [\"housing\", \"total_measure\"], \"rows\": [{\"housing\": \"yes\", \"total_measure\": 16502}, {\"housing\": \"no\", \"total_measure\": 9735}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 45.91}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v3_cli_20260504_071000_c",
+  "dataset_id": "m8",
+  "started_at": "2026-05-04T08:53:22.212454+00:00",
+  "ended_at": "2026-05-04T08:53:32.813779+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m8_07b5d670671f42eb",
+    "problem_id": "v2p_m8_68a3f1f68fc4ec9e",
+    "dataset_id": "m8",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=housing, measure_col=previous.",
+    "bindings": {
+      "group_col": "housing",
+      "measure_col": "previous",
+      "top_k": 10,
+      "top_n": 4,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=cli",
+      "problem_index_within_template=6",
+      "sql_variant_index=1/2",
+      "binding_index=125"
+    ],
+    "template_selection_mode": "cli",
+    "selected_template_rank": 11,
+    "problem_index_within_template": 6,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v3",
+  "sql_source_version": "v3",
+  "sql_source_label": "v3_current",
+  "generated_sql_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\sql\\v2q_m8_07b5d670671f42eb.sql",
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_07b5d670671f42eb",
+    "api_calls": 0,
+    "input_tokens": 22642,
+    "cached_input_tokens": 21888,
+    "output_tokens": 130,
+    "total_tokens": 22772,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2553,
+    "estimated_output_tokens": 114,
+    "estimated_total_tokens": 2667,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10505.06,
+    "sql_execution_elapsed_ms_total": 45.91,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_07b5d670671f42eb\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-04T08:53:32.758900+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10505.06, "started_at": "2026-05-04T08:53:22.220527+00:00", "ended_at": "2026-05-04T08:53:32.725855+00:00", "prompt_metrics": {"chars": 9126, "bytes_utf8": 9126, "lines": 300, "estimated_tokens": 2553}, "response_metrics": {"chars": 444, "bytes_utf8": 444, "lines": 1, "estimated_tokens": 114}, "usage": {"input_tokens": 22642, "cached_input_tokens": 21888, "output_tokens": 130}, "stderr_preview": "2026-05-04T08:53:22.479820Z  WARN codex_core::shell_snapshot: Failed to create shell snapshot for powershell: Shell snapshot not supported yet for PowerShell\n2026-05-04T08:53:24.861608Z  WARN codex_analytics::client: events failed with status 403 Forbidden: <html>\n  <head>\n    <meta name=\"viewport\" content=\"width=device-width, initial-scale=1\" />\n    <style global>body{font-family:Arial,Helvetica,sans-serif}.container{align-items:center;display:flex;flex-direction:column;gap:2rem;height:100%;justify-content:center;width:100%}@keyframes enlarge-appear{0%{opacity:0;transform:scale(75%) rotate(-90deg)}to{opacity:1;transform:scale(100%) rotate(0deg)}}.logo{color:#8e8ea0}.scale-appear{animation:enlarge-appear .4s ease-out}@media (min-width:768px){.scale-appear{height:48px;width:48px}}.data:empty{display:none}.data{border-radius:5px;color:#8e8ea0;text-align:center}@media (prefers-color-scheme:dark){body{background-color:#343541}.logo{color:#acacbe}}</style>\n  <meta http-equiv=\"refresh\" conte", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"housing\\\", SUM(CAST(\\\"previous\\\" AS NUMERIC)) AS total_measure\\nFROM \\\"m8\\\"\\nGROUP BY \\\"housing\\\"\\nHAVING SUM(CAST(\\\"previous\\\" AS NUMERIC)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 10;\",\"notes\":\"Used the provided Thresholded Group Ranking template with group_col=\\\"housing\\\" and measure_col=\\\"previous\\\". Cast \\\"previous\\\" to NUMERIC because the schema stores it as TEXT.\"}"}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07b5d670671f42eb/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m8",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m8_07b5d670671f42eb",
+  "api_calls": 0,
+  "input_tokens": 22642,
+  "cached_input_tokens": 21888,
+  "output_tokens": 130,
+  "total_tokens": 22772,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 2553,
+  "estimated_output_tokens": 114,
+  "estimated_total_tokens": 2667,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 10505.06,
+  "sql_execution_elapsed_ms_total": 45.91,
+  "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_07b5d670671f42eb\\cli\\conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9255, "bytes_utf8": 9255, "lines": 303, "estimated_tokens": 2612}}
+{"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": 442, "bytes_utf8": 442, "lines": 1, "estimated_tokens": 118}, "usage": {"input_tokens": 22703, "cached_input_tokens": 21888, "output_tokens": 138}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m8",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m8_07cc6accc9d1836e",
+    "api_calls": 0,
+    "input_tokens": 22703,
+    "cached_input_tokens": 21888,
+    "output_tokens": 138,
+    "total_tokens": 22841,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 2612,
+    "estimated_output_tokens": 118,
+    "estimated_total_tokens": 2730,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 15562.58,
+    "sql_execution_elapsed_ms_total": 49.92,
+    "conversation_log_path": "D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\\logs\\subitem_workload_v3\\runs\\v3_cli_20260504_071000_c\\m8\\artifacts\\v2q_m8_07cc6accc9d1836e\\cli\\conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,44 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --disable plugins --sandbox read-only --cd \"D:\\dpan\\Uni\\Project\\HKUNAISS\\SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-04T08:18:21.366268+00:00",
+  "ended_at": "2026-05-04T08:18:36.928634+00:00",
+  "elapsed_ms": 15562.58,
+  "prompt_metrics": {
+    "chars": 9255,
+    "bytes_utf8": 9255,
+    "lines": 303,
+    "estimated_tokens": 2612
+  },
+  "stdout_metrics": {
+    "chars": 779,
+    "bytes_utf8": 779,
+    "lines": 4,
+    "estimated_tokens": 231
+  },
+  "stderr_metrics": {
+    "chars": 23184,
+    "bytes_utf8": 23184,
+    "lines": 57,
+    "estimated_tokens": 13209
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 442,
+      "bytes_utf8": 442,
+      "lines": 1,
+      "estimated_tokens": 118
+    },
+    "usage": {
+      "input_tokens": 22703,
+      "cached_input_tokens": 21888,
+      "output_tokens": 138
+    }
+  },
+  "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/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,303 @@
+You are generating one SQLite SELECT query for a single-table SQL QA task.
+Return strict JSON only, with this schema: {"sql": "...", "notes": "..."}.
+Rules:
+- Use only the provided table and columns.
+- Do not write INSERT, UPDATE, DELETE, DROP, ALTER, CREATE, PRAGMA, ATTACH, DETACH, or VACUUM.
+- Prefer the planned template and bound roles when provided.
+- Add a leading SQL comment exactly like: -- template_id: <planned_template_id>.
+- Generate SQLite-compatible SQL. SQLite does not support PERCENTILE_CONT or STDDEV.
+- Quote identifiers with double quotes.
+- Return no markdown and no extra prose.
+
+Dataset context:
+Dataset context for SQL QA:
+- dataset_id: m8
+- dataset_name: Bank Marketing
+- table_name: m8
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 16 feature columns and target `y`.
+- task_type: classification
+- target_column: y
+- main_row_count: 45211
+- important_fields:
+- age: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for age.
+- job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for job.
+- marital: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for marital.
+- education: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for education.
+- default: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for default.
+- balance: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for balance.
+- housing: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for housing.
+- loan: role=feature, type=categorical_binary. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for loan.
+- contact: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for contact.
+- day: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for day.
+- month: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for month.
+- duration: role=feature, type=numeric. tags=['condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for duration.
+- campaign: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for campaign.
+- pdays: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for pdays.
+- previous: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for previous.
+- poutcome: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for poutcome.
+- y: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for y.
+- useful_field_combinations: [['age', 'job', 'y'], ['age', 'age', 'y']]
+- fields_requiring_caution: ['y', 'balance', 'duration', 'pdays']
+- source_url: https://archive.ics.uci.edu/dataset/222/bank+marketing
+
+SQLite schema snapshot:
+{
+  "table_name": "m8",
+  "quoted_table_name": "\"m8\"",
+  "row_count": 45211,
+  "columns": [
+    {
+      "name": "age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "marital",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "default",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "housing",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "loan",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "contact",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "day",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "month",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "campaign",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "pdays",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "previous",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "poutcome",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "y",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "age": "58",
+      "job": "management",
+      "marital": "married",
+      "education": "tertiary",
+      "default": "no",
+      "balance": "2143",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "261",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "44",
+      "job": "technician",
+      "marital": "single",
+      "education": "secondary",
+      "default": "no",
+      "balance": "29",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "151",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "entrepreneur",
+      "marital": "married",
+      "education": "secondary",
+      "default": "no",
+      "balance": "2",
+      "housing": "yes",
+      "loan": "yes",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "76",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "47",
+      "job": "blue-collar",
+      "marital": "married",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1506",
+      "housing": "yes",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "92",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    },
+    {
+      "age": "33",
+      "job": "unknown",
+      "marital": "single",
+      "education": "unknown",
+      "default": "no",
+      "balance": "1",
+      "housing": "no",
+      "loan": "no",
+      "contact": "unknown",
+      "day": "5",
+      "month": "may",
+      "duration": "198",
+      "campaign": "1",
+      "pdays": "-1",
+      "previous": "0",
+      "poutcome": "unknown",
+      "y": "no"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_c2_two_dim_target_rate",
+    "template_name": "Two-Axis Target Rate Surface",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {group_col_2},\n       AVG(CASE WHEN {target_col} = {target_value} THEN 1 ELSE 0 END) AS target_rate\nFROM {table}\nGROUP BY {group_col}, {group_col_2}\nORDER BY target_rate DESC;",
+    "required_roles": [
+      "group_col",
+      "group_col_2",
+      "target_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m8",
+  "question": "Use template Two-Axis Target Rate Surface to probe dependency_strength_similarity with semantic role within_group_proportion. Focus on group_col=marital, group_col_2=previous, target_col=y.",
+  "planned_template_id": "tpl_c2_two_dim_target_rate",
+  "bindings": {
+    "group_col": "marital",
+    "group_col_2": "previous",
+    "target_col": "y",
+    "target_value": "no",
+    "top_k": 11,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 1428.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2},\n       AVG(CASE WHEN {target_col} = {target_value} THEN 1 ELSE 0 END) AS target_rate\nFROM {table}\nGROUP BY {group_col}, {group_col_2}\nORDER BY target_rate DESC;"
+}
+
+Repair context:
+{}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019df211-2bc0-7113-8447-1ce7fd0bd60c"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_two_dim_target_rate\\nSELECT \\\"marital\\\", \\\"previous\\\",\\n       AVG(CASE WHEN \\\"y\\\" = 'no' THEN 1 ELSE 0 END) AS target_rate\\nFROM \\\"m8\\\"\\nGROUP BY \\\"marital\\\", \\\"previous\\\"\\nORDER BY target_rate DESC;\",\"notes\":\"Used the planned template with fixed bindings: group_col=\\\"marital\\\", group_col_2=\\\"previous\\\", target_col=\\\"y\\\", target_value='no'. Identifiers are double-quoted and the query is SQLite-compatible.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":22703,"cached_input_tokens":21888,"output_tokens":138}}

Query/sql/v3/runs/v3_cli_20260504_071000_c/m8/artifacts/v2q_m8_07cc6accc9d1836e/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_c2_two_dim_target_rate\nSELECT \"marital\", \"previous\",\n       AVG(CASE WHEN \"y\" = 'no' THEN 1 ELSE 0 END) AS target_rate\nFROM \"m8\"\nGROUP BY \"marital\", \"previous\"\nORDER BY target_rate DESC;","notes":"Used the planned template with fixed bindings: group_col=\"marital\", group_col_2=\"previous\", target_col=\"y\", target_value='no'. Identifiers are double-quoted and the query is SQLite-compatible."}