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Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/run_manifest.json

@@ -0,0 +1,69 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T16:09:45.223312+00:00",
+  "ended_at": "2026-05-19T16:09:52.840365+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0148d341df5f5dce",
+    "problem_id": "v2p_m1_6002c32a7e48a151",
+    "dataset_id": "m1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "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 Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=Survey_Date, measure_col=Efficiency_Rating.",
+    "bindings": {
+      "group_col": "Survey_Date",
+      "measure_col": "Efficiency_Rating",
+      "top_k": 14,
+      "top_n": 5,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 95.0,
+      "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 DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=1/2",
+      "binding_index=134"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0148d341df5f5dce/trace.jsonl

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+{"timestamp": "2026-05-19T16:09:48.517653+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3291.84, "started_at": "2026-05-19T16:09:45.224985+00:00", "ended_at": "2026-05-19T16:09:48.516865+00:00", "prompt_metrics": {"chars": 16428, "bytes_utf8": 16428, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-1703-7952-8567-4ba8e9d6b182\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:09:52.840248+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3320.41, "started_at": "2026-05-19T16:09:49.518862+00:00", "ended_at": "2026-05-19T16:09:52.839297+00:00", "prompt_metrics": {"chars": 16428, "bytes_utf8": 16428, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e4100-2801-7b02-933b-4e243a7e27f7\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16315, "bytes_utf8": 16315, "lines": 454, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 349, "bytes_utf8": 349, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16694, "cached_input_tokens": 12032, "output_tokens": 324, "reasoning_output_tokens": 220}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0412d1c7c316e5f5",
+    "api_calls": 0,
+    "input_tokens": 16694,
+    "cached_input_tokens": 12032,
+    "output_tokens": 324,
+    "total_tokens": 17018,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8424.61,
+    "sql_execution_elapsed_ms_total": 1.03,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:07:34.094178+00:00",
+  "ended_at": "2026-05-19T16:07:42.518818+00:00",
+  "elapsed_ms": 8424.61,
+  "prompt_metrics": {
+    "chars": 16315,
+    "bytes_utf8": 16315,
+    "lines": 454,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 714,
+    "bytes_utf8": 714,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 349,
+      "bytes_utf8": 349,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16694,
+      "cached_input_tokens": 12032,
+      "output_tokens": 324,
+      "reasoning_output_tokens": 220
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,454 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tail_low_support_group_count_v2",
+    "template_name": "Low-Support Group Count",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Low-Support Group Count to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=Has_Children.",
+  "planned_template_id": "tpl_tail_low_support_group_count_v2",
+  "bindings": {
+    "group_col": "Has_Children",
+    "top_k": 12,
+    "top_n": 5,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 38.0,
+    "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\n    {group_col},\n    COUNT(*) AS support\nFROM {table}\nGROUP BY {group_col}\nORDER BY support ASC, {group_col}\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-16ef-7732-9584-aa86cef2248e"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tail_low_support_group_count_v2\\nSELECT\\n    \\\"Has_Children\\\",\\n    COUNT(*) AS \\\"support\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Has_Children\\\"\\nORDER BY \\\"support\\\" ASC, \\\"Has_Children\\\"\\nLIMIT 12;\",\"notes\":\"Applied the provided Low-Support Group Count template with group_col=\\\"Has_Children\\\" and top_k=12 on single table \\\"m1\\\".\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16694,"cached_input_tokens":12032,"output_tokens":324,"reasoning_output_tokens":220}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0412d1c7c316e5f5/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tail_low_support_group_count_v2\nSELECT\n    \"Has_Children\",\n    COUNT(*) AS \"support\"\nFROM \"m1\"\nGROUP BY \"Has_Children\"\nORDER BY \"support\" ASC, \"Has_Children\"\nLIMIT 12;","notes":"Applied the provided Low-Support Group Count template with group_col=\"Has_Children\" and top_k=12 on single table \"m1\"."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16304, "bytes_utf8": 16304, "lines": 456, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 351, "bytes_utf8": 351, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16677, "cached_input_tokens": 12032, "output_tokens": 291, "reasoning_output_tokens": 195}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_06e4799f5ca8a8e8",
+    "api_calls": 0,
+    "input_tokens": 16677,
+    "cached_input_tokens": 12032,
+    "output_tokens": 291,
+    "total_tokens": 16968,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 10573.79,
+    "sql_execution_elapsed_ms_total": 1.19,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:18.005140+00:00",
+  "ended_at": "2026-05-19T15:28:28.578967+00:00",
+  "elapsed_ms": 10573.79,
+  "prompt_metrics": {
+    "chars": 16304,
+    "bytes_utf8": 16304,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 713,
+    "bytes_utf8": 713,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 351,
+      "bytes_utf8": 351,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16677,
+      "cached_input_tokens": 12032,
+      "output_tokens": 291,
+      "reasoning_output_tokens": 195
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Gender, measure_col=Age.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "Gender",
+    "measure_col": "Age",
+    "top_k": 10,
+    "top_n": 3,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 41.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-2349-7a01-8d79-f802273b05f9"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"Gender\\\", SUM(CAST(\\\"Age\\\" AS NUMERIC)) AS \\\"total_measure\\\"\\nFROM \\\"m1\\\"\\nGROUP BY \\\"Gender\\\"\\nORDER BY \\\"total_measure\\\" DESC;\",\"notes\":\"Used the required grouped numeric sum template with \\\"Gender\\\" as the grouping column and cast \\\"Age\\\" to NUMERIC because the SQLite schema stores it as TEXT.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16677,"cached_input_tokens":12032,"output_tokens":291,"reasoning_output_tokens":195}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_06e4799f5ca8a8e8/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"Gender\", SUM(CAST(\"Age\" AS NUMERIC)) AS \"total_measure\"\nFROM \"m1\"\nGROUP BY \"Gender\"\nORDER BY \"total_measure\" DESC;","notes":"Used the required grouped numeric sum template with \"Gender\" as the grouping column and cast \"Age\" to NUMERIC because the SQLite schema stores it as TEXT."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:11:24.799231+00:00",
+  "ended_at": "2026-05-19T16:11:28.221052+00:00",
+  "elapsed_ms": 3421.8,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 16403,
+    "bytes_utf8": 16403,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:11:29.223077+00:00",
+  "ended_at": "2026-05-19T16:11:34.248141+00:00",
+  "elapsed_ms": 5025.03,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 16403,
+    "bytes_utf8": 16403,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Age.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "Location_Type",
+    "measure_col": "Age",
+    "top_k": 15,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 38.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=Location_Type, measure_col=Age.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "Location_Type",
+    "measure_col": "Age",
+    "top_k": 15,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 38.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-9c25-7e40-ac07-eec69444e7ef"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-9c25-7e40-ac07-eec69444e7ef"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-ad51-75a3-b323-0ad439537fd3"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_08382dd41d4b025d/cli/sql_response_attempt_2.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e4101-ad51-75a3-b323-0ad439537fd3"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.
+Result preview: [{"Location_Type": "Urban", "group_value": 1944.0}, {"Location_Type": "Suburban", "group_value": 1911.0}, {"Location_Type": "Rural", "group_value": 431.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/generated_sql.sql

@@ -0,0 +1,26 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_mass_similarity
+-- intended_facet_id: tail_ranked_signal
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m1_0978a623dc7f6b02
+-- problem_id: v2p_m1_088d53eb6133696a
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "Location_Type", SUM(CAST("WFH_Days_Per_Week" AS REAL)) AS group_value
+    FROM "m1"
+    GROUP BY "Location_Type"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."Location_Type", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.1
+ORDER BY g.group_value DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"Location_Type\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS group_value\n    FROM \"m1\"\n    GROUP BY \"Location_Type\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"Location_Type\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.1\nORDER BY g.group_value DESC;", "result": "{\"query\": \"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\", \"columns\": [\"Location_Type\", \"group_value\"], \"rows\": [{\"Location_Type\": \"Urban\", \"group_value\": 1944.0}, {\"Location_Type\": \"Suburban\", \"group_value\": 1911.0}, {\"Location_Type\": \"Rural\", \"group_value\": 431.0}], \"row_count_returned\": 3, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 1.57}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_a",
+  "dataset_id": "m1",
+  "started_at": "2026-05-19T15:47:01.624418+00:00",
+  "ended_at": "2026-05-19T15:47:13.400476+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_m1_0978a623dc7f6b02",
+    "problem_id": "v2p_m1_088d53eb6133696a",
+    "dataset_id": "m1",
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_mass_similarity",
+    "intended_facet_id": "tail_ranked_signal",
+    "variant_semantic_role": "count_distribution",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.",
+    "bindings": {
+      "group_col": "Location_Type",
+      "measure_col": "WFH_Days_Per_Week",
+      "top_k": 12,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 4.0,
+      "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": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "notes": [
+      "default_facets=tail_ranked_signal",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=72"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 7,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_0978a623dc7f6b02.sql",
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0978a623dc7f6b02",
+    "api_calls": 0,
+    "input_tokens": 16827,
+    "cached_input_tokens": 15744,
+    "output_tokens": 421,
+    "total_tokens": 17248,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11770.09,
+    "sql_execution_elapsed_ms_total": 1.57,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:47:13.397085+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 11770.09, "started_at": "2026-05-19T15:47:01.625725+00:00", "ended_at": "2026-05-19T15:47:13.395852+00:00", "prompt_metrics": {"chars": 16899, "bytes_utf8": 16899, "lines": 456, "estimated_tokens": null}, "response_metrics": {"chars": 632, "bytes_utf8": 632, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16827, "cached_input_tokens": 15744, "output_tokens": 421, "reasoning_output_tokens": 246}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.1\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the planned template with group_col=\\\"Location_Type\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". Cast \\\"WFH_Days_Per_Week\\\" from TEXT to REAL for SQLite aggregation.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "m1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_m1_0978a623dc7f6b02",
+  "api_calls": 0,
+  "input_tokens": 16827,
+  "cached_input_tokens": 15744,
+  "output_tokens": 421,
+  "total_tokens": 17248,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 1,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 11770.09,
+  "sql_execution_elapsed_ms_total": 1.57,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0978a623dc7f6b02/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16902, "bytes_utf8": 16902, "lines": 456, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 670, "bytes_utf8": 670, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16829, "cached_input_tokens": 15744, "output_tokens": 512, "reasoning_output_tokens": 334}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0b10a62218209cf5",
+    "api_calls": 0,
+    "input_tokens": 16829,
+    "cached_input_tokens": 15744,
+    "output_tokens": 512,
+    "total_tokens": 17341,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 11213.87,
+    "sql_execution_elapsed_ms_total": 2.6,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:47:13.402858+00:00",
+  "ended_at": "2026-05-19T15:47:24.616774+00:00",
+  "elapsed_ms": 11213.87,
+  "prompt_metrics": {
+    "chars": 16902,
+    "bytes_utf8": 16902,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1037,
+    "bytes_utf8": 1037,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 670,
+      "bytes_utf8": 670,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16829,
+      "cached_input_tokens": 15744,
+      "output_tokens": 512,
+      "reasoning_output_tokens": 334
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_relative_total_threshold",
+    "template_name": "Relative-to-Total Extreme Threshold",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role filtered_stable_view. Focus on group_col=Location_Type, measure_col=WFH_Days_Per_Week.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "Location_Type",
+    "measure_col": "WFH_Days_Per_Week",
+    "top_k": 17,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 4.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "WITH grouped AS (\n    SELECT {group_col}, SUM({measure_col}) AS group_value\n    FROM {table}\n    GROUP BY {group_col}\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.{group_col}, g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * {fraction_threshold}\nORDER BY g.group_value DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40eb-76ff-70c1-a1d9-1da41f6c4e84"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"Location_Type\\\", SUM(CAST(\\\"WFH_Days_Per_Week\\\" AS REAL)) AS group_value\\n    FROM \\\"m1\\\"\\n    GROUP BY \\\"Location_Type\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"Location_Type\\\", g.group_value\\nFROM grouped AS g\\nCROSS JOIN total AS t\\nWHERE g.group_value > t.total_value * 0.05\\nORDER BY g.group_value DESC;\",\"notes\":\"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\\\"Location_Type\\\" and measure_col=\\\"WFH_Days_Per_Week\\\". The measure is cast from TEXT to REAL so SQLite can sum it correctly.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16829,"cached_input_tokens":15744,"output_tokens":512,"reasoning_output_tokens":334}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0b10a62218209cf5/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"Location_Type\", SUM(CAST(\"WFH_Days_Per_Week\" AS REAL)) AS group_value\n    FROM \"m1\"\n    GROUP BY \"Location_Type\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"Location_Type\", g.group_value\nFROM grouped AS g\nCROSS JOIN total AS t\nWHERE g.group_value > t.total_value * 0.05\nORDER BY g.group_value DESC;","notes":"Applied the planned Relative-to-Total Extreme Threshold template with group_col=\"Location_Type\" and measure_col=\"WFH_Days_Per_Week\". The measure is cast from TEXT to REAL so SQLite can sum it correctly."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 16506, "bytes_utf8": 16506, "lines": 456, "estimated_tokens": null}}
+{"attempt": 1, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_1.txt", "raw_content_path": "cli/sql_response_attempt_1.raw.txt", "stderr_path": "cli/sql_stderr_attempt_1.txt", "metrics": {"chars": 1945, "bytes_utf8": 1945, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16719, "cached_input_tokens": 15744, "output_tokens": 3248, "reasoning_output_tokens": 2584}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0d4d344ddc1436c1",
+    "api_calls": 0,
+    "input_tokens": 16719,
+    "cached_input_tokens": 15744,
+    "output_tokens": 3248,
+    "total_tokens": 19967,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 46168.19,
+    "sql_execution_elapsed_ms_total": 7.93,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:52:52.913522+00:00",
+  "ended_at": "2026-05-19T15:53:39.081737+00:00",
+  "elapsed_ms": 46168.19,
+  "prompt_metrics": {
+    "chars": 16506,
+    "bytes_utf8": 16506,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2579,
+    "bytes_utf8": 2579,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 1945,
+      "bytes_utf8": 1945,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16719,
+      "cached_input_tokens": 15744,
+      "output_tokens": 3248,
+      "reasoning_output_tokens": 2584
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=Gender, measure_col=Years_Experience.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "Gender",
+    "measure_col": "Years_Experience",
+    "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": 7.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.raw.txt

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+{"type":"thread.started","thread_id":"019e40f0-a4d2-7e82-b3c6-e22dd9fd3c25"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"ordered\\\" AS (\\n  SELECT\\n    \\\"Gender\\\",\\n    CAST(\\\"Years_Experience\\\" AS REAL) AS \\\"measure_value\\\",\\n    ROW_NUMBER() OVER (\\n      PARTITION BY \\\"Gender\\\"\\n      ORDER BY CAST(\\\"Years_Experience\\\" AS REAL)\\n    ) AS \\\"rn\\\",\\n    COUNT(*) OVER (\\n      PARTITION BY \\\"Gender\\\"\\n    ) AS \\\"cnt\\\"\\n  FROM \\\"m1\\\"\\n  WHERE \\\"Gender\\\" IS NOT NULL\\n    AND \\\"Years_Experience\\\" IS NOT NULL\\n    AND TRIM(\\\"Years_Experience\\\") <> ''\\n),\\n\\\"eligible\\\" AS (\\n  SELECT *\\n  FROM \\\"ordered\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"positions\\\" AS (\\n  SELECT DISTINCT\\n    \\\"Gender\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) AS \\\"lower_rn\\\",\\n    CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n        THEN CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT) + 1\\n      ELSE CAST((1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS INT)\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"eligible\\\"\\n),\\n\\\"bounds\\\" AS (\\n  SELECT\\n    p.\\\"Gender\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\",\\n    MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"lower_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"lower_val\\\",\\n    MAX(CASE WHEN e.\\\"rn\\\" = p.\\\"upper_rn\\\" THEN e.\\\"measure_value\\\" END) AS \\\"upper_val\\\"\\n  FROM \\\"positions\\\" AS p\\n  JOIN \\\"eligible\\\" AS e\\n    ON e.\\\"Gender\\\" = p.\\\"Gender\\\"\\n  GROUP BY\\n    p.\\\"Gender\\\",\\n    p.\\\"pos\\\",\\n    p.\\\"lower_rn\\\",\\n    p.\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"Gender\\\",\\n  CASE\\n    WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_val\\\"\\n    ELSE \\\"lower_val\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_val\\\" - \\\"lower_val\\\")\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"bounds\\\"\\nORDER BY \\\"percentile_measure\\\" DESC;\",\"notes\":\"Emulates `PERCENTILE_CONT(0.9)` in SQLite for each `Gender` by linearly interpolating ordered `CAST(\\\"Years_Experience\\\" AS REAL)` values; excludes null/blank measures and keeps only groups with at least 5 rows.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":16719,"cached_input_tokens":15744,"output_tokens":3248,"reasoning_output_tokens":2584}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d4d344ddc1436c1/cli/sql_response_attempt_1.txt

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+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"ordered\" AS (\n  SELECT\n    \"Gender\",\n    CAST(\"Years_Experience\" AS REAL) AS \"measure_value\",\n    ROW_NUMBER() OVER (\n      PARTITION BY \"Gender\"\n      ORDER BY CAST(\"Years_Experience\" AS REAL)\n    ) AS \"rn\",\n    COUNT(*) OVER (\n      PARTITION BY \"Gender\"\n    ) AS \"cnt\"\n  FROM \"m1\"\n  WHERE \"Gender\" IS NOT NULL\n    AND \"Years_Experience\" IS NOT NULL\n    AND TRIM(\"Years_Experience\") <> ''\n),\n\"eligible\" AS (\n  SELECT *\n  FROM \"ordered\"\n  WHERE \"cnt\" >= 5\n),\n\"positions\" AS (\n  SELECT DISTINCT\n    \"Gender\",\n    (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n    CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) AS \"lower_rn\",\n    CASE\n      WHEN (1.0 + 0.9 * (\"cnt\" - 1)) > CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n        THEN CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT) + 1\n      ELSE CAST((1.0 + 0.9 * (\"cnt\" - 1)) AS INT)\n    END AS \"upper_rn\"\n  FROM \"eligible\"\n),\n\"bounds\" AS (\n  SELECT\n    p.\"Gender\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\",\n    MAX(CASE WHEN e.\"rn\" = p.\"lower_rn\" THEN e.\"measure_value\" END) AS \"lower_val\",\n    MAX(CASE WHEN e.\"rn\" = p.\"upper_rn\" THEN e.\"measure_value\" END) AS \"upper_val\"\n  FROM \"positions\" AS p\n  JOIN \"eligible\" AS e\n    ON e.\"Gender\" = p.\"Gender\"\n  GROUP BY\n    p.\"Gender\",\n    p.\"pos\",\n    p.\"lower_rn\",\n    p.\"upper_rn\"\n)\nSELECT\n  \"Gender\",\n  CASE\n    WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_val\"\n    ELSE \"lower_val\" + (\"pos\" - \"lower_rn\") * (\"upper_val\" - \"lower_val\")\n  END AS \"percentile_measure\"\nFROM \"bounds\"\nORDER BY \"percentile_measure\" DESC;","notes":"Emulates `PERCENTILE_CONT(0.9)` in SQLite for each `Gender` by linearly interpolating ordered `CAST(\"Years_Experience\" AS REAL)` values; excludes null/blank measures and keeps only groups with at least 5 rows."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl

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+{"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": 381, "bytes_utf8": 381, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 16683, "cached_input_tokens": 12032, "output_tokens": 275, "reasoning_output_tokens": 180}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/session_summary.json

@@ -0,0 +1,25 @@
+{
+  "engine": "v2-cli:codex",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "ai_cli_calls": 1,
+  "usage_summary": {
+    "dataset_id": "m1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m1_0d6aed317412ef3a",
+    "api_calls": 0,
+    "input_tokens": 16683,
+    "cached_input_tokens": 12032,
+    "output_tokens": 275,
+    "total_tokens": 16958,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 1,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 8145.35,
+    "sql_execution_elapsed_ms_total": 1.06,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:44.475828+00:00",
+  "ended_at": "2026-05-19T15:28:52.621206+00:00",
+  "elapsed_ms": 8145.35,
+  "prompt_metrics": {
+    "chars": 16349,
+    "bytes_utf8": 16349,
+    "lines": 456,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 735,
+    "bytes_utf8": 735,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 381,
+      "bytes_utf8": 381,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 16683,
+      "cached_input_tokens": 12032,
+      "output_tokens": 275,
+      "reasoning_output_tokens": 180
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/artifacts/v2q_m1_0d6aed317412ef3a/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,456 @@
+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: m1
+- dataset_name: Remote Worker Productivity
+- table_name: m1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one employee survey/assessment record in a remote-work context.
+- task_type: classification
+- target_column: Response_Quality
+- main_row_count: 1500
+- important_fields:
+- Employee_ID: role=identifier, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Employee identifier.
+- Age: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Employee age in years.
+- Years_Experience: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Years of professional experience.
+- WFH_Days_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of work-from-home days per week.
+- Gender: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Self-reported gender category.
+- Education_Level: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Highest education category.
+- Marital_Status: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Marital status category.
+- Has_Children: role=feature, type=categorical_binary. tags=['condition_candidate', 'subgroup_candidate'] desc=Whether the employee has children.
+- Location_Type: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Residential location type.
+- Department: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Work department/function.
+- Job_Level: role=feature, type=categorical_ordinal. ordered=['Junior', 'Mid-Level', 'Senior', 'Lead', 'Manager', 'Director'] tags=['condition_candidate', 'subgroup_candidate'] desc=Job seniority level.
+- Company_Size: role=feature, type=categorical_ordinal. ordered=['Startup (1-50)', 'Small (51-200)', 'Medium (201-1000)', 'Large (1001-5000)', 'Enterprise (5000+)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Employer size bracket.
+- Industry: role=feature, type=categorical_nominal. tags=['condition_candidate', 'subgroup_candidate'] desc=Industry sector.
+- Home_Office_Quality: role=feature, type=categorical_ordinal. ordered=['Poor', 'Average', 'Good', 'Excellent'] tags=['condition_candidate', 'subgroup_candidate'] desc=Self-rated home office quality.
+- Internet_Speed_Category: role=feature, type=categorical_ordinal. ordered=['Slow (<25 Mbps)', 'Moderate (25-50 Mbps)', 'Fast (50-100 Mbps)', 'Very Fast (100+ Mbps)'] tags=['condition_candidate', 'subgroup_candidate'] desc=Internet speed category at home office.
+- Work_Hours_Per_Week: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Average work hours per week.
+- Manager_Support_Level: role=feature, type=categorical_ordinal. ordered=['Very Low', 'Low', 'Moderate', 'High', 'Very High'] tags=['condition_candidate', 'subgroup_candidate'] desc=Perceived manager support level.
+- Team_Collaboration_Frequency: role=feature, type=categorical_ordinal. ordered=['Monthly', 'Bi-weekly', 'Weekly', 'Few times per week', 'Daily'] tags=['condition_candidate', 'subgroup_candidate'] desc=Frequency of team collaboration.
+- Productivity_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Productivity score.
+- Task_Completion_Rate: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Task completion rate score.
+- Quality_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Work quality score.
+- Innovation_Score: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Innovation score.
+- Efficiency_Rating: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Efficiency rating score.
+- Meetings_Per_Week: role=feature, type=numeric_discrete. tags=['condition_candidate', 'measure'] desc=Number of meetings per week.
+- Commute_Time_Minutes: role=feature, type=numeric. tags=['condition_candidate', 'measure'] desc=Typical commute time in minutes.
+- Job_Satisfaction: role=feature, type=numeric_score. tags=['condition_candidate', 'measure', 'response_candidate'] desc=Job satisfaction score.
+- Stress_Level: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Stress level (scaled score).
+- Work_Life_Balance: role=feature, type=numeric_ordinal_scale. tags=['condition_candidate', 'measure'] desc=Work-life balance score (scaled).
+- Survey_Date: role=feature, type=date. tags=['time_candidate', 'condition_candidate'] desc=Survey date.
+- Response_Quality: role=target, type=categorical_ordinal_target. ordered=['Low', 'Medium', 'High'] tags=['target_candidate'] desc=Response quality class label.
+- useful_field_combinations: [['Department', 'Job_Level', 'Response_Quality'], ['Manager_Support_Level', 'Team_Collaboration_Frequency', 'Response_Quality'], ['WFH_Days_Per_Week', 'Home_Office_Quality', 'Productivity_Score']]
+- fields_requiring_caution: ['Employee_ID', 'Productivity_Score', 'Task_Completion_Rate', 'Quality_Score', 'Efficiency_Rating', 'Job_Satisfaction']
+- source_url: https://huggingface.co/datasets/nprak26/remote-worker-productivity
+
+SQLite schema snapshot:
+{
+  "table_name": "m1",
+  "quoted_table_name": "\"m1\"",
+  "row_count": 1500,
+  "columns": [
+    {
+      "name": "Employee_ID",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Age",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Years_Experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "WFH_Days_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Education_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Marital_Status",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Has_Children",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Location_Type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Department",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Company_Size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Industry",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Home_Office_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Internet_Speed_Category",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Hours_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Manager_Support_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Team_Collaboration_Frequency",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Productivity_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Task_Completion_Rate",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Quality_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Innovation_Score",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Efficiency_Rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Meetings_Per_Week",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Commute_Time_Minutes",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Job_Satisfaction",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Stress_Level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Work_Life_Balance",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Survey_Date",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "Response_Quality",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "Employee_ID": "EMP0001",
+      "Age": "39",
+      "Years_Experience": "10",
+      "WFH_Days_Per_Week": "2",
+      "Gender": "Female",
+      "Education_Level": "Associate Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Product",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Large (1001-5000)",
+      "Industry": "Finance",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "41",
+      "Manager_Support_Level": "Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "52.2",
+      "Task_Completion_Rate": "56.6",
+      "Quality_Score": "58.1",
+      "Innovation_Score": "52.1",
+      "Efficiency_Rating": "72.1",
+      "Meetings_Per_Week": "4",
+      "Commute_Time_Minutes": "48",
+      "Job_Satisfaction": "55.9",
+      "Stress_Level": "6",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-04-05",
+      "Response_Quality": "Medium"
+    },
+    {
+      "Employee_ID": "EMP0002",
+      "Age": "33",
+      "Years_Experience": "4",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Female",
+      "Education_Level": "Master Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "No",
+      "Location_Type": "Urban",
+      "Department": "Customer Success",
+      "Job_Level": "Senior",
+      "Company_Size": "Startup (1-50)",
+      "Industry": "Education",
+      "Home_Office_Quality": "Good",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "52",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Monthly",
+      "Productivity_Score": "81.5",
+      "Task_Completion_Rate": "70.8",
+      "Quality_Score": "93.3",
+      "Innovation_Score": "77.9",
+      "Efficiency_Rating": "89.5",
+      "Meetings_Per_Week": "12",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "96.1",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "8",
+      "Survey_Date": "2024-01-29",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0003",
+      "Age": "40",
+      "Years_Experience": "3",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "PhD",
+      "Marital_Status": "Single",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Operations",
+      "Job_Level": "Mid-Level",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Fast (50-100 Mbps)",
+      "Work_Hours_Per_Week": "43",
+      "Manager_Support_Level": "Moderate",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "82.2",
+      "Task_Completion_Rate": "81.9",
+      "Quality_Score": "84.7",
+      "Innovation_Score": "63.2",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "15",
+      "Commute_Time_Minutes": "24",
+      "Job_Satisfaction": "90.4",
+      "Stress_Level": "5",
+      "Work_Life_Balance": "6",
+      "Survey_Date": "2024-01-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0004",
+      "Age": "48",
+      "Years_Experience": "14",
+      "WFH_Days_Per_Week": "3",
+      "Gender": "Male",
+      "Education_Level": "Bachelor Degree",
+      "Marital_Status": "Married",
+      "Has_Children": "Yes",
+      "Location_Type": "Urban",
+      "Department": "Finance",
+      "Job_Level": "Manager",
+      "Company_Size": "Medium (201-1000)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Excellent",
+      "Internet_Speed_Category": "Very Fast (100+ Mbps)",
+      "Work_Hours_Per_Week": "45",
+      "Manager_Support_Level": "High",
+      "Team_Collaboration_Frequency": "Daily",
+      "Productivity_Score": "75.6",
+      "Task_Completion_Rate": "70.2",
+      "Quality_Score": "67.8",
+      "Innovation_Score": "82.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "8",
+      "Commute_Time_Minutes": "8",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "10",
+      "Work_Life_Balance": "5",
+      "Survey_Date": "2024-04-18",
+      "Response_Quality": "High"
+    },
+    {
+      "Employee_ID": "EMP0005",
+      "Age": "32",
+      "Years_Experience": "6",
+      "WFH_Days_Per_Week": "5",
+      "Gender": "Male",
+      "Education_Level": "High School",
+      "Marital_Status": "Divorced",
+      "Has_Children": "Yes",
+      "Location_Type": "Rural",
+      "Department": "Engineering",
+      "Job_Level": "Senior",
+      "Company_Size": "Small (51-200)",
+      "Industry": "Technology",
+      "Home_Office_Quality": "Average",
+      "Internet_Speed_Category": "Moderate (25-50 Mbps)",
+      "Work_Hours_Per_Week": "42",
+      "Manager_Support_Level": "Very Low",
+      "Team_Collaboration_Frequency": "Few times per week",
+      "Productivity_Score": "98.0",
+      "Task_Completion_Rate": "98.2",
+      "Quality_Score": "86.4",
+      "Innovation_Score": "67.5",
+      "Efficiency_Rating": "95.0",
+      "Meetings_Per_Week": "10",
+      "Commute_Time_Minutes": "0",
+      "Job_Satisfaction": "100.0",
+      "Stress_Level": "3",
+      "Work_Life_Balance": "4",
+      "Survey_Date": "2024-02-19",
+      "Response_Quality": "High"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_h2o_group_sum",
+    "template_name": "Grouped Numeric Sum",
+    "primary_family": "subgroup_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m1",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=Education_Level, measure_col=Years_Experience.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "Education_Level",
+    "measure_col": "Years_Experience",
+    "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": 5.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}