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

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

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_0c8c55d78fa3d2db.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: c10
+-- 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_c10_0c8c55d78fa3d2db
+-- problem_id: v2p_c10_02d15dab3de77794
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "s1", SUM(CAST("s1" AS NUMERIC)) AS total_measure
+FROM "c10"
+GROUP BY "s1"
+ORDER BY total_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_150e25373331ab7e.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: 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_c10_150e25373331ab7e
+-- problem_id: v2p_c10_4601737b54944315
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "c2", "s3",
+       SUM(CAST("c3" AS REAL)) AS "total_measure",
+       SUM(CAST("c3" AS REAL)) * 100.0 / SUM(SUM(CAST("c3" AS REAL))) OVER (PARTITION BY "c2") AS "share_within_group"
+FROM "c10"
+GROUP BY "c2", "s3"
+ORDER BY "share_within_group" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_3bf2d02a0e3ba0b5.sql

@@ -0,0 +1,20 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: 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_c10_3bf2d02a0e3ba0b5
+-- problem_id: v2p_c10_77a80f3f1fd79566
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "c2", "s3",
+       SUM(CAST("c3" AS REAL)) AS "total_measure",
+       SUM(CAST("c3" AS REAL)) * 100.0 / SUM(SUM(CAST("c3" AS REAL))) OVER (PARTITION BY "c2") AS "share_within_group"
+FROM "c10"
+GROUP BY "c2", "s3"
+ORDER BY "share_within_group" DESC
+LIMIT 15;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_57dcf1ec83f9d50b.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: c10
+-- 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_c10_57dcf1ec83f9d50b
+-- problem_id: v2p_c10_a552459169e616ef
+-- realization_mode: deterministic
+-- source_kind: deterministic
+WITH grouped AS (
+    SELECT "s1" AS value_label, COUNT(*) AS support
+    FROM "c10"
+    GROUP BY "s1"
+)
+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/c10/sql/v2q_c10_64ca0079f85ff2dc.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: 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_c10_64ca0079f85ff2dc
+-- problem_id: v2p_c10_2d74a1d86d0f031e
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "s3", "c3",
+       SUM(CAST("s4" AS INTEGER)) AS total_measure,
+       SUM(CAST("s4" AS INTEGER)) * 100.0 / SUM(SUM(CAST("s4" AS INTEGER))) OVER (PARTITION BY "s3") AS share_within_group
+FROM "c10"
+GROUP BY "s3", "c3"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_6834ece4ea14b5f6.sql

@@ -0,0 +1,15 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_threshold_rarity_cdf
+-- query_record_id: v2q_c10_6834ece4ea14b5f6
+-- problem_id: v2p_c10_2bd53f44d2f67401
+-- realization_mode: agent
+-- source_kind: agent
+SELECT AVG(CASE WHEN CAST("s5" AS REAL) <= 3.25 THEN 1 ELSE 0 END) AS "empirical_cdf_at_threshold"
+FROM "c10";

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_7303da6d47482102.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: c10
+-- 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_c10_7303da6d47482102
+-- problem_id: v2p_c10_2e21e4f1d3ece7d2
+-- realization_mode: agent
+-- source_kind: agent
+SELECT
+  "s4",
+  "c4",
+  SUM(CAST("s5" AS REAL)) AS total_measure,
+  SUM(CAST("s5" AS REAL)) * 100.0 / SUM(SUM(CAST("s5" AS REAL))) OVER (PARTITION BY "s4") AS share_within_group
+FROM "c10"
+GROUP BY "s4", "c4"
+ORDER BY share_within_group DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_b0c331e4e07d5956.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: c10
+-- 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_c10_b0c331e4e07d5956
+-- problem_id: v2p_c10_f0154b5d7b0adaad
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "s1", SUM(CAST("c4" AS REAL)) AS group_value
+    FROM "c10"
+    GROUP BY "s1"
+), total AS (
+    SELECT SUM(group_value) AS total_value
+    FROM grouped
+)
+SELECT g."s1", 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/c10/sql/v2q_c10_ba8cf43a42128a91.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: c10
+-- family_id: subgroup_structure
+-- canonical_subitem_id: internal_profile_stability
+-- intended_facet_id: subgroup_conditional_contrast
+-- variant_semantic_role: collapsed_target_view
+-- template_id: tpl_h2o_group_sum
+-- query_record_id: v2q_c10_ba8cf43a42128a91
+-- problem_id: v2p_c10_3663d741630d026b
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "s3", SUM(CAST("s3" AS INTEGER)) AS total_measure
+FROM "c10"
+GROUP BY "s3"
+ORDER BY total_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_be136feed7ec761a.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: c10
+-- 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_c10_be136feed7ec761a
+-- problem_id: v2p_c10_05eb3b08974ecb6c
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "s4", SUM(CAST("s4" AS INTEGER)) AS total_measure
+FROM "c10"
+GROUP BY "s4"
+ORDER BY total_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_cdf07da8d4b3547b.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: c10
+-- 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_c10_cdf07da8d4b3547b
+-- problem_id: v2p_c10_ee461d6fadb1d5a4
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "c4", SUM(CAST("c4" AS INTEGER)) AS "total_measure"
+FROM "c10"
+GROUP BY "c4"
+ORDER BY "total_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_e95c606c8b51b765.sql

@@ -0,0 +1,59 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_a
+-- sql_source_dataset_id: c10
+-- family_id: 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_c10_e95c606c8b51b765
+-- problem_id: v2p_c10_d23e683e564e668e
+-- realization_mode: agent
+-- source_kind: agent
+WITH "ordered" AS (
+  SELECT
+    "s5",
+    CAST("c3" AS REAL) AS "measure_value",
+    ROW_NUMBER() OVER (
+      PARTITION BY "s5"
+      ORDER BY CAST("c3" AS REAL)
+    ) AS "rn",
+    COUNT(*) OVER (
+      PARTITION BY "s5"
+    ) AS "cnt"
+  FROM "c10"
+),
+"targets" AS (
+  SELECT DISTINCT
+    "s5",
+    (1.0 + 0.9 * ("cnt" - 1)) AS "target_pos",
+    CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) AS "lower_rn",
+    CASE
+      WHEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) < (1.0 + 0.9 * ("cnt" - 1))
+        THEN CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER) + 1
+      ELSE CAST((1.0 + 0.9 * ("cnt" - 1)) AS INTEGER)
+    END AS "upper_rn"
+  FROM "ordered"
+),
+"picked" AS (
+  SELECT
+    o."s5",
+    t."target_pos",
+    MAX(CASE WHEN o."rn" = t."lower_rn" THEN o."measure_value" END) AS "lower_val",
+    MAX(CASE WHEN o."rn" = t."upper_rn" THEN o."measure_value" END) AS "upper_val"
+  FROM "ordered" AS o
+  JOIN "targets" AS t
+    ON o."s5" = t."s5"
+  GROUP BY o."s5", t."target_pos"
+)
+SELECT
+  "s5",
+  CASE
+    WHEN "lower_val" IS NULL THEN NULL
+    WHEN "upper_val" IS NULL THEN "lower_val"
+    WHEN "target_pos" = CAST("target_pos" AS INTEGER) THEN "lower_val"
+    ELSE "lower_val" + ("target_pos" - CAST("target_pos" AS INTEGER)) * ("upper_val" - "lower_val")
+  END AS "percentile_measure"
+FROM "picked"
+ORDER BY "percentile_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_f6fe13b810157e4b.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: c10
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: contrastive_conditional_view
+-- template_id: tpl_m4_group_ratio_two_conditions
+-- query_record_id: v2q_c10_f6fe13b810157e4b
+-- problem_id: v2p_c10_712d3c61d5512ba8
+-- realization_mode: agent
+-- source_kind: agent
+WITH grouped AS (
+    SELECT "s4",
+           SUM(CASE WHEN "s3" = '2' THEN 1 ELSE 0 END) AS numerator_count,
+           SUM(CASE WHEN "s3" = '1' THEN 1 ELSE 0 END) AS denominator_count
+    FROM "c10"
+    GROUP BY "s4"
+)
+SELECT "s4",
+       CAST(numerator_count AS FLOAT) / NULLIF(denominator_count, 0) AS condition_ratio
+FROM grouped
+ORDER BY condition_ratio DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/c10/sql/v2q_c10_fb57cf013d307b6b.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: c10
+-- 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_c10_fb57cf013d307b6b
+-- problem_id: v2p_c10_13360e72f3bb3704
+-- realization_mode: agent
+-- source_kind: agent
+WITH "buckets" AS (
+    SELECT
+        "s1",
+        NTILE(10) OVER (ORDER BY CAST("s1" AS INTEGER) DESC) AS "tail_bucket"
+    FROM "c10"
+)
+SELECT "s1"
+FROM "buckets"
+WHERE "tail_bucket" = 1
+ORDER BY CAST("s1" AS INTEGER) DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9605, "bytes_utf8": 9605, "lines": 268, "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": 428, "bytes_utf8": 428, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14739, "cached_input_tokens": 13696, "output_tokens": 485, "reasoning_output_tokens": 367}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0143799233bedfc5",
+    "api_calls": 0,
+    "input_tokens": 14739,
+    "cached_input_tokens": 13696,
+    "output_tokens": 485,
+    "total_tokens": 15224,
+    "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": 12525.53,
+    "sql_execution_elapsed_ms_total": 6.62,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/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": "2026-05-19T15:43:07.130754+00:00",
+  "ended_at": "2026-05-19T15:43:19.656321+00:00",
+  "elapsed_ms": 12525.53,
+  "prompt_metrics": {
+    "chars": 9605,
+    "bytes_utf8": 9605,
+    "lines": 268,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 803,
+    "bytes_utf8": 803,
+    "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": 428,
+      "bytes_utf8": 428,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14739,
+      "cached_input_tokens": 13696,
+      "output_tokens": 485,
+      "reasoning_output_tokens": 367
+    }
+  },
+  "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/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,268 @@
+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: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_c2_filtered_group_count_2d",
+    "template_name": "Filtered Two-Dimensional Group Count",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col",
+      "group_col_2",
+      "predicate_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=last_new_job.",
+  "planned_template_id": "tpl_c2_filtered_group_count_2d",
+  "bindings": {
+    "group_col": "experience",
+    "group_col_2": "last_new_job",
+    "predicate_col": "last_new_job",
+    "predicate_op": "=",
+    "predicate_value": "4",
+    "top_k": 13,
+    "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": 88.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40e7-b47d-7041-bc2f-fac52d830844"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"last_new_job\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE \\\"last_new_job\\\" = '4'\\nGROUP BY \\\"experience\\\", \\\"last_new_job\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the planned filtered two-dimensional group count template with \\\"experience\\\" and \\\"last_new_job\\\" as the grouping columns, restricted to rows where \\\"last_new_job\\\" = '4'.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14739,"cached_input_tokens":13696,"output_tokens":485,"reasoning_output_tokens":367}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0143799233bedfc5/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"last_new_job\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE \"last_new_job\" = '4'\nGROUP BY \"experience\", \"last_new_job\"\nORDER BY \"row_count\" DESC;","notes":"Uses the planned filtered two-dimensional group count template with \"experience\" and \"last_new_job\" as the grouping columns, restricted to rows where \"last_new_job\" = '4'."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9380, "bytes_utf8": 9380, "lines": 264, "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": 440, "bytes_utf8": 440, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14659, "cached_input_tokens": 12032, "output_tokens": 311, "reasoning_output_tokens": 197}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/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": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_03d55ad136bfea10",
+    "api_calls": 0,
+    "input_tokens": 14659,
+    "cached_input_tokens": 12032,
+    "output_tokens": 311,
+    "total_tokens": 14970,
+    "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": 8392.9,
+    "sql_execution_elapsed_ms_total": 32.73,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:08:33.110209+00:00",
+  "ended_at": "2026-05-19T16:08:41.503135+00:00",
+  "elapsed_ms": 8392.9,
+  "prompt_metrics": {
+    "chars": 9380,
+    "bytes_utf8": 9380,
+    "lines": 264,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 799,
+    "bytes_utf8": 799,
+    "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": 440,
+      "bytes_utf8": 440,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14659,
+      "cached_input_tokens": 12032,
+      "output_tokens": 311,
+      "reasoning_output_tokens": 197
+    }
+  },
+  "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/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,264 @@
+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: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=experience, measure_col=enrollee_id.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "experience",
+    "measure_col": "enrollee_id",
+    "top_k": 18,
+    "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": 22283.62,
+    "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/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fe-fd64-7820-8a73-52d63a1bdeb1"}
+{"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  \\\"experience\\\",\\n  AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"experience\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"experience\\\" and measure_col=\\\"enrollee_id\\\". CAST is used because \\\"enrollee_id\\\" is stored as TEXT in the schema but contains numeric-like values.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14659,"cached_input_tokens":12032,"output_tokens":311,"reasoning_output_tokens":197}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_03d55ad136bfea10/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"experience\",\n  AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"experience\") AS avg_measure\nFROM \"m9\"\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col=\"experience\" and measure_col=\"enrollee_id\". CAST is used because \"enrollee_id\" is stored as TEXT in the schema but contains numeric-like values."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9618, "bytes_utf8": 9618, "lines": 268, "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": 498, "bytes_utf8": 498, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14740, "cached_input_tokens": 13696, "output_tokens": 641, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0b646637c9fd1427",
+    "api_calls": 0,
+    "input_tokens": 14740,
+    "cached_input_tokens": 13696,
+    "output_tokens": 641,
+    "total_tokens": 15381,
+    "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": 19037.11,
+    "sql_execution_elapsed_ms_total": 9.96,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/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": "2026-05-19T15:43:19.672520+00:00",
+  "ended_at": "2026-05-19T15:43:38.709669+00:00",
+  "elapsed_ms": 19037.11,
+  "prompt_metrics": {
+    "chars": 9618,
+    "bytes_utf8": 9618,
+    "lines": 268,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1206,
+    "bytes_utf8": 1206,
+    "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": 498,
+      "bytes_utf8": 498,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14740,
+      "cached_input_tokens": 13696,
+      "output_tokens": 641,
+      "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/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,268 @@
+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: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_c2_filtered_group_count_2d",
+    "template_name": "Filtered Two-Dimensional Group Count",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col",
+      "group_col_2",
+      "predicate_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "question": "Use template Filtered Two-Dimensional Group Count to probe slice_level_consistency with semantic role count_distribution. Focus on group_col=experience, group_col_2=training_hours.",
+  "planned_template_id": "tpl_c2_filtered_group_count_2d",
+  "bindings": {
+    "group_col": "experience",
+    "group_col_2": "training_hours",
+    "predicate_col": "training_hours",
+    "predicate_op": ">=",
+    "predicate_value": 88.0,
+    "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": 25169.75,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {group_col_2}, COUNT(*) AS row_count\nFROM {table}\nWHERE {predicate_col} {predicate_op} {predicate_value}\nGROUP BY {group_col}, {group_col_2}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,5 @@
+{"type":"thread.started","thread_id":"019e40e7-e57d-7103-8002-64c3f0e0a52c"}
+{"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 15375. Please try again in 461ms. 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_c2_filtered_group_count_2d\\nSELECT \\\"experience\\\", \\\"training_hours\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nWHERE CAST(\\\"training_hours\\\" AS REAL) >= 88.0\\nGROUP BY \\\"experience\\\", \\\"training_hours\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Used the provided filtered two-dimensional group-count template with \\\"experience\\\" and \\\"training_hours\\\". CAST(\\\"training_hours\\\" AS REAL) is needed because the schema stores it as TEXT while the predicate value is numeric.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14740,"cached_input_tokens":13696,"output_tokens":641,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0b646637c9fd1427/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_c2_filtered_group_count_2d\nSELECT \"experience\", \"training_hours\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nWHERE CAST(\"training_hours\" AS REAL) >= 88.0\nGROUP BY \"experience\", \"training_hours\"\nORDER BY \"row_count\" DESC;","notes":"Used the provided filtered two-dimensional group-count template with \"experience\" and \"training_hours\". CAST(\"training_hours\" AS REAL) is needed because the schema stores it as TEXT while the predicate value is numeric."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9405, "bytes_utf8": 9405, "lines": 264, "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": 637, "bytes_utf8": 637, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14666, "cached_input_tokens": 12032, "output_tokens": 682, "reasoning_output_tokens": 516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/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": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_0d1d5d0b6b0ef65a",
+    "api_calls": 0,
+    "input_tokens": 14666,
+    "cached_input_tokens": 12032,
+    "output_tokens": 682,
+    "total_tokens": 15348,
+    "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": 12741.36,
+    "sql_execution_elapsed_ms_total": 66.76,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/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": "2026-05-19T16:06:31.519396+00:00",
+  "ended_at": "2026-05-19T16:06:44.260783+00:00",
+  "elapsed_ms": 12741.36,
+  "prompt_metrics": {
+    "chars": 9405,
+    "bytes_utf8": 9405,
+    "lines": 264,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1011,
+    "bytes_utf8": 1011,
+    "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": 637,
+      "bytes_utf8": 637,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14666,
+      "cached_input_tokens": 12032,
+      "output_tokens": 682,
+      "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/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,264 @@
+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: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=city_development_index, measure_col=enrollee_id.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "city_development_index",
+    "measure_col": "enrollee_id",
+    "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": 25169.75,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT 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/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fd-226e-7e53-8e85-3ca82cf2b29b"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT \\\"city_development_index\\\",\\n       AVG(CAST(\\\"enrollee_id\\\" AS REAL)) OVER (PARTITION BY \\\"city_development_index\\\") AS avg_measure\\nFROM \\\"m9\\\"\\nWHERE \\\"city_development_index\\\" IS NOT NULL\\n  AND \\\"city_development_index\\\" <> ''\\n  AND \\\"enrollee_id\\\" IS NOT NULL\\n  AND \\\"enrollee_id\\\" <> ''\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col \\\"city_development_index\\\" and measure_col \\\"enrollee_id\\\". Cast the text-valued identifier to REAL for SQLite averaging and filtered empty values to keep the view stable.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14666,"cached_input_tokens":12032,"output_tokens":682,"reasoning_output_tokens":516}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_0d1d5d0b6b0ef65a/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT \"city_development_index\",\n       AVG(CAST(\"enrollee_id\" AS REAL)) OVER (PARTITION BY \"city_development_index\") AS avg_measure\nFROM \"m9\"\nWHERE \"city_development_index\" IS NOT NULL\n  AND \"city_development_index\" <> ''\n  AND \"enrollee_id\" IS NOT NULL\n  AND \"enrollee_id\" <> ''\nORDER BY avg_measure DESC;","notes":"Applied the Window Partition Average template with group_col \"city_development_index\" and measure_col \"enrollee_id\". Cast the text-valued identifier to REAL for SQLite averaging and filtered empty values to keep the view stable."}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9223, "bytes_utf8": 9223, "lines": 262, "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": 315, "bytes_utf8": 315, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14624, "cached_input_tokens": 13696, "output_tokens": 327, "reasoning_output_tokens": 244}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/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": "m9",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_m9_126d5fc6d858efd2",
+    "api_calls": 0,
+    "input_tokens": 14624,
+    "cached_input_tokens": 13696,
+    "output_tokens": 327,
+    "total_tokens": 14951,
+    "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": 11424.96,
+    "sql_execution_elapsed_ms_total": 8.06,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:32:07.906876+00:00",
+  "ended_at": "2026-05-19T15:32:19.331865+00:00",
+  "elapsed_ms": 11424.96,
+  "prompt_metrics": {
+    "chars": 9223,
+    "bytes_utf8": 9223,
+    "lines": 262,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 665,
+    "bytes_utf8": 665,
+    "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": 315,
+      "bytes_utf8": 315,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14624,
+      "cached_input_tokens": 13696,
+      "output_tokens": 327,
+      "reasoning_output_tokens": 244
+    }
+  },
+  "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/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,262 @@
+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: m9
+- dataset_name: Hr Analytics Job Change Of Data Scientists
+- table_name: m9
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 13 feature columns and target `target`.
+- task_type: classification
+- target_column: target
+- main_row_count: 19158
+- important_fields:
+- enrollee_id: role=feature, type=identifier_numeric. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for enrollee id.
+- city: role=feature, type=categorical_nominal. tags=['condition_candidate'] desc=Categorical field for city.
+- city_development_index: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for city development index.
+- gender: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for gender.
+- relevent_experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate'] desc=Categorical field for relevent experience.
+- enrolled_university: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for enrolled university.
+- education_level: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for education level.
+- major_discipline: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for major discipline.
+- experience: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for experience.
+- company_size: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company size.
+- company_type: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for company type.
+- last_new_job: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for last new job.
+- training_hours: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure', 'high_cardinality_candidate'] desc=Numeric field for training hours.
+- target: role=target, type=categorical_ordinal_target. ordered=['0.0', '1.0'] tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for target.
+- useful_field_combinations: [['city_development_index', 'gender', 'target'], ['city_development_index', 'city_development_index', 'target'], ['city_development_index', 'city', 'target']]
+- fields_requiring_caution: ['target', 'training_hours', 'gender', 'enrolled_university', 'education_level']
+- source_url: https://www.kaggle.com/datasets/arashnic/hr-analytics-job-change-of-data-scientists
+
+SQLite schema snapshot:
+{
+  "table_name": "m9",
+  "quoted_table_name": "\"m9\"",
+  "row_count": 19158,
+  "columns": [
+    {
+      "name": "enrollee_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "city_development_index",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "gender",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "relevent_experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "enrolled_university",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "education_level",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "major_discipline",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "experience",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_size",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "company_type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "last_new_job",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "training_hours",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "target",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "enrollee_id": "8949",
+      "city": "city_103",
+      "city_development_index": "0.92",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "1",
+      "training_hours": "36",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "29725",
+      "city": "city_40",
+      "city_development_index": "0.7759999999999999",
+      "gender": "Male",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "15",
+      "company_size": "50-99",
+      "company_type": "Pvt Ltd",
+      "last_new_job": ">4",
+      "training_hours": "47",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "11561",
+      "city": "city_21",
+      "city_development_index": "0.624",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "Full time course",
+      "education_level": "Graduate",
+      "major_discipline": "STEM",
+      "experience": "5",
+      "company_size": "",
+      "company_type": "",
+      "last_new_job": "never",
+      "training_hours": "83",
+      "target": "0.0"
+    },
+    {
+      "enrollee_id": "33241",
+      "city": "city_115",
+      "city_development_index": "0.789",
+      "gender": "",
+      "relevent_experience": "No relevent experience",
+      "enrolled_university": "",
+      "education_level": "Graduate",
+      "major_discipline": "Business Degree",
+      "experience": "<1",
+      "company_size": "",
+      "company_type": "Pvt Ltd",
+      "last_new_job": "never",
+      "training_hours": "52",
+      "target": "1.0"
+    },
+    {
+      "enrollee_id": "666",
+      "city": "city_162",
+      "city_development_index": "0.767",
+      "gender": "Male",
+      "relevent_experience": "Has relevent experience",
+      "enrolled_university": "no_enrollment",
+      "education_level": "Masters",
+      "major_discipline": "STEM",
+      "experience": ">20",
+      "company_size": "50-99",
+      "company_type": "Funded Startup",
+      "last_new_job": "4",
+      "training_hours": "8",
+      "target": "0.0"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "m9",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=city_development_index.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "city_development_index",
+    "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": 25169.75,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40dd-a56e-7bd1-b115-35267fa6b027"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"city_development_index\\\", COUNT(*) AS \\\"row_count\\\"\\nFROM \\\"m9\\\"\\nGROUP BY \\\"city_development_index\\\"\\nORDER BY \\\"row_count\\\" DESC;\",\"notes\":\"Uses the planned grouped-count template with group_col bound to city_development_index on the single table m9.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14624,"cached_input_tokens":13696,"output_tokens":327,"reasoning_output_tokens":244}}

Query/sql/v2/runs/v2_cli_20260502_081223_a/m9/artifacts/v2q_m9_126d5fc6d858efd2/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"city_development_index\", COUNT(*) AS \"row_count\"\nFROM \"m9\"\nGROUP BY \"city_development_index\"\nORDER BY \"row_count\" DESC;","notes":"Uses the planned grouped-count template with group_col bound to city_development_index on the single table m9."}