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Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9353, "bytes_utf8": 9355, "lines": 240, "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": 2083, "bytes_utf8": 2083, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14683, "cached_input_tokens": 12032, "output_tokens": 2471, "reasoning_output_tokens": 1780}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/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": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_1348349818597a09",
+    "api_calls": 0,
+    "input_tokens": 14683,
+    "cached_input_tokens": 12032,
+    "output_tokens": 2471,
+    "total_tokens": 17154,
+    "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": 37690.08,
+    "sql_execution_elapsed_ms_total": 60.81,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/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": "2026-05-19T15:48:48.942115+00:00",
+  "ended_at": "2026-05-19T15:49:26.632237+00:00",
+  "elapsed_ms": 37690.08,
+  "prompt_metrics": {
+    "chars": 9353,
+    "bytes_utf8": 9355,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2800,
+    "bytes_utf8": 2800,
+    "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": 2083,
+      "bytes_utf8": 2083,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14683,
+      "cached_input_tokens": 12032,
+      "output_tokens": 2471,
+      "reasoning_output_tokens": 1780
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+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: c17
+- dataset_name: Netflix Shows
+- table_name: c17
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `type`.
+- task_type: classification
+- target_column: type
+- main_row_count: 8807
+- important_fields:
+- show_id: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for show id.
+- type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for type.
+- title: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for title.
+- director: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for director.
+- cast: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate', 'missingness_candidate'] desc=Identifier-like field for cast.
+- country: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for country.
+- date_added: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for date added.
+- release_year: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for release year.
+- rating: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for rating.
+- duration: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for duration.
+- listed_in: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude'] desc=Text field for listed in.
+- description: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for description.
+- useful_field_combinations: [['type', 'release_year', 'type'], ['release_year', 'rating', 'type']]
+- fields_requiring_caution: ['type', 'director', 'country', 'date_added', 'cast']
+- source_url: https://www.kaggle.com/datasets/shivamb/netflix-shows
+
+SQLite schema snapshot:
+{
+  "table_name": "c17",
+  "quoted_table_name": "\"c17\"",
+  "row_count": 8807,
+  "columns": [
+    {
+      "name": "show_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "title",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "director",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "cast",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "country",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "date_added",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "release_year",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "listed_in",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "description",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "show_id": "s1",
+      "type": "Movie",
+      "title": "Dick Johnson Is Dead",
+      "director": "Kirsten Johnson",
+      "cast": "",
+      "country": "United States",
+      "date_added": "September 25, 2021",
+      "release_year": "2020",
+      "rating": "PG-13",
+      "duration": "90 min",
+      "listed_in": "Documentaries",
+      "description": "As her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable."
+    },
+    {
+      "show_id": "s2",
+      "type": "TV Show",
+      "title": "Blood & Water",
+      "director": "",
+      "cast": "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick Mofokeng",
+      "country": "South Africa",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, TV Dramas, TV Mysteries",
+      "description": "After crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth."
+    },
+    {
+      "show_id": "s3",
+      "type": "TV Show",
+      "title": "Ganglands",
+      "director": "Julien Leclercq",
+      "cast": "Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary Diombera",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Crime TV Shows, International TV Shows, TV Action & Adventure",
+      "description": "To protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war."
+    },
+    {
+      "show_id": "s4",
+      "type": "TV Show",
+      "title": "Jailbirds New Orleans",
+      "director": "",
+      "cast": "",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Docuseries, Reality TV",
+      "description": "Feuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series."
+    },
+    {
+      "show_id": "s5",
+      "type": "TV Show",
+      "title": "Kota Factory",
+      "director": "",
+      "cast": "Mayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun Kumar",
+      "country": "India",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, Romantic TV Shows, TV Comedies",
+      "description": "In a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life."
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "c17",
+  "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=rating, measure_col=release_year.",
+  "planned_template_id": "tpl_grouped_percentile_point",
+  "bindings": {
+    "group_col": "rating",
+    "measure_col": "release_year",
+    "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": 2018.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40ec-ebb0-7c61-80a8-f70c469390fb"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    CAST(NULLIF(\\\"release_year\\\", '') AS INTEGER) AS \\\"release_year\\\"\\n  FROM \\\"c17\\\"\\n  WHERE NULLIF(\\\"rating\\\", '') IS NOT NULL\\n    AND NULLIF(\\\"release_year\\\", '') IS NOT NULL\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    \\\"release_year\\\",\\n    ROW_NUMBER() OVER (PARTITION BY \\\"rating\\\" ORDER BY \\\"release_year\\\") AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"rating\\\") AS \\\"cnt\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"pos\\\" AS (\\n  SELECT DISTINCT\\n    \\\"rating\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) AS \\\"lower_rn\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) + CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) THEN 1\\n      ELSE 0\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    \\\"p\\\".\\\"rating\\\",\\n    \\\"p\\\".\\\"pos\\\",\\n    \\\"p\\\".\\\"lower_rn\\\",\\n    \\\"p\\\".\\\"upper_rn\\\",\\n    MAX(CASE WHEN \\\"r\\\".\\\"rn\\\" = \\\"p\\\".\\\"lower_rn\\\" THEN \\\"r\\\".\\\"release_year\\\" END) AS \\\"lower_value\\\",\\n    MAX(CASE WHEN \\\"r\\\".\\\"rn\\\" = \\\"p\\\".\\\"upper_rn\\\" THEN \\\"r\\\".\\\"release_year\\\" END) AS \\\"upper_value\\\"\\n  FROM \\\"pos\\\" AS \\\"p\\\"\\n  JOIN \\\"ranked\\\" AS \\\"r\\\"\\n    ON \\\"r\\\".\\\"rating\\\" = \\\"p\\\".\\\"rating\\\"\\n  GROUP BY\\n    \\\"p\\\".\\\"rating\\\",\\n    \\\"p\\\".\\\"pos\\\",\\n    \\\"p\\\".\\\"lower_rn\\\",\\n    \\\"p\\\".\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"rating\\\",\\n  CASE\\n    WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_value\\\"\\n    ELSE \\\"lower_value\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC, \\\"rating\\\" ASC;\",\"notes\":\"SQLite does not support PERCENTILE_CONT, so this emulates the 0.9 continuous percentile per \\\"rating\\\" using window functions and linear interpolation. Empty \\\"rating\\\" and \\\"release_year\\\" values are excluded, \\\"release_year\\\" is cast from TEXT to INTEGER, and groups smaller than 5 rows are filtered out using the provided minimum group size.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14683,"cached_input_tokens":12032,"output_tokens":2471,"reasoning_output_tokens":1780}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n  SELECT\n    \"rating\",\n    CAST(NULLIF(\"release_year\", '') AS INTEGER) AS \"release_year\"\n  FROM \"c17\"\n  WHERE NULLIF(\"rating\", '') IS NOT NULL\n    AND NULLIF(\"release_year\", '') IS NOT NULL\n),\n\"ranked\" AS (\n  SELECT\n    \"rating\",\n    \"release_year\",\n    ROW_NUMBER() OVER (PARTITION BY \"rating\" ORDER BY \"release_year\") AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"rating\") AS \"cnt\"\n  FROM \"base\"\n),\n\"pos\" AS (\n  SELECT DISTINCT\n    \"rating\",\n    (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n    CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) AS \"lower_rn\",\n    CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) + CASE\n      WHEN (1.0 + 0.9 * (\"cnt\" - 1)) > CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) THEN 1\n      ELSE 0\n    END AS \"upper_rn\"\n  FROM \"ranked\"\n  WHERE \"cnt\" >= 5\n),\n\"picked\" AS (\n  SELECT\n    \"p\".\"rating\",\n    \"p\".\"pos\",\n    \"p\".\"lower_rn\",\n    \"p\".\"upper_rn\",\n    MAX(CASE WHEN \"r\".\"rn\" = \"p\".\"lower_rn\" THEN \"r\".\"release_year\" END) AS \"lower_value\",\n    MAX(CASE WHEN \"r\".\"rn\" = \"p\".\"upper_rn\" THEN \"r\".\"release_year\" END) AS \"upper_value\"\n  FROM \"pos\" AS \"p\"\n  JOIN \"ranked\" AS \"r\"\n    ON \"r\".\"rating\" = \"p\".\"rating\"\n  GROUP BY\n    \"p\".\"rating\",\n    \"p\".\"pos\",\n    \"p\".\"lower_rn\",\n    \"p\".\"upper_rn\"\n)\nSELECT\n  \"rating\",\n  CASE\n    WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_value\"\n    ELSE \"lower_value\" + (\"pos\" - \"lower_rn\") * (\"upper_value\" - \"lower_value\")\n  END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC, \"rating\" ASC;","notes":"SQLite does not support PERCENTILE_CONT, so this emulates the 0.9 continuous percentile per \"rating\" using window functions and linear interpolation. Empty \"rating\" and \"release_year\" values are excluded, \"release_year\" is cast from TEXT to INTEGER, and groups smaller than 5 rows are filtered out using the provided minimum group size."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=rating, measure_col=release_year.
+Result preview: [{"rating": "TV-14", "percentile_measure": 2020.0}, {"rating": "TV-G", "percentile_measure": 2020.0}, {"rating": "TV-MA", "percentile_measure": 2020.0}, {"rating": "TV-PG", "percentile_measure": 2020.0}, {"rating": "TV-Y", "percentile_measure": 2020.0}]

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/generated_sql.sql

@@ -0,0 +1,66 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_b
+-- sql_source_dataset_id: c17
+-- 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_c17_1348349818597a09
+-- problem_id: v2p_c17_c015a09048747e0b
+-- realization_mode: agent
+-- source_kind: agent
+WITH "base" AS (
+  SELECT
+    "rating",
+    CAST(NULLIF("release_year", '') AS INTEGER) AS "release_year"
+  FROM "c17"
+  WHERE NULLIF("rating", '') IS NOT NULL
+    AND NULLIF("release_year", '') IS NOT NULL
+),
+"ranked" AS (
+  SELECT
+    "rating",
+    "release_year",
+    ROW_NUMBER() OVER (PARTITION BY "rating" ORDER BY "release_year") AS "rn",
+    COUNT(*) OVER (PARTITION BY "rating") AS "cnt"
+  FROM "base"
+),
+"pos" AS (
+  SELECT DISTINCT
+    "rating",
+    (1.0 + 0.9 * ("cnt" - 1)) AS "pos",
+    CAST(1.0 + 0.9 * ("cnt" - 1) AS INTEGER) AS "lower_rn",
+    CAST(1.0 + 0.9 * ("cnt" - 1) AS INTEGER) + CASE
+      WHEN (1.0 + 0.9 * ("cnt" - 1)) > CAST(1.0 + 0.9 * ("cnt" - 1) AS INTEGER) THEN 1
+      ELSE 0
+    END AS "upper_rn"
+  FROM "ranked"
+  WHERE "cnt" >= 5
+),
+"picked" AS (
+  SELECT
+    "p"."rating",
+    "p"."pos",
+    "p"."lower_rn",
+    "p"."upper_rn",
+    MAX(CASE WHEN "r"."rn" = "p"."lower_rn" THEN "r"."release_year" END) AS "lower_value",
+    MAX(CASE WHEN "r"."rn" = "p"."upper_rn" THEN "r"."release_year" END) AS "upper_value"
+  FROM "pos" AS "p"
+  JOIN "ranked" AS "r"
+    ON "r"."rating" = "p"."rating"
+  GROUP BY
+    "p"."rating",
+    "p"."pos",
+    "p"."lower_rn",
+    "p"."upper_rn"
+)
+SELECT
+  "rating",
+  CASE
+    WHEN "lower_rn" = "upper_rn" THEN "lower_value"
+    ELSE "lower_value" + ("pos" - "lower_rn") * ("upper_value" - "lower_value")
+  END AS "percentile_measure"
+FROM "picked"
+ORDER BY "percentile_measure" DESC, "rating" ASC;

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_grouped_percentile_point\nWITH \"base\" AS (\n  SELECT\n    \"rating\",\n    CAST(NULLIF(\"release_year\", '') AS INTEGER) AS \"release_year\"\n  FROM \"c17\"\n  WHERE NULLIF(\"rating\", '') IS NOT NULL\n    AND NULLIF(\"release_year\", '') IS NOT NULL\n),\n\"ranked\" AS (\n  SELECT\n    \"rating\",\n    \"release_year\",\n    ROW_NUMBER() OVER (PARTITION BY \"rating\" ORDER BY \"release_year\") AS \"rn\",\n    COUNT(*) OVER (PARTITION BY \"rating\") AS \"cnt\"\n  FROM \"base\"\n),\n\"pos\" AS (\n  SELECT DISTINCT\n    \"rating\",\n    (1.0 + 0.9 * (\"cnt\" - 1)) AS \"pos\",\n    CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) AS \"lower_rn\",\n    CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) + CASE\n      WHEN (1.0 + 0.9 * (\"cnt\" - 1)) > CAST(1.0 + 0.9 * (\"cnt\" - 1) AS INTEGER) THEN 1\n      ELSE 0\n    END AS \"upper_rn\"\n  FROM \"ranked\"\n  WHERE \"cnt\" >= 5\n),\n\"picked\" AS (\n  SELECT\n    \"p\".\"rating\",\n    \"p\".\"pos\",\n    \"p\".\"lower_rn\",\n    \"p\".\"upper_rn\",\n    MAX(CASE WHEN \"r\".\"rn\" = \"p\".\"lower_rn\" THEN \"r\".\"release_year\" END) AS \"lower_value\",\n    MAX(CASE WHEN \"r\".\"rn\" = \"p\".\"upper_rn\" THEN \"r\".\"release_year\" END) AS \"upper_value\"\n  FROM \"pos\" AS \"p\"\n  JOIN \"ranked\" AS \"r\"\n    ON \"r\".\"rating\" = \"p\".\"rating\"\n  GROUP BY\n    \"p\".\"rating\",\n    \"p\".\"pos\",\n    \"p\".\"lower_rn\",\n    \"p\".\"upper_rn\"\n)\nSELECT\n  \"rating\",\n  CASE\n    WHEN \"lower_rn\" = \"upper_rn\" THEN \"lower_value\"\n    ELSE \"lower_value\" + (\"pos\" - \"lower_rn\") * (\"upper_value\" - \"lower_value\")\n  END AS \"percentile_measure\"\nFROM \"picked\"\nORDER BY \"percentile_measure\" DESC, \"rating\" ASC;", "result": "{\"query\": \"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    CAST(NULLIF(\\\"release_year\\\", '') AS INTEGER) AS \\\"release_year\\\"\\n  FROM \\\"c17\\\"\\n  WHERE NULLIF(\\\"rating\\\", '') IS NOT NULL\\n    AND NULLIF(\\\"release_year\\\", '') IS NOT NULL\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    \\\"release_year\\\",\\n    ROW_NUMBER() OVER (PARTITION BY \\\"rating\\\" ORDER BY \\\"release_year\\\") AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"rating\\\") AS \\\"cnt\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"pos\\\" AS (\\n  SELECT DISTINCT\\n    \\\"rating\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) AS \\\"lower_rn\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) + CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) THEN 1\\n      ELSE 0\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    \\\"p\\\".\\\"rating\\\",\\n    \\\"p\\\".\\\"pos\\\",\\n    \\\"p\\\".\\\"lower_rn\\\",\\n    \\\"p\\\".\\\"upper_rn\\\",\\n    MAX(CASE WHEN \\\"r\\\".\\\"rn\\\" = \\\"p\\\".\\\"lower_rn\\\" THEN \\\"r\\\".\\\"release_year\\\" END) AS \\\"lower_value\\\",\\n    MAX(CASE WHEN \\\"r\\\".\\\"rn\\\" = \\\"p\\\".\\\"upper_rn\\\" THEN \\\"r\\\".\\\"release_year\\\" END) AS \\\"upper_value\\\"\\n  FROM \\\"pos\\\" AS \\\"p\\\"\\n  JOIN \\\"ranked\\\" AS \\\"r\\\"\\n    ON \\\"r\\\".\\\"rating\\\" = \\\"p\\\".\\\"rating\\\"\\n  GROUP BY\\n    \\\"p\\\".\\\"rating\\\",\\n    \\\"p\\\".\\\"pos\\\",\\n    \\\"p\\\".\\\"lower_rn\\\",\\n    \\\"p\\\".\\\"upper_rn\\\"\\n)\\nSELECT\\n  \\\"rating\\\",\\n  CASE\\n    WHEN \\\"lower_rn\\\" = \\\"upper_rn\\\" THEN \\\"lower_value\\\"\\n    ELSE \\\"lower_value\\\" + (\\\"pos\\\" - \\\"lower_rn\\\") * (\\\"upper_value\\\" - \\\"lower_value\\\")\\n  END AS \\\"percentile_measure\\\"\\nFROM \\\"picked\\\"\\nORDER BY \\\"percentile_measure\\\" DESC, \\\"rating\\\" ASC;\", \"columns\": [\"rating\", \"percentile_measure\"], \"rows\": [{\"rating\": \"TV-14\", \"percentile_measure\": 2020.0}, {\"rating\": \"TV-G\", \"percentile_measure\": 2020.0}, {\"rating\": \"TV-MA\", \"percentile_measure\": 2020.0}, {\"rating\": \"TV-PG\", \"percentile_measure\": 2020.0}, {\"rating\": \"TV-Y\", \"percentile_measure\": 2020.0}, {\"rating\": \"TV-Y7\", \"percentile_measure\": 2020.0}, {\"rating\": \"PG\", \"percentile_measure\": 2019.0}, {\"rating\": \"PG-13\", \"percentile_measure\": 2019.0}, {\"rating\": \"R\", \"percentile_measure\": 2019.0}, {\"rating\": \"G\", \"percentile_measure\": 2017}, {\"rating\": \"TV-Y7-FV\", \"percentile_measure\": 2017.0}, {\"rating\": \"NR\", \"percentile_measure\": 2016.0}], \"row_count_returned\": 12, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 60.81}"}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_b",
+  "dataset_id": "c17",
+  "started_at": "2026-05-19T15:48:48.939552+00:00",
+  "ended_at": "2026-05-19T15:49:26.698109+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c17_1348349818597a09",
+    "problem_id": "v2p_c17_c015a09048747e0b",
+    "dataset_id": "c17",
+    "template_id": "tpl_grouped_percentile_point",
+    "template_name": "Grouped Percentile Point",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_concentration_consistency",
+    "intended_facet_id": "rare_target_concentration",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Grouped Percentile Point to probe tail_concentration_consistency with semantic role ranked_signal_view. Focus on group_col=rating, measure_col=release_year.",
+    "bindings": {
+      "group_col": "rating",
+      "measure_col": "release_year",
+      "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": 2018.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col},\n       PERCENTILE_CONT({percentile_value}) WITHIN GROUP (ORDER BY {measure_col}) AS percentile_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY percentile_measure DESC;",
+    "notes": [
+      "default_facets=rare_target_concentration",
+      "template_selection_mode=rule",
+      "problem_index_within_template=3",
+      "sql_variant_index=2/2",
+      "binding_index=86"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 8,
+    "problem_index_within_template": 3,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/sql/v2q_c17_1348349818597a09.sql",
+  "usage_summary": {
+    "dataset_id": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_1348349818597a09",
+    "api_calls": 0,
+    "input_tokens": 14683,
+    "cached_input_tokens": 12032,
+    "output_tokens": 2471,
+    "total_tokens": 17154,
+    "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": 37690.08,
+    "sql_execution_elapsed_ms_total": 60.81,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:49:26.633977+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 37690.08, "started_at": "2026-05-19T15:48:48.942115+00:00", "ended_at": "2026-05-19T15:49:26.632237+00:00", "prompt_metrics": {"chars": 9353, "bytes_utf8": 9355, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 2083, "bytes_utf8": 2083, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14683, "cached_input_tokens": 12032, "output_tokens": 2471, "reasoning_output_tokens": 1780}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_grouped_percentile_point\\nWITH \\\"base\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    CAST(NULLIF(\\\"release_year\\\", '') AS INTEGER) AS \\\"release_year\\\"\\n  FROM \\\"c17\\\"\\n  WHERE NULLIF(\\\"rating\\\", '') IS NOT NULL\\n    AND NULLIF(\\\"release_year\\\", '') IS NOT NULL\\n),\\n\\\"ranked\\\" AS (\\n  SELECT\\n    \\\"rating\\\",\\n    \\\"release_year\\\",\\n    ROW_NUMBER() OVER (PARTITION BY \\\"rating\\\" ORDER BY \\\"release_year\\\") AS \\\"rn\\\",\\n    COUNT(*) OVER (PARTITION BY \\\"rating\\\") AS \\\"cnt\\\"\\n  FROM \\\"base\\\"\\n),\\n\\\"pos\\\" AS (\\n  SELECT DISTINCT\\n    \\\"rating\\\",\\n    (1.0 + 0.9 * (\\\"cnt\\\" - 1)) AS \\\"pos\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) AS \\\"lower_rn\\\",\\n    CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) + CASE\\n      WHEN (1.0 + 0.9 * (\\\"cnt\\\" - 1)) > CAST(1.0 + 0.9 * (\\\"cnt\\\" - 1) AS INTEGER) THEN 1\\n      ELSE 0\\n    END AS \\\"upper_rn\\\"\\n  FROM \\\"ranked\\\"\\n  WHERE \\\"cnt\\\" >= 5\\n),\\n\\\"picked\\\" AS (\\n  SELECT\\n    \\\"p\\\".\\\"rating\\\",\\n    \\\"p\\\".\\\"pos\\\",\\n    \\\"p\\\".\\\"lower_rn\\\","}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c17",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c17_1348349818597a09",
+  "api_calls": 0,
+  "input_tokens": 14683,
+  "cached_input_tokens": 12032,
+  "output_tokens": 2471,
+  "total_tokens": 17154,
+  "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": 37690.08,
+  "sql_execution_elapsed_ms_total": 60.81,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1348349818597a09/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9185, "bytes_utf8": 9187, "lines": 240, "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": 409, "bytes_utf8": 409, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14647, "cached_input_tokens": 0, "output_tokens": 282, "reasoning_output_tokens": 179}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/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": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_14bb0fa770f6753e",
+    "api_calls": 0,
+    "input_tokens": 14647,
+    "cached_input_tokens": 0,
+    "output_tokens": 282,
+    "total_tokens": 14929,
+    "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": 10788.61,
+    "sql_execution_elapsed_ms_total": 7.23,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:28:35.855677+00:00",
+  "ended_at": "2026-05-19T15:28:46.644310+00:00",
+  "elapsed_ms": 10788.61,
+  "prompt_metrics": {
+    "chars": 9185,
+    "bytes_utf8": 9187,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 767,
+    "bytes_utf8": 767,
+    "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": 409,
+      "bytes_utf8": 409,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14647,
+      "cached_input_tokens": 0,
+      "output_tokens": 282,
+      "reasoning_output_tokens": 179
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+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: c17
+- dataset_name: Netflix Shows
+- table_name: c17
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `type`.
+- task_type: classification
+- target_column: type
+- main_row_count: 8807
+- important_fields:
+- show_id: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for show id.
+- type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for type.
+- title: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for title.
+- director: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for director.
+- cast: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate', 'missingness_candidate'] desc=Identifier-like field for cast.
+- country: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for country.
+- date_added: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for date added.
+- release_year: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for release year.
+- rating: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for rating.
+- duration: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for duration.
+- listed_in: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude'] desc=Text field for listed in.
+- description: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for description.
+- useful_field_combinations: [['type', 'release_year', 'type'], ['release_year', 'rating', 'type']]
+- fields_requiring_caution: ['type', 'director', 'country', 'date_added', 'cast']
+- source_url: https://www.kaggle.com/datasets/shivamb/netflix-shows
+
+SQLite schema snapshot:
+{
+  "table_name": "c17",
+  "quoted_table_name": "\"c17\"",
+  "row_count": 8807,
+  "columns": [
+    {
+      "name": "show_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "title",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "director",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "cast",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "country",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "date_added",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "release_year",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "listed_in",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "description",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "show_id": "s1",
+      "type": "Movie",
+      "title": "Dick Johnson Is Dead",
+      "director": "Kirsten Johnson",
+      "cast": "",
+      "country": "United States",
+      "date_added": "September 25, 2021",
+      "release_year": "2020",
+      "rating": "PG-13",
+      "duration": "90 min",
+      "listed_in": "Documentaries",
+      "description": "As her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable."
+    },
+    {
+      "show_id": "s2",
+      "type": "TV Show",
+      "title": "Blood & Water",
+      "director": "",
+      "cast": "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick Mofokeng",
+      "country": "South Africa",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, TV Dramas, TV Mysteries",
+      "description": "After crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth."
+    },
+    {
+      "show_id": "s3",
+      "type": "TV Show",
+      "title": "Ganglands",
+      "director": "Julien Leclercq",
+      "cast": "Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary Diombera",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Crime TV Shows, International TV Shows, TV Action & Adventure",
+      "description": "To protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war."
+    },
+    {
+      "show_id": "s4",
+      "type": "TV Show",
+      "title": "Jailbirds New Orleans",
+      "director": "",
+      "cast": "",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Docuseries, Reality TV",
+      "description": "Feuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series."
+    },
+    {
+      "show_id": "s5",
+      "type": "TV Show",
+      "title": "Kota Factory",
+      "director": "",
+      "cast": "Mayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun Kumar",
+      "country": "India",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, Romantic TV Shows, TV Comedies",
+      "description": "In a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life."
+    }
+  ]
+}
+
+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": "c17",
+  "question": "Use template Grouped Numeric Sum to probe internal_profile_stability with semantic role collapsed_target_view. Focus on group_col=release_year, measure_col=release_year.",
+  "planned_template_id": "tpl_h2o_group_sum",
+  "bindings": {
+    "group_col": "release_year",
+    "measure_col": "release_year",
+    "top_k": 11,
+    "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": 2019.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nORDER BY total_measure DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40da-6914-7e13-bce1-fed2680b7d8d"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_h2o_group_sum\\nSELECT \\\"release_year\\\", SUM(\\\"release_year\\\") AS total_measure\\nFROM \\\"c17\\\"\\nGROUP BY \\\"release_year\\\"\\nORDER BY total_measure DESC;\",\"notes\":\"Used the provided grouped numeric sum template with group_col=\\\"release_year\\\" and measure_col=\\\"release_year\\\" on the single table \\\"c17\\\". SQLite will coerce numeric-looking TEXT values in \\\"release_year\\\" during SUM.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14647,"cached_input_tokens":0,"output_tokens":282,"reasoning_output_tokens":179}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_14bb0fa770f6753e/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_h2o_group_sum\nSELECT \"release_year\", SUM(\"release_year\") AS total_measure\nFROM \"c17\"\nGROUP BY \"release_year\"\nORDER BY total_measure DESC;","notes":"Used the provided grouped numeric sum template with group_col=\"release_year\" and measure_col=\"release_year\" on the single table \"c17\". SQLite will coerce numeric-looking TEXT values in \"release_year\" during SUM."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=release_year, measure_col=release_year.
+Result preview: [{"release_year": "2021", "avg_measure": 2021.0}, {"release_year": "2020", "avg_measure": 2020.0}, {"release_year": "2019", "avg_measure": 2019.0}, {"release_year": "2018", "avg_measure": 2018.0}, {"release_year": "2017", "avg_measure": 2017.0}] Results were truncated.

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_b
+-- sql_source_dataset_id: c17
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: direction_consistency
+-- intended_facet_id: conditional_rate_shift
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_c17_1594879205169dd0
+-- problem_id: v2p_c17_02d22de6c6ac1ca0
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "release_year",
+  AVG(CAST("release_year" AS REAL)) OVER (PARTITION BY "release_year") AS avg_measure
+FROM "c17"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"release_year\",\n  AVG(CAST(\"release_year\" AS REAL)) OVER (PARTITION BY \"release_year\") AS avg_measure\nFROM \"c17\"\nORDER BY avg_measure DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"release_year\\\",\\n  AVG(CAST(\\\"release_year\\\" AS REAL)) OVER (PARTITION BY \\\"release_year\\\") AS avg_measure\\nFROM \\\"c17\\\"\\nORDER BY avg_measure DESC;\", \"columns\": [\"release_year\", \"avg_measure\"], \"rows\": [{\"release_year\": \"2021\", \"avg_measure\": 2021.0}, {\"release_year\": \"2020\", \"avg_measure\": 2020.0}, {\"release_year\": \"2019\", \"avg_measure\": 2019.0}, {\"release_year\": \"2018\", \"avg_measure\": 2018.0}, {\"release_year\": \"2017\", \"avg_measure\": 2017.0}, {\"release_year\": \"2016\", \"avg_measure\": 2016.0}, {\"release_year\": \"2015\", \"avg_measure\": 2015.0}, {\"release_year\": \"2014\", \"avg_measure\": 2014.0}, {\"release_year\": \"2013\", \"avg_measure\": 2013.0}, {\"release_year\": \"2012\", \"avg_measure\": 2012.0}, {\"release_year\": \"2011\", \"avg_measure\": 2011.0}, {\"release_year\": \"2010\", \"avg_measure\": 2010.0}, {\"release_year\": \"2009\", \"avg_measure\": 2009.0}, {\"release_year\": \"2008\", \"avg_measure\": 2008.0}, {\"release_year\": \"2007\", \"avg_measure\": 2007.0}, {\"release_year\": \"2006\", \"avg_measure\": 2006.0}, {\"release_year\": \"2005\", \"avg_measure\": 2005.0}, {\"release_year\": \"2004\", \"avg_measure\": 2004.0}, {\"release_year\": \"2003\", \"avg_measure\": 2003.0}, {\"release_year\": \"2002\", \"avg_measure\": 2002.0}, {\"release_year\": \"2001\", \"avg_measure\": 2001.0}, {\"release_year\": \"2000\", \"avg_measure\": 2000.0}, {\"release_year\": \"1999\", \"avg_measure\": 1999.0}, {\"release_year\": \"1998\", \"avg_measure\": 1998.0}, {\"release_year\": \"1997\", \"avg_measure\": 1997.0}, {\"release_year\": \"1996\", \"avg_measure\": 1996.0}, {\"release_year\": \"1995\", \"avg_measure\": 1995.0}, {\"release_year\": \"1994\", \"avg_measure\": 1994.0}, {\"release_year\": \"1993\", \"avg_measure\": 1993.0}, {\"release_year\": \"1992\", \"avg_measure\": 1992.0}, {\"release_year\": \"1991\", \"avg_measure\": 1991.0}, {\"release_year\": \"1990\", \"avg_measure\": 1990.0}, {\"release_year\": \"1989\", \"avg_measure\": 1989.0}, {\"release_year\": \"1988\", \"avg_measure\": 1988.0}, {\"release_year\": \"1987\", \"avg_measure\": 1987.0}, {\"release_year\": \"1986\", \"avg_measure\": 1986.0}, {\"release_year\": \"1985\", \"avg_measure\": 1985.0}, {\"release_year\": \"1984\", \"avg_measure\": 1984.0}, {\"release_year\": \"1983\", \"avg_measure\": 1983.0}, {\"release_year\": \"1982\", \"avg_measure\": 1982.0}, {\"release_year\": \"1981\", \"avg_measure\": 1981.0}, {\"release_year\": \"1980\", \"avg_measure\": 1980.0}, {\"release_year\": \"1979\", \"avg_measure\": 1979.0}, {\"release_year\": \"1978\", \"avg_measure\": 1978.0}, {\"release_year\": \"1977\", \"avg_measure\": 1977.0}, {\"release_year\": \"1976\", \"avg_measure\": 1976.0}, {\"release_year\": \"1975\", \"avg_measure\": 1975.0}, {\"release_year\": \"1974\", \"avg_measure\": 1974.0}, {\"release_year\": \"1973\", \"avg_measure\": 1973.0}, {\"release_year\": \"1972\", \"avg_measure\": 1972.0}], \"row_count_returned\": 50, \"row_limit\": 50, \"truncated\": true, \"elapsed_ms\": 36.99}"}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_b",
+  "dataset_id": "c17",
+  "started_at": "2026-05-19T16:00:52.634232+00:00",
+  "ended_at": "2026-05-19T16:01:01.683516+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c17_1594879205169dd0",
+    "problem_id": "v2p_c17_02d22de6c6ac1ca0",
+    "dataset_id": "c17",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "direction_consistency",
+    "intended_facet_id": "conditional_rate_shift",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe direction_consistency with semantic role ranked_signal_view. Focus on group_col=release_year, measure_col=release_year.",
+    "bindings": {
+      "group_col": "release_year",
+      "measure_col": "release_year",
+      "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": 2019.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_rate_shift",
+      "template_selection_mode=rule",
+      "problem_index_within_template=2",
+      "sql_variant_index=1/2",
+      "binding_index=133"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 2,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/sql/v2q_c17_1594879205169dd0.sql",
+  "usage_summary": {
+    "dataset_id": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_1594879205169dd0",
+    "api_calls": 0,
+    "input_tokens": 14657,
+    "cached_input_tokens": 12288,
+    "output_tokens": 292,
+    "total_tokens": 14949,
+    "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": 9005.78,
+    "sql_execution_elapsed_ms_total": 36.99,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:01:01.643708+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 9005.78, "started_at": "2026-05-19T16:00:52.636317+00:00", "ended_at": "2026-05-19T16:01:01.642130+00:00", "prompt_metrics": {"chars": 9267, "bytes_utf8": 9269, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 424, "bytes_utf8": 424, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14657, "cached_input_tokens": 12288, "output_tokens": 292, "reasoning_output_tokens": 185}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"release_year\\\",\\n  AVG(CAST(\\\"release_year\\\" AS REAL)) OVER (PARTITION BY \\\"release_year\\\") AS avg_measure\\nFROM \\\"c17\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Applied the Window Partition Average template with group_col=\\\"release_year\\\" and measure_col=\\\"release_year\\\". CAST is used because \\\"release_year\\\" is stored as TEXT in the SQLite schema.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c17",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c17_1594879205169dd0",
+  "api_calls": 0,
+  "input_tokens": 14657,
+  "cached_input_tokens": 12288,
+  "output_tokens": 292,
+  "total_tokens": 14949,
+  "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": 9005.78,
+  "sql_execution_elapsed_ms_total": 36.99,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1594879205169dd0/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/conversation.jsonl

@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9255, "bytes_utf8": 9257, "lines": 240, "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": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "status": "failed", "error": "AI CLI command failed with exit code 1: "}
+{"attempt": 2, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_2.txt", "metrics": {"chars": 9255, "bytes_utf8": 9257, "lines": 240, "estimated_tokens": null}}
+{"attempt": 2, "phase": "sql_generation", "role": "assistant", "content_path": "cli/sql_response_attempt_2.txt", "raw_content_path": "cli/sql_response_attempt_2.raw.txt", "stderr_path": "cli/sql_stderr_attempt_2.txt", "metrics": {"chars": 518, "bytes_utf8": 518, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14656, "cached_input_tokens": 12032, "output_tokens": 493, "reasoning_output_tokens": 361}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/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": 2,
+  "usage_summary": {
+    "dataset_id": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_17dbb084bc432c6f",
+    "api_calls": 0,
+    "input_tokens": 14656,
+    "cached_input_tokens": 12032,
+    "output_tokens": 493,
+    "total_tokens": 15149,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13486.33,
+    "sql_execution_elapsed_ms_total": 29.53,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:09.170744+00:00",
+  "ended_at": "2026-05-19T16:02:12.293206+00:00",
+  "elapsed_ms": 3122.44,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 9255,
+    "bytes_utf8": 9257,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_1.txt",
+  "response_path": "cli/sql_response_attempt_1.txt",
+  "raw_response_path": "cli/sql_response_attempt_1.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_1.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:02:13.294845+00:00",
+  "ended_at": "2026-05-19T16:02:23.658779+00:00",
+  "elapsed_ms": 10363.89,
+  "prompt_metrics": {
+    "chars": 9255,
+    "bytes_utf8": 9257,
+    "lines": 240,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 881,
+    "bytes_utf8": 881,
+    "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": 518,
+      "bytes_utf8": 518,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 14656,
+      "cached_input_tokens": 12032,
+      "output_tokens": 493,
+      "reasoning_output_tokens": 361
+    }
+  },
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,240 @@
+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: c17
+- dataset_name: Netflix Shows
+- table_name: c17
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `type`.
+- task_type: classification
+- target_column: type
+- main_row_count: 8807
+- important_fields:
+- show_id: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for show id.
+- type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for type.
+- title: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for title.
+- director: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for director.
+- cast: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate', 'missingness_candidate'] desc=Identifier-like field for cast.
+- country: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for country.
+- date_added: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for date added.
+- release_year: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for release year.
+- rating: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for rating.
+- duration: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for duration.
+- listed_in: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude'] desc=Text field for listed in.
+- description: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for description.
+- useful_field_combinations: [['type', 'release_year', 'type'], ['release_year', 'rating', 'type']]
+- fields_requiring_caution: ['type', 'director', 'country', 'date_added', 'cast']
+- source_url: https://www.kaggle.com/datasets/shivamb/netflix-shows
+
+SQLite schema snapshot:
+{
+  "table_name": "c17",
+  "quoted_table_name": "\"c17\"",
+  "row_count": 8807,
+  "columns": [
+    {
+      "name": "show_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "title",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "director",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "cast",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "country",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "date_added",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "release_year",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "listed_in",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "description",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "show_id": "s1",
+      "type": "Movie",
+      "title": "Dick Johnson Is Dead",
+      "director": "Kirsten Johnson",
+      "cast": "",
+      "country": "United States",
+      "date_added": "September 25, 2021",
+      "release_year": "2020",
+      "rating": "PG-13",
+      "duration": "90 min",
+      "listed_in": "Documentaries",
+      "description": "As her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable."
+    },
+    {
+      "show_id": "s2",
+      "type": "TV Show",
+      "title": "Blood & Water",
+      "director": "",
+      "cast": "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick Mofokeng",
+      "country": "South Africa",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, TV Dramas, TV Mysteries",
+      "description": "After crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth."
+    },
+    {
+      "show_id": "s3",
+      "type": "TV Show",
+      "title": "Ganglands",
+      "director": "Julien Leclercq",
+      "cast": "Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary Diombera",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Crime TV Shows, International TV Shows, TV Action & Adventure",
+      "description": "To protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war."
+    },
+    {
+      "show_id": "s4",
+      "type": "TV Show",
+      "title": "Jailbirds New Orleans",
+      "director": "",
+      "cast": "",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Docuseries, Reality TV",
+      "description": "Feuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series."
+    },
+    {
+      "show_id": "s5",
+      "type": "TV Show",
+      "title": "Kota Factory",
+      "director": "",
+      "cast": "Mayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun Kumar",
+      "country": "India",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, Romantic TV Shows, TV Comedies",
+      "description": "In a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life."
+    }
+  ]
+}
+
+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": "c17",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=type, measure_col=release_year.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "type",
+    "measure_col": "release_year",
+    "top_k": 16,
+    "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": 2018.0,
+    "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_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,240 @@
+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: c17
+- dataset_name: Netflix Shows
+- table_name: c17
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one tabular observation with 11 feature columns and target `type`.
+- task_type: classification
+- target_column: type
+- main_row_count: 8807
+- important_fields:
+- show_id: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for show id.
+- type: role=target, type=binary_target. tags=['subgroup_candidate', 'condition_candidate', 'target_candidate'] desc=Target field for type.
+- title: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for title.
+- director: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for director.
+- cast: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate', 'missingness_candidate'] desc=Identifier-like field for cast.
+- country: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for country.
+- date_added: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude', 'missingness_candidate'] desc=Text field for date added.
+- release_year: role=feature, type=numeric_discrete. tags=['subgroup_candidate', 'condition_candidate', 'measure'] desc=Numeric field for release year.
+- rating: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'missingness_candidate'] desc=Categorical field for rating.
+- duration: role=feature, type=categorical_nominal. tags=['subgroup_candidate', 'condition_candidate', 'high_cardinality_candidate', 'missingness_candidate'] desc=Categorical field for duration.
+- listed_in: role=feature, type=free_text. tags=['high_cardinality_candidate', 'text_exclude'] desc=Text field for listed in.
+- description: role=feature, type=identifier_string. tags=['identifier', 'probe_exclude', 'high_cardinality_candidate'] desc=Identifier-like field for description.
+- useful_field_combinations: [['type', 'release_year', 'type'], ['release_year', 'rating', 'type']]
+- fields_requiring_caution: ['type', 'director', 'country', 'date_added', 'cast']
+- source_url: https://www.kaggle.com/datasets/shivamb/netflix-shows
+
+SQLite schema snapshot:
+{
+  "table_name": "c17",
+  "quoted_table_name": "\"c17\"",
+  "row_count": 8807,
+  "columns": [
+    {
+      "name": "show_id",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "type",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "title",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "director",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "cast",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "country",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "date_added",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "release_year",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "rating",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "duration",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "listed_in",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "description",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "show_id": "s1",
+      "type": "Movie",
+      "title": "Dick Johnson Is Dead",
+      "director": "Kirsten Johnson",
+      "cast": "",
+      "country": "United States",
+      "date_added": "September 25, 2021",
+      "release_year": "2020",
+      "rating": "PG-13",
+      "duration": "90 min",
+      "listed_in": "Documentaries",
+      "description": "As her father nears the end of his life, filmmaker Kirsten Johnson stages his death in inventive and comical ways to help them both face the inevitable."
+    },
+    {
+      "show_id": "s2",
+      "type": "TV Show",
+      "title": "Blood & Water",
+      "director": "",
+      "cast": "Ama Qamata, Khosi Ngema, Gail Mabalane, Thabang Molaba, Dillon Windvogel, Natasha Thahane, Arno Greeff, Xolile Tshabalala, Getmore Sithole, Cindy Mahlangu, Ryle De Morny, Greteli Fincham, Sello Maake Ka-Ncube, Odwa Gwanya, Mekaila Mathys, Sandi Schultz, Duane Williams, Shamilla Miller, Patrick Mofokeng",
+      "country": "South Africa",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, TV Dramas, TV Mysteries",
+      "description": "After crossing paths at a party, a Cape Town teen sets out to prove whether a private-school swimming star is her sister who was abducted at birth."
+    },
+    {
+      "show_id": "s3",
+      "type": "TV Show",
+      "title": "Ganglands",
+      "director": "Julien Leclercq",
+      "cast": "Sami Bouajila, Tracy Gotoas, Samuel Jouy, Nabiha Akkari, Sofia Lesaffre, Salim Kechiouche, Noureddine Farihi, Geert Van Rampelberg, Bakary Diombera",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Crime TV Shows, International TV Shows, TV Action & Adventure",
+      "description": "To protect his family from a powerful drug lord, skilled thief Mehdi and his expert team of robbers are pulled into a violent and deadly turf war."
+    },
+    {
+      "show_id": "s4",
+      "type": "TV Show",
+      "title": "Jailbirds New Orleans",
+      "director": "",
+      "cast": "",
+      "country": "",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "1 Season",
+      "listed_in": "Docuseries, Reality TV",
+      "description": "Feuds, flirtations and toilet talk go down among the incarcerated women at the Orleans Justice Center in New Orleans on this gritty reality series."
+    },
+    {
+      "show_id": "s5",
+      "type": "TV Show",
+      "title": "Kota Factory",
+      "director": "",
+      "cast": "Mayur More, Jitendra Kumar, Ranjan Raj, Alam Khan, Ahsaas Channa, Revathi Pillai, Urvi Singh, Arun Kumar",
+      "country": "India",
+      "date_added": "September 24, 2021",
+      "release_year": "2021",
+      "rating": "TV-MA",
+      "duration": "2 Seasons",
+      "listed_in": "International TV Shows, Romantic TV Shows, TV Comedies",
+      "description": "In a city of coaching centers known to train India’s finest collegiate minds, an earnest but unexceptional student and his friends navigate campus life."
+    }
+  ]
+}
+
+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": "c17",
+  "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=type, measure_col=release_year.",
+  "planned_template_id": "tpl_m4_window_partition_avg",
+  "bindings": {
+    "group_col": "type",
+    "measure_col": "release_year",
+    "top_k": 16,
+    "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": 2018.0,
+    "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_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-2197-7cf2-ad12-6a55c50703a3"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-2197-7cf2-ad12-6a55c50703a3"}
+{"type":"turn.started"}
+{"type":"error","message":"Quota exceeded. Check your plan and billing details."}
+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40f9-31c3-7a91-842a-e18e94de8c2f"}
+{"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  \\\"type\\\",\\n  AVG(CASE WHEN NULLIF(TRIM(\\\"release_year\\\"), '') IS NOT NULL THEN CAST(\\\"release_year\\\" AS REAL) END) OVER (PARTITION BY \\\"type\\\") AS avg_measure\\nFROM \\\"c17\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested window-partition average template with group_col=\\\"type\\\" and measure_col=\\\"release_year\\\". Since \\\"release_year\\\" is stored as TEXT in SQLite, the query casts it to REAL and ignores blank strings before averaging.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":14656,"cached_input_tokens":12032,"output_tokens":493,"reasoning_output_tokens":361}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/sql_response_attempt_2.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_m4_window_partition_avg\nSELECT DISTINCT\n  \"type\",\n  AVG(CASE WHEN NULLIF(TRIM(\"release_year\"), '') IS NOT NULL THEN CAST(\"release_year\" AS REAL) END) OVER (PARTITION BY \"type\") AS avg_measure\nFROM \"c17\"\nORDER BY avg_measure DESC;","notes":"Uses the requested window-partition average template with group_col=\"type\" and measure_col=\"release_year\". Since \"release_year\" is stored as TEXT in SQLite, the query casts it to REAL and ignores blank strings before averaging."}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=type, measure_col=release_year.
+Result preview: [{"type": "TV Show", "avg_measure": 2016.605754857997}, {"type": "Movie", "avg_measure": 2013.1215136193116}]

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_b
+-- sql_source_dataset_id: c17
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: ranked_signal_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_c17_17dbb084bc432c6f
+-- problem_id: v2p_c17_e31986015997814e
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "type",
+  AVG(CASE WHEN NULLIF(TRIM("release_year"), '') IS NOT NULL THEN CAST("release_year" AS REAL) END) OVER (PARTITION BY "type") AS avg_measure
+FROM "c17"
+ORDER BY avg_measure DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_b",
+  "dataset_id": "c17",
+  "started_at": "2026-05-19T16:02:09.168323+00:00",
+  "ended_at": "2026-05-19T16:02:23.691881+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c17_17dbb084bc432c6f",
+    "problem_id": "v2p_c17_e31986015997814e",
+    "dataset_id": "c17",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "ranked_signal_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role ranked_signal_view. Focus on group_col=type, measure_col=release_year.",
+    "bindings": {
+      "group_col": "type",
+      "measure_col": "release_year",
+      "top_k": 16,
+      "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": 2018.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT DISTINCT {group_col},\n       AVG({measure_col}) OVER (PARTITION BY {group_col}) AS avg_measure\nFROM {table}\nORDER BY avg_measure DESC;",
+    "notes": [
+      "default_facets=conditional_interaction_hotspots",
+      "template_selection_mode=rule",
+      "problem_index_within_template=5",
+      "sql_variant_index=2/2",
+      "binding_index=136"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 5,
+    "sql_variant_index": 2,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/sql/v2q_c17_17dbb084bc432c6f.sql",
+  "usage_summary": {
+    "dataset_id": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_17dbb084bc432c6f",
+    "api_calls": 0,
+    "input_tokens": 14656,
+    "cached_input_tokens": 12032,
+    "output_tokens": 493,
+    "total_tokens": 15149,
+    "cost_usd": 0.0,
+    "ai_cli_calls": 2,
+    "estimated_input_tokens": 0,
+    "estimated_output_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "ai_cli_json_usage",
+    "cli_elapsed_ms_total": 13486.33,
+    "sql_execution_elapsed_ms_total": 29.53,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:02:12.293912+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 1, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3122.44, "started_at": "2026-05-19T16:02:09.170744+00:00", "ended_at": "2026-05-19T16:02:12.293206+00:00", "prompt_metrics": {"chars": 9255, "bytes_utf8": 9257, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 280, "bytes_utf8": 280, "lines": 4, "estimated_tokens": null}, "usage": {}, "stderr_preview": "", "stdout_preview": "{\"type\":\"thread.started\",\"thread_id\":\"019e40f9-2197-7cf2-ad12-6a55c50703a3\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}
+{"timestamp": "2026-05-19T16:02:23.659933+00:00", "event_type": "ai_cli_sql_generation", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 0, "elapsed_ms": 10363.89, "started_at": "2026-05-19T16:02:13.294845+00:00", "ended_at": "2026-05-19T16:02:23.658779+00:00", "prompt_metrics": {"chars": 9255, "bytes_utf8": 9257, "lines": 240, "estimated_tokens": null}, "response_metrics": {"chars": 518, "bytes_utf8": 518, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14656, "cached_input_tokens": 12032, "output_tokens": 493, "reasoning_output_tokens": 361}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"type\\\",\\n  AVG(CASE WHEN NULLIF(TRIM(\\\"release_year\\\"), '') IS NOT NULL THEN CAST(\\\"release_year\\\" AS REAL) END) OVER (PARTITION BY \\\"type\\\") AS avg_measure\\nFROM \\\"c17\\\"\\nORDER BY avg_measure DESC;\",\"notes\":\"Uses the requested window-partition average template with group_col=\\\"type\\\" and measure_col=\\\"release_year\\\". Since \\\"release_year\\\" is stored as TEXT in SQLite, the query casts it to REAL and ignores blank strings before averaging.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "c17",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_c17_17dbb084bc432c6f",
+  "api_calls": 0,
+  "input_tokens": 14656,
+  "cached_input_tokens": 12032,
+  "output_tokens": 493,
+  "total_tokens": 15149,
+  "cost_usd": 0.0,
+  "ai_cli_calls": 2,
+  "estimated_input_tokens": 0,
+  "estimated_output_tokens": 0,
+  "estimated_total_tokens": 0,
+  "usage_source": "ai_cli_json_usage",
+  "cli_elapsed_ms_total": 13486.33,
+  "sql_execution_elapsed_ms_total": 29.53,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_17dbb084bc432c6f/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1d5e0be2525ca666/final_answer.txt

@@ -0,0 +1 @@
+{"row_count": null, "preview_rows": [{"total_rows": 8807, "missing_rows": 0, "missing_rate": 0.0}]}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1d5e0be2525ca666/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_b
+-- sql_source_dataset_id: c17
+-- family_id: missingness_structure
+-- canonical_subitem_id: marginal_missing_rate_consistency
+-- intended_facet_id: missing_indicator_distribution
+-- variant_semantic_role: missing_indicator_view
+-- template_id: tpl_missing_marginal_rate_profile
+-- query_record_id: v2q_c17_1d5e0be2525ca666
+-- problem_id: v2p_c17_1c7fefa77244ee52
+-- realization_mode: deterministic
+-- source_kind: deterministic
+SELECT
+    COUNT(*) AS total_rows,
+    SUM(CASE WHEN "date_added" IS NULL THEN 1 ELSE 0 END) AS missing_rows,
+    AVG(CASE WHEN "date_added" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate
+FROM "c17";

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1d5e0be2525ca666/query_results.jsonl

@@ -0,0 +1 @@
+{"node_name": "v2_template", "tool_name": "sqlite_query", "query": "-- sql_source_version: v2\n-- sql_source_label: v2_current\n-- sql_source_run_id: v2_cli_20260502_081223_b\n-- sql_source_dataset_id: c17\n-- family_id: missingness_structure\n-- canonical_subitem_id: marginal_missing_rate_consistency\n-- intended_facet_id: missing_indicator_distribution\n-- variant_semantic_role: missing_indicator_view\n-- template_id: tpl_missing_marginal_rate_profile\n-- query_record_id: v2q_c17_1d5e0be2525ca666\n-- problem_id: v2p_c17_1c7fefa77244ee52\n-- realization_mode: deterministic\n-- source_kind: deterministic\nSELECT\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN \"date_added\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN \"date_added\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM \"c17\";", "result": "{\"query\": \"-- sql_source_version: v2\\n-- sql_source_label: v2_current\\n-- sql_source_run_id: v2_cli_20260502_081223_b\\n-- sql_source_dataset_id: c17\\n-- family_id: missingness_structure\\n-- canonical_subitem_id: marginal_missing_rate_consistency\\n-- intended_facet_id: missing_indicator_distribution\\n-- variant_semantic_role: missing_indicator_view\\n-- template_id: tpl_missing_marginal_rate_profile\\n-- query_record_id: v2q_c17_1d5e0be2525ca666\\n-- problem_id: v2p_c17_1c7fefa77244ee52\\n-- realization_mode: deterministic\\n-- source_kind: deterministic\\nSELECT\\n    COUNT(*) AS total_rows,\\n    SUM(CASE WHEN \\\"date_added\\\" IS NULL THEN 1 ELSE 0 END) AS missing_rows,\\n    AVG(CASE WHEN \\\"date_added\\\" IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\\nFROM \\\"c17\\\";\", \"columns\": [\"total_rows\", \"missing_rows\", \"missing_rate\"], \"rows\": [{\"total_rows\": 8807, \"missing_rows\": 0, \"missing_rate\": 0.0}], \"row_count_returned\": 1, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 2.2}"}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1d5e0be2525ca666/run_manifest.json

@@ -0,0 +1,57 @@
+{
+  "run_id": "v2_cli_20260502_081223_b",
+  "dataset_id": "c17",
+  "started_at": "2026-05-19T16:03:15.307396+00:00",
+  "ended_at": "2026-05-19T16:03:15.310274+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_c17_1d5e0be2525ca666",
+    "problem_id": "v2p_c17_1c7fefa77244ee52",
+    "dataset_id": "c17",
+    "template_id": "tpl_missing_marginal_rate_profile",
+    "template_name": "Marginal Missing Rate Profile",
+    "family_id": "missingness_structure",
+    "canonical_subitem_id": "marginal_missing_rate_consistency",
+    "intended_facet_id": "missing_indicator_distribution",
+    "variant_semantic_role": "missing_indicator_view",
+    "subitem_assignment_source": "template_fixed",
+    "source_kind": "deterministic",
+    "realization_mode": "deterministic",
+    "gate_priority": "deterministic",
+    "extended_family": false,
+    "question": "Use template Marginal Missing Rate Profile to probe marginal_missing_rate_consistency with semantic role missing_indicator_view. Focus on missing_col=date_added.",
+    "bindings": {
+      "missing_col": "date_added"
+    },
+    "binding_roles": [
+      "missing_col"
+    ],
+    "coverage_target_min": "enumerate_all_applicable",
+    "runtime_sql_skeleton": "SELECT\n    COUNT(*) AS total_rows,\n    SUM(CASE WHEN {missing_col} IS NULL THEN 1 ELSE 0 END) AS missing_rows,\n    AVG(CASE WHEN {missing_col} IS NULL THEN 1.0 ELSE 0.0 END) AS missing_rate\nFROM {table};",
+    "notes": [
+      "default_facets=missing_indicator_distribution",
+      "template_selection_mode=deterministic",
+      "problem_index_within_template=4",
+      "sql_variant_index=1/1"
+    ],
+    "template_selection_mode": "deterministic",
+    "selected_template_rank": 0,
+    "problem_index_within_template": 4,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/sql/v2q_c17_1d5e0be2525ca666.sql",
+  "usage_summary": {
+    "engine": "template",
+    "input_tokens": 0,
+    "cached_input_tokens": 0,
+    "output_tokens": 0,
+    "total_tokens": 0,
+    "estimated_total_tokens": 0,
+    "usage_source": "none"
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1d5e0be2525ca666/usage_summary.json

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

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1fcda40232b5e8fc/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 9978, "bytes_utf8": 9980, "lines": 243, "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": 686, "bytes_utf8": 686, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 14853, "cached_input_tokens": 13696, "output_tokens": 573, "reasoning_output_tokens": 385}}

Query/sql/v2/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1fcda40232b5e8fc/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": "c17",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_c17_1fcda40232b5e8fc",
+    "api_calls": 0,
+    "input_tokens": 14853,
+    "cached_input_tokens": 13696,
+    "output_tokens": 573,
+    "total_tokens": 15426,
+    "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": 14799.22,
+    "sql_execution_elapsed_ms_total": 8.4,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_b/c17/artifacts/v2q_c17_1fcda40232b5e8fc/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}