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Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_1bf8fc06dacea1e9.sql

@@ -0,0 +1,24 @@
+-- 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_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_m4_quantile_tail_slice
+-- query_record_id: v2q_m1_1bf8fc06dacea1e9
+-- problem_id: v2p_m1_23a15564d1926815
+-- realization_mode: agent
+-- source_kind: agent
+WITH "buckets" AS (
+    SELECT
+        CAST("Job_Satisfaction" AS REAL) AS "Job_Satisfaction",
+        NTILE(10) OVER (ORDER BY CAST("Job_Satisfaction" AS REAL) DESC) AS "tail_bucket"
+    FROM "m1"
+    WHERE "Job_Satisfaction" IS NOT NULL
+)
+SELECT "Job_Satisfaction"
+FROM "buckets"
+WHERE "tail_bucket" = 1
+ORDER BY "Job_Satisfaction" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_1c81aceb60acc633.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_1c81aceb60acc633
+-- problem_id: v2p_m1_beb6fa3fd68c3b56
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+    SELECT "Industry", SUM(CAST("Quality_Score" AS REAL)) AS "group_value"
+    FROM "m1"
+    GROUP BY "Industry"
+), "total" AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM "grouped"
+)
+SELECT g."Industry", 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/sql/v2q_m1_1cacaa98c22d42c3.sql

@@ -0,0 +1,23 @@
+-- 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_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_m4_quantile_tail_slice
+-- query_record_id: v2q_m1_1cacaa98c22d42c3
+-- problem_id: v2p_m1_c35f9b1c9e631696
+-- realization_mode: agent
+-- source_kind: agent
+WITH buckets AS (
+    SELECT
+        "Productivity_Score",
+        NTILE(10) OVER (ORDER BY CAST("Productivity_Score" AS REAL) DESC) AS tail_bucket
+    FROM "m1"
+)
+SELECT "Productivity_Score"
+FROM buckets
+WHERE tail_bucket = 1
+ORDER BY CAST("Productivity_Score" AS REAL) DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_249b31099d1e3aec.sql

@@ -0,0 +1,65 @@
+-- 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_concentration_consistency
+-- intended_facet_id: rare_target_concentration
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_grouped_percentile_point
+-- query_record_id: v2q_m1_249b31099d1e3aec
+-- problem_id: v2p_m1_d623ff814f884430
+-- realization_mode: agent
+-- source_kind: agent
+WITH "ordered" AS (
+  SELECT
+    "Response_Quality",
+    CAST("Age" AS REAL) AS "age_value",
+    ROW_NUMBER() OVER (
+      PARTITION BY "Response_Quality"
+      ORDER BY CAST("Age" AS REAL)
+    ) AS "rn",
+    COUNT(*) OVER (
+      PARTITION BY "Response_Quality"
+    ) AS "cnt"
+  FROM "m1"
+  WHERE "Age" IS NOT NULL
+),
+"positions" AS (
+  SELECT DISTINCT
+    "Response_Quality",
+    ((("cnt" - 1) * 0.9) + 1.0) AS "pos"
+  FROM "ordered"
+),
+"bounds" AS (
+  SELECT
+    "Response_Quality",
+    "pos",
+    CAST("pos" AS INTEGER) AS "lower_rn",
+    CAST("pos" AS INTEGER) + CASE
+      WHEN "pos" > CAST("pos" AS INTEGER) THEN 1
+      ELSE 0
+    END AS "upper_rn"
+  FROM "positions"
+)
+SELECT
+  b."Response_Quality",
+  CASE
+    WHEN b."lower_rn" = b."upper_rn" THEN
+      MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END)
+    ELSE
+      MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END) +
+      (b."pos" - b."lower_rn") * (
+        MAX(CASE WHEN o."rn" = b."upper_rn" THEN o."age_value" END) -
+        MAX(CASE WHEN o."rn" = b."lower_rn" THEN o."age_value" END)
+      )
+  END AS "percentile_measure"
+FROM "bounds" AS b
+JOIN "ordered" AS o
+  ON o."Response_Quality" = b."Response_Quality"
+GROUP BY
+  b."Response_Quality",
+  b."pos",
+  b."lower_rn",
+  b."upper_rn"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_3630c87ccc27f385.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_c2_filtered_group_count_2d
+-- query_record_id: v2q_m1_3630c87ccc27f385
+-- problem_id: v2p_m1_eae99f02770d939a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Has_Children", "Stress_Level", COUNT(*) AS row_count
+FROM "m1"
+WHERE CAST("Stress_Level" AS REAL) >= 7.0
+GROUP BY "Has_Children", "Stress_Level"
+ORDER BY row_count DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_3c2b82ce429ef200.sql

@@ -0,0 +1,25 @@
+-- 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: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: value_imbalance_profile
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_cardinality_support_rank_profile
+-- query_record_id: v2q_m1_3c2b82ce429ef200
+-- problem_id: v2p_m1_47534f1c87fff78a
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "Education_Level" AS value_label, COUNT(*) AS support
+    FROM "m1"
+    GROUP BY "Education_Level"
+)
+SELECT
+    value_label,
+    support,
+    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank
+FROM grouped
+ORDER BY support DESC, value_label;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_53277d01856342f6.sql

@@ -0,0 +1,28 @@
+-- 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: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m1_53277d01856342f6
+-- problem_id: v2p_m1_b1adf7ba4482a0f1
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "Survey_Date",
+  "Commute_Time_Minutes",
+  "total_measure",
+  "total_measure" * 100.0 / SUM("total_measure") OVER (PARTITION BY "Survey_Date") AS "share_within_group"
+FROM (
+  SELECT
+    "Survey_Date",
+    "Commute_Time_Minutes",
+    SUM(CAST("Productivity_Score" AS REAL)) AS "total_measure"
+  FROM "m1"
+  GROUP BY "Survey_Date", "Commute_Time_Minutes"
+)
+ORDER BY "share_within_group" DESC
+LIMIT 12;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_66bccadb790a3123.sql

@@ -0,0 +1,21 @@
+-- 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: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: focused_target_view
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m1_66bccadb790a3123
+-- problem_id: v2p_m1_36f31961263eaac2
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "Survey_Date",
+  "Commute_Time_Minutes",
+  SUM(CAST("Productivity_Score" AS REAL)) AS total_measure,
+  SUM(CAST("Productivity_Score" AS REAL)) * 100.0 / SUM(SUM(CAST("Productivity_Score" AS REAL))) OVER (PARTITION BY "Survey_Date") AS share_within_group
+FROM "m1"
+GROUP BY "Survey_Date", "Commute_Time_Minutes"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_7a6589da210c1bd4.sql

@@ -0,0 +1,25 @@
+-- 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: cardinality_structure
+-- canonical_subitem_id: support_rank_profile_consistency
+-- intended_facet_id: support_concentration
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_cardinality_support_rank_profile
+-- query_record_id: v2q_m1_7a6589da210c1bd4
+-- problem_id: v2p_m1_301068b55a94c124
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "Gender" AS value_label, COUNT(*) AS support
+    FROM "m1"
+    GROUP BY "Gender"
+)
+SELECT
+    value_label,
+    support,
+    CAST(support AS FLOAT) / NULLIF(SUM(support) OVER (), 0) AS support_share,
+    ROW_NUMBER() OVER (ORDER BY support DESC, value_label) AS support_rank
+FROM grouped
+ORDER BY support DESC, value_label;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_ae154c66cfcfc8db.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_ae154c66cfcfc8db
+-- problem_id: v2p_m1_0700623b46bbf458
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "Home_Office_Quality", SUM(CAST("Innovation_Score" AS REAL)) AS group_value
+    FROM "m1"
+    GROUP BY "Home_Office_Quality"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."Home_Office_Quality", g.group_value
+FROM grouped AS g
+CROSS JOIN total AS t
+WHERE g.group_value > t.total_value * 0.05
+ORDER BY g.group_value DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_ae904dc31842a947.sql

@@ -0,0 +1,17 @@
+-- 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: subgroup_structure
+-- canonical_subitem_id: subgroup_size_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_clickbench_group_count
+-- query_record_id: v2q_m1_ae904dc31842a947
+-- problem_id: v2p_m1_a0748ef3f83d4716
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Team_Collaboration_Frequency", COUNT(*) AS "row_count"
+FROM "m1"
+GROUP BY "Team_Collaboration_Frequency"
+ORDER BY "row_count" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_bbde35724f878391.sql

@@ -0,0 +1,17 @@
+-- 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: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_distribution_shift
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_m1_bbde35724f878391
+-- problem_id: v2p_m1_92333ae77b9cc6cf
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Industry", SUM(CAST("Efficiency_Rating" AS REAL)) AS "total_measure"
+FROM "m1"
+GROUP BY "Industry"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_c24c10c5a88f3e89.sql

@@ -0,0 +1,17 @@
+-- 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: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_rank_order
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_m1_c24c10c5a88f3e89
+-- problem_id: v2p_m1_efa43cba80a67eac
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Company_Size", SUM(CAST("Innovation_Score" AS REAL)) AS "total_measure"
+FROM "m1"
+GROUP BY "Company_Size"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_c38da853d6c799f6.sql

@@ -0,0 +1,27 @@
+-- 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: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m1_c38da853d6c799f6
+-- problem_id: v2p_m1_2dfe55a422e2c976
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+    SELECT "Industry", SUM(CAST("Quality_Score" AS REAL)) AS "group_value"
+    FROM "m1"
+    GROUP BY "Industry"
+),
+"total" AS (
+    SELECT SUM("group_value") AS "total_value"
+    FROM "grouped"
+)
+SELECT g."Industry", g."group_value"
+FROM "grouped" AS g
+CROSS JOIN "total" AS t
+WHERE g."group_value" > t."total_value" * 0.05
+ORDER BY g."group_value" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_c38ddb99037a01c0.sql

@@ -0,0 +1,25 @@
+-- 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: conditional_dependency_structure
+-- canonical_subitem_id: dependency_strength_similarity
+-- intended_facet_id: pairwise_conditional_dependency
+-- variant_semantic_role: within_group_proportion
+-- template_id: tpl_tpcds_within_group_share
+-- query_record_id: v2q_m1_c38ddb99037a01c0
+-- problem_id: v2p_m1_c57fabae5f022d21
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Stress_Level", "Innovation_Score",
+       SUM(CAST("WFH_Days_Per_Week" AS REAL)) AS total_measure,
+       SUM(CAST("WFH_Days_Per_Week" AS REAL)) * 100.0 / SUM(SUM(CAST("WFH_Days_Per_Week" AS REAL))) OVER (PARTITION BY "Stress_Level") AS share_within_group
+FROM "m1"
+WHERE "Stress_Level" IS NOT NULL
+  AND "Innovation_Score" IS NOT NULL
+  AND "WFH_Days_Per_Week" IS NOT NULL
+  AND TRIM("Stress_Level") <> ''
+  AND TRIM("Innovation_Score") <> ''
+  AND TRIM("WFH_Days_Per_Week") <> ''
+GROUP BY "Stress_Level", "Innovation_Score"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_d5258be445612f80.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: m1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: count_distribution
+-- template_id: tpl_c2_filtered_group_count_2d
+-- query_record_id: v2q_m1_d5258be445612f80
+-- problem_id: v2p_m1_0dba2323f7ac0476
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "Has_Children", "Team_Collaboration_Frequency", COUNT(*) AS "row_count"
+FROM "m1"
+WHERE CAST("Job_Satisfaction" AS REAL) >= 100.0
+GROUP BY "Has_Children", "Team_Collaboration_Frequency"
+ORDER BY "row_count" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m1/sql/v2q_m1_ebe2f831ba81a22e.sql

@@ -0,0 +1,31 @@
+-- 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: filtered_stable_view
+-- template_id: tpl_tpch_relative_total_threshold
+-- query_record_id: v2q_m1_ebe2f831ba81a22e
+-- problem_id: v2p_m1_61c86b57800495e1
+-- realization_mode: agent
+-- source_kind: agent
+WITH "grouped" AS (
+  SELECT
+    "Manager_Support_Level",
+    SUM(CAST("Meetings_Per_Week" AS REAL)) AS "group_value"
+  FROM "m1"
+  GROUP BY "Manager_Support_Level"
+),
+"total" AS (
+  SELECT SUM("group_value") AS "total_value"
+  FROM "grouped"
+)
+SELECT
+  g."Manager_Support_Level",
+  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/n12/artifacts/v2q_n12_000cdbe19ac46399/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 4634, "bytes_utf8": 4634, "lines": 144, "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": 412, "bytes_utf8": 412, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13468, "cached_input_tokens": 12672, "output_tokens": 616, "reasoning_output_tokens": 505}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_000cdbe19ac46399/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": "n12",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n12_000cdbe19ac46399",
+    "api_calls": 0,
+    "input_tokens": 13468,
+    "cached_input_tokens": 12672,
+    "output_tokens": 616,
+    "total_tokens": 14084,
+    "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": 12091.4,
+    "sql_execution_elapsed_ms_total": 383.27,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_000cdbe19ac46399/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_000cdbe19ac46399/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": "2026-05-19T15:59:21.211884+00:00",
+  "ended_at": "2026-05-19T15:59:33.303314+00:00",
+  "elapsed_ms": 12091.4,
+  "prompt_metrics": {
+    "chars": 4634,
+    "bytes_utf8": 4634,
+    "lines": 144,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 764,
+    "bytes_utf8": 764,
+    "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": 412,
+      "bytes_utf8": 412,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 13468,
+      "cached_input_tokens": 12672,
+      "output_tokens": 616,
+      "reasoning_output_tokens": 505
+    }
+  },
+  "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/n12/artifacts/v2q_n12_000cdbe19ac46399/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,144 @@
+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: n12
+- dataset_name: Skin Segmentation
+- table_name: n12
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 3 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 245057
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 3.
+- target: role=target, type=categorical_ordinal_target. ordered=['1', '2'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['feature_1', 'feature_2', 'target'], ['feature_1', 'feature_1', 'target']]
+- fields_requiring_caution: ['target', 'feature_1', 'feature_2', 'feature_3']
+- source_url: https://archive.ics.uci.edu/dataset/229/skin+segmentation
+
+SQLite schema snapshot:
+{
+  "table_name": "n12",
+  "quoted_table_name": "\"n12\"",
+  "row_count": 245057,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "74",
+      "feature_2": "85",
+      "feature_3": "123",
+      "target": "1"
+    },
+    {
+      "feature_1": "73",
+      "feature_2": "84",
+      "feature_3": "122",
+      "target": "1"
+    },
+    {
+      "feature_1": "72",
+      "feature_2": "83",
+      "feature_3": "121",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    }
+  ]
+}
+
+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": "n12",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=feature_3, measure_col=feature_3.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "feature_3",
+    "measure_col": "feature_3",
+    "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": 186.0,
+    "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/n12/artifacts/v2q_n12_000cdbe19ac46399/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f6-9185-7e93-a0e9-3b0811d27453"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"feature_3\\\",\\n  AVG(CAST(\\\"feature_3\\\" AS REAL)) OVER (PARTITION BY \\\"feature_3\\\") AS avg_measure\\nFROM \\\"n12\\\"\\nWHERE \\\"feature_3\\\" IS NOT NULL\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested Window Partition Average template with group_col and measure_col both bound to feature_3. CAST keeps the window AVG numeric in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":13468,"cached_input_tokens":12672,"output_tokens":616,"reasoning_output_tokens":505}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_000cdbe19ac46399/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"feature_3\",\n  AVG(CAST(\"feature_3\" AS REAL)) OVER (PARTITION BY \"feature_3\") AS avg_measure\nFROM \"n12\"\nWHERE \"feature_3\" IS NOT NULL\nORDER BY avg_measure DESC;","notes":"Uses the requested Window Partition Average template with group_col and measure_col both bound to feature_3. CAST keeps the window AVG numeric in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_0090e33f694627e3/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 4550, "bytes_utf8": 4550, "lines": 144, "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": 376, "bytes_utf8": 376, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13456, "cached_input_tokens": 12032, "output_tokens": 500, "reasoning_output_tokens": 396}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_0090e33f694627e3/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": "n12",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n12_0090e33f694627e3",
+    "api_calls": 0,
+    "input_tokens": 13456,
+    "cached_input_tokens": 12032,
+    "output_tokens": 500,
+    "total_tokens": 13956,
+    "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": 14140.62,
+    "sql_execution_elapsed_ms_total": 125.95,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_0090e33f694627e3/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_0090e33f694627e3/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": "2026-05-19T15:30:00.565626+00:00",
+  "ended_at": "2026-05-19T15:30:14.706275+00:00",
+  "elapsed_ms": 14140.62,
+  "prompt_metrics": {
+    "chars": 4550,
+    "bytes_utf8": 4550,
+    "lines": 144,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 730,
+    "bytes_utf8": 730,
+    "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": 376,
+      "bytes_utf8": 376,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 13456,
+      "cached_input_tokens": 12032,
+      "output_tokens": 500,
+      "reasoning_output_tokens": 396
+    }
+  },
+  "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/n12/artifacts/v2q_n12_0090e33f694627e3/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,144 @@
+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: n12
+- dataset_name: Skin Segmentation
+- table_name: n12
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 3 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 245057
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 3.
+- target: role=target, type=categorical_ordinal_target. ordered=['1', '2'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['feature_1', 'feature_2', 'target'], ['feature_1', 'feature_1', 'target']]
+- fields_requiring_caution: ['target', 'feature_1', 'feature_2', 'feature_3']
+- source_url: https://archive.ics.uci.edu/dataset/229/skin+segmentation
+
+SQLite schema snapshot:
+{
+  "table_name": "n12",
+  "quoted_table_name": "\"n12\"",
+  "row_count": 245057,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "74",
+      "feature_2": "85",
+      "feature_3": "123",
+      "target": "1"
+    },
+    {
+      "feature_1": "73",
+      "feature_2": "84",
+      "feature_3": "122",
+      "target": "1"
+    },
+    {
+      "feature_1": "72",
+      "feature_2": "83",
+      "feature_3": "121",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    }
+  ]
+}
+
+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": "n12",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=feature_1, measure_col=feature_2.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "feature_1",
+    "measure_col": "feature_2",
+    "top_k": 19,
+    "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": 104.84,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, 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/n12/artifacts/v2q_n12_0090e33f694627e3/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40db-b400-7e81-bc06-7e0b2e9fdbd7"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"feature_1\\\", SUM(CAST(\\\"feature_2\\\" AS REAL)) AS total_measure\\nFROM \\\"n12\\\"\\nGROUP BY \\\"feature_1\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Applied the planned grouped-sum template with group_col=\\\"feature_1\\\" and measure_col=\\\"feature_2\\\". CAST is used because the schema stores numeric-looking fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":13456,"cached_input_tokens":12032,"output_tokens":500,"reasoning_output_tokens":396}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_0090e33f694627e3/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"feature_1\", SUM(CAST(\"feature_2\" AS REAL)) AS total_measure\nFROM \"n12\"\nGROUP BY \"feature_1\"\nORDER BY total_measure DESC;","notes":"Applied the planned grouped-sum template with group_col=\"feature_1\" and measure_col=\"feature_2\". CAST is used because the schema stores numeric-looking fields as TEXT in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_00999fc963621d8d/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 5099, "bytes_utf8": 5099, "lines": 144, "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": 626, "bytes_utf8": 626, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13588, "cached_input_tokens": 12032, "output_tokens": 375, "reasoning_output_tokens": 207}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_00999fc963621d8d/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": "n12",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n12_00999fc963621d8d",
+    "api_calls": 0,
+    "input_tokens": 13588,
+    "cached_input_tokens": 12032,
+    "output_tokens": 375,
+    "total_tokens": 13963,
+    "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": 14626.64,
+    "sql_execution_elapsed_ms_total": 113.92,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_00999fc963621d8d/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_00999fc963621d8d/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": "2026-05-19T15:43:00.397449+00:00",
+  "ended_at": "2026-05-19T15:43:15.024123+00:00",
+  "elapsed_ms": 14626.64,
+  "prompt_metrics": {
+    "chars": 5099,
+    "bytes_utf8": 5099,
+    "lines": 144,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1330,
+    "bytes_utf8": 1330,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 626,
+      "bytes_utf8": 626,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 13588,
+      "cached_input_tokens": 12032,
+      "output_tokens": 375,
+      "reasoning_output_tokens": 207
+    }
+  },
+  "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/n12/artifacts/v2q_n12_00999fc963621d8d/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,144 @@
+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: n12
+- dataset_name: Skin Segmentation
+- table_name: n12
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 3 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 245057
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 3.
+- target: role=target, type=categorical_ordinal_target. ordered=['1', '2'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['feature_1', 'feature_2', 'target'], ['feature_1', 'feature_1', 'target']]
+- fields_requiring_caution: ['target', 'feature_1', 'feature_2', 'feature_3']
+- source_url: https://archive.ics.uci.edu/dataset/229/skin+segmentation
+
+SQLite schema snapshot:
+{
+  "table_name": "n12",
+  "quoted_table_name": "\"n12\"",
+  "row_count": 245057,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "74",
+      "feature_2": "85",
+      "feature_3": "123",
+      "target": "1"
+    },
+    {
+      "feature_1": "73",
+      "feature_2": "84",
+      "feature_3": "122",
+      "target": "1"
+    },
+    {
+      "feature_1": "72",
+      "feature_2": "83",
+      "feature_3": "121",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    }
+  ]
+}
+
+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": "n12",
+  "question": "Use template Relative-to-Total Extreme Threshold to probe tail_mass_similarity with semantic role count_distribution. Focus on group_col=target, measure_col=feature_1.",
+  "planned_template_id": "tpl_tpch_relative_total_threshold",
+  "bindings": {
+    "group_col": "target",
+    "measure_col": "feature_1",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 100.24,
+    "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/n12/artifacts/v2q_n12_00999fc963621d8d/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e7-9a11-7000-a9e9-fb79f7517a91"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 14223. Please try again in 426ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_relative_total_threshold\\nWITH grouped AS (\\n    SELECT \\\"target\\\", SUM(CAST(\\\"feature_1\\\" AS REAL)) AS group_value\\n    FROM \\\"n12\\\"\\n    GROUP BY \\\"target\\\"\\n), total AS (\\n    SELECT SUM(group_value) AS total_value\\n    FROM grouped\\n)\\nSELECT g.\\\"target\\\", 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\":\"Used the required template with group_col=\\\"target\\\" and measure_col=\\\"feature_1\\\". CAST(\\\"feature_1\\\" AS REAL) is applied because the schema stores numeric-looking values as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":13588,"cached_input_tokens":12032,"output_tokens":375,"reasoning_output_tokens":207}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_00999fc963621d8d/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_relative_total_threshold\nWITH grouped AS (\n    SELECT \"target\", SUM(CAST(\"feature_1\" AS REAL)) AS group_value\n    FROM \"n12\"\n    GROUP BY \"target\"\n), total AS (\n    SELECT SUM(group_value) AS total_value\n    FROM grouped\n)\nSELECT g.\"target\", 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":"Used the required template with group_col=\"target\" and measure_col=\"feature_1\". CAST(\"feature_1\" AS REAL) is applied because the schema stores numeric-looking values as TEXT in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 4957, "bytes_utf8": 4957, "lines": 146, "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": 644, "bytes_utf8": 644, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13575, "cached_input_tokens": 12032, "output_tokens": 707, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/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": "n12",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n12_02831a2a3919d4f9",
+    "api_calls": 0,
+    "input_tokens": 13575,
+    "cached_input_tokens": 12032,
+    "output_tokens": 707,
+    "total_tokens": 14282,
+    "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": 18525.88,
+    "sql_execution_elapsed_ms_total": 228.32,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/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": "2026-05-19T15:33:57.457881+00:00",
+  "ended_at": "2026-05-19T15:34:15.983784+00:00",
+  "elapsed_ms": 18525.88,
+  "prompt_metrics": {
+    "chars": 4957,
+    "bytes_utf8": 4957,
+    "lines": 146,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1364,
+    "bytes_utf8": 1364,
+    "lines": 5,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 644,
+      "bytes_utf8": 644,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 13575,
+      "cached_input_tokens": 12032,
+      "output_tokens": 707,
+      "reasoning_output_tokens": 516
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,146 @@
+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: n12
+- dataset_name: Skin Segmentation
+- table_name: n12
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 3 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 245057
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 3.
+- target: role=target, type=categorical_ordinal_target. ordered=['1', '2'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['feature_1', 'feature_2', 'target'], ['feature_1', 'feature_1', 'target']]
+- fields_requiring_caution: ['target', 'feature_1', 'feature_2', 'feature_3']
+- source_url: https://archive.ics.uci.edu/dataset/229/skin+segmentation
+
+SQLite schema snapshot:
+{
+  "table_name": "n12",
+  "quoted_table_name": "\"n12\"",
+  "row_count": 245057,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "74",
+      "feature_2": "85",
+      "feature_3": "123",
+      "target": "1"
+    },
+    {
+      "feature_1": "73",
+      "feature_2": "84",
+      "feature_3": "122",
+      "target": "1"
+    },
+    {
+      "feature_1": "72",
+      "feature_2": "83",
+      "feature_3": "121",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n12",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=feature_3, measure_col=feature_3.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "feature_3",
+    "measure_col": "feature_3",
+    "item_col": "feature_1",
+    "top_k": 16,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 163.32,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40df-5154-7723-aabb-6f1384ef343f"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 14210. Please try again in 426ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT\\n  \\\"feature_3\\\",\\n  \\\"feature_1\\\",\\n  SUM(CAST(\\\"feature_3\\\" AS INTEGER)) AS total_measure,\\n  SUM(CAST(\\\"feature_3\\\" AS INTEGER)) * 100.0 / SUM(SUM(CAST(\\\"feature_3\\\" AS INTEGER))) OVER (PARTITION BY \\\"feature_3\\\") AS share_within_group\\nFROM \\\"n12\\\"\\nGROUP BY \\\"feature_3\\\", \\\"feature_1\\\"\\nORDER BY share_within_group DESC\\nLIMIT 16;\",\"notes\":\"Applied the provided Within-Group Share of Total template with group_col=\\\"feature_3\\\", item_col=\\\"feature_1\\\", and measure_col=\\\"feature_3\\\". Casted \\\"feature_3\\\" to INTEGER because the schema stores numeric fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":13575,"cached_input_tokens":12032,"output_tokens":707,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_02831a2a3919d4f9/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT\n  \"feature_3\",\n  \"feature_1\",\n  SUM(CAST(\"feature_3\" AS INTEGER)) AS total_measure,\n  SUM(CAST(\"feature_3\" AS INTEGER)) * 100.0 / SUM(SUM(CAST(\"feature_3\" AS INTEGER))) OVER (PARTITION BY \"feature_3\") AS share_within_group\nFROM \"n12\"\nGROUP BY \"feature_3\", \"feature_1\"\nORDER BY share_within_group DESC\nLIMIT 16;","notes":"Applied the provided Within-Group Share of Total template with group_col=\"feature_3\", item_col=\"feature_1\", and measure_col=\"feature_3\". Casted \"feature_3\" to INTEGER because the schema stores numeric fields as TEXT in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_05d3d81cb0182d3a/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 4550, "bytes_utf8": 4550, "lines": 144, "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": 401, "bytes_utf8": 401, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 13456, "cached_input_tokens": 12672, "output_tokens": 336, "reasoning_output_tokens": 226}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_05d3d81cb0182d3a/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": "n12",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n12_05d3d81cb0182d3a",
+    "api_calls": 0,
+    "input_tokens": 13456,
+    "cached_input_tokens": 12672,
+    "output_tokens": 336,
+    "total_tokens": 13792,
+    "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": 10523.92,
+    "sql_execution_elapsed_ms_total": 121.03,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_05d3d81cb0182d3a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/n12/artifacts/v2q_n12_05d3d81cb0182d3a/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": "2026-05-19T15:30:52.116748+00:00",
+  "ended_at": "2026-05-19T15:31:02.640706+00:00",
+  "elapsed_ms": 10523.92,
+  "prompt_metrics": {
+    "chars": 4550,
+    "bytes_utf8": 4550,
+    "lines": 144,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 755,
+    "bytes_utf8": 755,
+    "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": 401,
+      "bytes_utf8": 401,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 13456,
+      "cached_input_tokens": 12672,
+      "output_tokens": 336,
+      "reasoning_output_tokens": 226
+    }
+  },
+  "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/n12/artifacts/v2q_n12_05d3d81cb0182d3a/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,144 @@
+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: n12
+- dataset_name: Skin Segmentation
+- table_name: n12
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 3 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 245057
+- important_fields:
+- feature_1: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 1.
+- feature_2: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 2.
+- feature_3: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for feature 3.
+- target: role=target, type=categorical_ordinal_target. ordered=['1', '2'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['feature_1', 'feature_2', 'target'], ['feature_1', 'feature_1', 'target']]
+- fields_requiring_caution: ['target', 'feature_1', 'feature_2', 'feature_3']
+- source_url: https://archive.ics.uci.edu/dataset/229/skin+segmentation
+
+SQLite schema snapshot:
+{
+  "table_name": "n12",
+  "quoted_table_name": "\"n12\"",
+  "row_count": 245057,
+  "columns": [
+    {
+      "name": "feature_1",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_2",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "feature_3",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "feature_1": "74",
+      "feature_2": "85",
+      "feature_3": "123",
+      "target": "1"
+    },
+    {
+      "feature_1": "73",
+      "feature_2": "84",
+      "feature_3": "122",
+      "target": "1"
+    },
+    {
+      "feature_1": "72",
+      "feature_2": "83",
+      "feature_3": "121",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    },
+    {
+      "feature_1": "70",
+      "feature_2": "81",
+      "feature_3": "119",
+      "target": "1"
+    }
+  ]
+}
+
+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": "n12",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=feature_3, measure_col=feature_1.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "feature_3",
+    "measure_col": "feature_1",
+    "top_k": 16,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 100.24,
+    "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/n12/artifacts/v2q_n12_05d3d81cb0182d3a/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40dc-7d5a-7632-84fd-163e6d5a90c3"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"feature_3\\\", SUM(CAST(\\\"feature_1\\\" AS REAL)) AS total_measure\\nFROM \\\"n12\\\"\\nGROUP BY \\\"feature_3\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the required Grouped Numeric Sum template with group_col bound to \\\"feature_3\\\" and measure_col bound to \\\"feature_1\\\". CAST to REAL is added because the schema stores numeric fields as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":13456,"cached_input_tokens":12672,"output_tokens":336,"reasoning_output_tokens":226}}