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Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29253, "bytes_utf8": 29253, "lines": 790, "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": 237, "bytes_utf8": 237, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20286, "cached_input_tokens": 19840, "output_tokens": 220, "reasoning_output_tokens": 154}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/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": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_01bf1c58a49bee59",
+    "api_calls": 0,
+    "input_tokens": 20286,
+    "cached_input_tokens": 19840,
+    "output_tokens": 220,
+    "total_tokens": 20506,
+    "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": 26288.86,
+    "sql_execution_elapsed_ms_total": 2.48,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_attempt_1.metadata.json

@@ -0,0 +1,45 @@
+{
+  "attempt": 1,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T15:33:11.419557+00:00",
+  "ended_at": "2026-05-19T15:33:37.708461+00:00",
+  "elapsed_ms": 26288.86,
+  "prompt_metrics": {
+    "chars": 29253,
+    "bytes_utf8": 29253,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 2248,
+    "bytes_utf8": 2248,
+    "lines": 9,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 237,
+      "bytes_utf8": 237,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20286,
+      "cached_input_tokens": 19840,
+      "output_tokens": 220,
+      "reasoning_output_tokens": 154
+    }
+  },
+  "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_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,790 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
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+      "word_freq_data": "0",
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+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
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+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
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+      "word_freq_money": "0.43",
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+      "word_freq_hpl": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_parts": "0",
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+      "word_freq_direct": "0",
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+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_clickbench_group_count",
+    "template_name": "Grouped Count by Category",
+    "primary_family": "subgroup_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;",
+    "required_roles": [
+      "group_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Grouped Count by Category to probe subgroup_size_stability with semantic role count_distribution. Focus on group_col=class.",
+  "planned_template_id": "tpl_clickbench_group_count",
+  "bindings": {
+    "group_col": "class",
+    "top_k": 12,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, COUNT(*) AS row_count\nFROM {table}\nGROUP BY {group_col}\nORDER BY row_count DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,9 @@
+{"type":"thread.started","thread_id":"019e40de-9d8c-7c12-9ecd-04623ca51b1d"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1997605, Requested 20921. Please try again in 555ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 4/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 5/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 2000000, Requested 20921. Please try again in 627ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 1/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1990463, Requested 20921. Please try again in 341ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_clickbench_group_count\\nSELECT \\\"class\\\", COUNT(*) AS row_count\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nORDER BY row_count DESC;\",\"notes\":\"Counts rows for each value of \\\"class\\\" to inspect the class distribution.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20286,"cached_input_tokens":19840,"output_tokens":220,"reasoning_output_tokens":154}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_01bf1c58a49bee59/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_clickbench_group_count\nSELECT \"class\", COUNT(*) AS row_count\nFROM \"n1\"\nGROUP BY \"class\"\nORDER BY row_count DESC;","notes":"Counts rows for each value of \"class\" to inspect the class distribution."}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/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": "2026-05-19T16:01:30.469248+00:00",
+  "ended_at": "2026-05-19T16:01:33.417284+00:00",
+  "elapsed_ms": 2948.01,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29304,
+    "bytes_utf8": 29304,
+    "lines": 790,
+    "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_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_attempt_2.metadata.json

@@ -0,0 +1,43 @@
+{
+  "attempt": 2,
+  "phase": "sql_generation",
+  "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -",
+  "started_at": "2026-05-19T16:01:34.420466+00:00",
+  "ended_at": "2026-05-19T16:01:37.616337+00:00",
+  "elapsed_ms": 3195.82,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29304,
+    "bytes_utf8": 29304,
+    "lines": 790,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 281,
+    "bytes_utf8": 281,
+    "lines": 4,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 280,
+      "bytes_utf8": 280,
+      "lines": 4,
+      "estimated_tokens": null
+    },
+    "usage": {}
+  },
+  "status": "failed",
+  "error": "AI CLI command failed with exit code 1: ",
+  "prompt_path": "cli/sql_prompt_attempt_2.txt",
+  "response_path": "cli/sql_response_attempt_2.txt",
+  "raw_response_path": "cli/sql_response_attempt_2.raw.txt",
+  "stderr_path": "cli/sql_stderr_attempt_2.txt"
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,790 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "word_freq_address",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,790 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
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+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
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+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "primary_family": "tail_rarity_structure",
+    "portability": "yes",
+    "sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "required_roles": [
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+  "planned_template_id": "tpl_threshold_rarity_cdf",
+  "bindings": {
+    "measure_col": "word_freq_address",
+    "top_k": 10,
+    "top_n": 6,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.raw.txt

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+{"type":"turn.started"}
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Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_1.txt

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+{"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_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.raw.txt

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+{"type":"turn.started"}
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+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/cli/sql_response_attempt_2.txt

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+{"type":"turn.started"}
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+{"type":"turn.failed","error":{"message":"Quota exceeded. Check your plan and billing details."}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/run_manifest.json

@@ -0,0 +1,67 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:01:30.467285+00:00",
+  "ended_at": "2026-05-19T16:01:37.617251+00:00",
+  "status": "failed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_03957dab4b318bed",
+    "problem_id": "v2p_n1_e25e5b5ae21d3cf2",
+    "dataset_id": "n1",
+    "template_id": "tpl_threshold_rarity_cdf",
+    "template_name": "Threshold Rarity CDF",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Threshold Rarity CDF to probe tail_set_consistency with semantic role rare_extreme_view. Focus on measure_col=word_freq_address.",
+    "bindings": {
+      "measure_col": "word_freq_address",
+      "top_k": 10,
+      "top_n": 6,
+      "num_tiles": 10,
+      "percentile_value": 0.9,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT AVG(CASE WHEN {measure_col} <= {measure_threshold} THEN 1 ELSE 0 END) AS empirical_cdf_at_threshold\nFROM {table};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=8",
+      "sql_variant_index=1/1",
+      "binding_index=115"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 10,
+    "problem_index_within_template": 8,
+    "sql_variant_index": 1,
+    "sql_variant_total": 1
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "error": "AI CLI command failed with exit code 1: "
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_03957dab4b318bed/trace.jsonl

@@ -0,0 +1,2 @@
+{"timestamp": "2026-05-19T16:01:33.418088+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": 2948.01, "started_at": "2026-05-19T16:01:30.469248+00:00", "ended_at": "2026-05-19T16:01:33.417284+00:00", "prompt_metrics": {"chars": 29304, "bytes_utf8": 29304, "lines": 790, "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\":\"019e40f8-8a61-75f3-92fd-c0829756a48e\"}\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": "2026-05-19T16:01:37.617154+00:00", "event_type": "ai_cli_sql_generation_error", "engine": "v2-cli:codex", "attempt": 2, "command": "codex exec --skip-git-repo-check --disable plugins --sandbox read-only --cd \"/data/jialinzhang/SQLagent\" -m gpt-5.4 --json -", "returncode": 1, "elapsed_ms": 3195.82, "started_at": "2026-05-19T16:01:34.420466+00:00", "ended_at": "2026-05-19T16:01:37.616337+00:00", "prompt_metrics": {"chars": 29304, "bytes_utf8": 29304, "lines": 790, "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\":\"019e40f8-99d2-75b1-a421-a886202b5193\"}\n{\"type\":\"turn.started\"}\n{\"type\":\"error\",\"message\":\"Quota exceeded. Check your plan and billing details.\"}\n{\"type\":\"turn.failed\",\"error\":{\"message\":\"Quota exceeded. Check your plan and billing details.\"}}", "error": "AI CLI command failed with exit code 1: "}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl

@@ -0,0 +1,4 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29525, "bytes_utf8": 29525, "lines": 792, "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": 29525, "bytes_utf8": 29525, "lines": 792, "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": 450, "bytes_utf8": 450, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20359, "cached_input_tokens": 12032, "output_tokens": 329, "reasoning_output_tokens": 207}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/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": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_05786dfc8dc90728",
+    "api_calls": 0,
+    "input_tokens": 20359,
+    "cached_input_tokens": 12032,
+    "output_tokens": 329,
+    "total_tokens": 20688,
+    "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": 11670.96,
+    "sql_execution_elapsed_ms_total": 7.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/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": "2026-05-19T16:04:51.033297+00:00",
+  "ended_at": "2026-05-19T16:04:53.995292+00:00",
+  "elapsed_ms": 2961.97,
+  "returncode": 1,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "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_d/n1/artifacts/v2q_n1_05786dfc8dc90728/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": "2026-05-19T16:04:54.997365+00:00",
+  "ended_at": "2026-05-19T16:05:03.706403+00:00",
+  "elapsed_ms": 8708.99,
+  "prompt_metrics": {
+    "chars": 29525,
+    "bytes_utf8": 29525,
+    "lines": 792,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 810,
+    "bytes_utf8": 810,
+    "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": 450,
+      "bytes_utf8": 450,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20359,
+      "cached_input_tokens": 12032,
+      "output_tokens": 329,
+      "reasoning_output_tokens": 207
+    }
+  },
+  "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_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,792 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_prompt_attempt_2.txt

@@ -0,0 +1,792 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_address",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_3d",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_order",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_addresses",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_free",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_email",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_you",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_your",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_font",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_000",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_money",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hp",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_hpl",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_george",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_650",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_lab",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_labs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_telnet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_857",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_data",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_415",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_85",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_technology",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_1999",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_parts",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_pm",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_direct",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_cs",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_original",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_project",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_re",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_edu",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_table",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_conference",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "char_freq_%23",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0.64",
+      "word_freq_all": "0.64",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.32",
+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.21",
+      "word_freq_address": "0.28",
+      "word_freq_all": "0.5",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.14",
+      "word_freq_over": "0.28",
+      "word_freq_remove": "0.21",
+      "word_freq_internet": "0.07",
+      "word_freq_order": "0",
+      "word_freq_mail": "0.94",
+      "word_freq_receive": "0.21",
+      "word_freq_will": "0.79",
+      "word_freq_people": "0.65",
+      "word_freq_report": "0.21",
+      "word_freq_addresses": "0.14",
+      "word_freq_free": "0.14",
+      "word_freq_business": "0.07",
+      "word_freq_email": "0.28",
+      "word_freq_you": "3.47",
+      "word_freq_credit": "0",
+      "word_freq_your": "1.59",
+      "word_freq_font": "0",
+      "word_freq_000": "0.43",
+      "word_freq_money": "0.43",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0.07",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.132",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.372",
+      "char_freq_%24": "0.18",
+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0.06",
+      "word_freq_address": "0",
+      "word_freq_all": "0.71",
+      "word_freq_3d": "0",
+      "word_freq_our": "1.23",
+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
+      "word_freq_internet": "0.12",
+      "word_freq_order": "0.64",
+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
+      "word_freq_business": "0.06",
+      "word_freq_email": "1.03",
+      "word_freq_you": "1.36",
+      "word_freq_credit": "0.32",
+      "word_freq_your": "0.51",
+      "word_freq_font": "0",
+      "word_freq_000": "1.16",
+      "word_freq_money": "0.06",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.137",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "primary_family": "tail_rarity_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "required_roles": [
+      "group_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Thresholded Group Ranking to probe tail_mass_similarity with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_font.",
+  "planned_template_id": "tpl_tpch_thresholded_group_ranking",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_font",
+    "top_k": 15,
+    "top_n": 7,
+    "num_tiles": 10,
+    "percentile_value": 0.95,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.05,
+    "baseline_multiplier": 1.75,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 4,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-99fd-7642-b681-b4ddb8fde85a"}
+{"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_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_1.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-99fd-7642-b681-b4ddb8fde85a"}
+{"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_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.raw.txt

@@ -0,0 +1,4 @@
+{"type":"thread.started","thread_id":"019e40fb-a979-7543-afd3-3440f83bdb76"}
+{"type":"turn.started"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_font\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_font\\\" AS REAL)) > 0.0\\nORDER BY total_measure DESC\\nLIMIT 15;\",\"notes\":\"Used the required template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_font\\\". CAST to REAL is included because the schema stores these numeric features as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20359,"cached_input_tokens":12032,"output_tokens":329,"reasoning_output_tokens":207}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_05786dfc8dc90728/cli/sql_response_attempt_2.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_font\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_font\" AS REAL)) > 0.0\nORDER BY total_measure DESC\nLIMIT 15;","notes":"Used the required template with group_col=\"class\" and measure_col=\"word_freq_font\". CAST to REAL is included because the schema stores these numeric features as TEXT in SQLite."}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%3B.
+Result preview: [{"class": "0", "avg_measure": 0.050280846484935436}, {"class": "1", "avg_measure": 0.020573083287369003}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/generated_sql.sql

@@ -0,0 +1,18 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: conditional_dependency_structure
+-- canonical_subitem_id: slice_level_consistency
+-- intended_facet_id: conditional_interaction_hotspots
+-- variant_semantic_role: filtered_stable_view
+-- template_id: tpl_m4_window_partition_avg
+-- query_record_id: v2q_n1_06a5b861b6b26a1c
+-- problem_id: v2p_n1_4f1bfaf50ec0d80a
+-- realization_mode: agent
+-- source_kind: agent
+SELECT DISTINCT
+  "class",
+  AVG(CAST("char_freq_%3B" AS REAL)) OVER (PARTITION BY "class") AS "avg_measure"
+FROM "n1"
+ORDER BY "avg_measure" DESC;

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/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  \"class\",\n  AVG(CAST(\"char_freq_%3B\" AS REAL)) OVER (PARTITION BY \"class\") AS \"avg_measure\"\nFROM \"n1\"\nORDER BY \"avg_measure\" DESC;", "result": "{\"query\": \"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\", \"columns\": [\"class\", \"avg_measure\"], \"rows\": [{\"class\": \"0\", \"avg_measure\": 0.050280846484935436}, {\"class\": \"1\", \"avg_measure\": 0.020573083287369003}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 8.27}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T15:41:15.530686+00:00",
+  "ended_at": "2026-05-19T15:41:26.750416+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_06a5b861b6b26a1c",
+    "problem_id": "v2p_n1_4f1bfaf50ec0d80a",
+    "dataset_id": "n1",
+    "template_id": "tpl_m4_window_partition_avg",
+    "template_name": "Window Partition Average",
+    "family_id": "conditional_dependency_structure",
+    "canonical_subitem_id": "slice_level_consistency",
+    "intended_facet_id": "conditional_interaction_hotspots",
+    "variant_semantic_role": "filtered_stable_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Window Partition Average to probe slice_level_consistency with semantic role filtered_stable_view. Focus on group_col=class, measure_col=char_freq_%3B.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "char_freq_%3B",
+      "top_k": 13,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 0.0,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT 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=1",
+      "sql_variant_index=1/2",
+      "binding_index=48"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 5,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_06a5b861b6b26a1c.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_06a5b861b6b26a1c",
+    "api_calls": 0,
+    "input_tokens": 20331,
+    "cached_input_tokens": 19840,
+    "output_tokens": 505,
+    "total_tokens": 20836,
+    "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": 11207.34,
+    "sql_execution_elapsed_ms_total": 8.27,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T15:41:26.740799+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": 11207.34, "started_at": "2026-05-19T15:41:15.532632+00:00", "ended_at": "2026-05-19T15:41:26.740024+00:00", "prompt_metrics": {"chars": 29440, "bytes_utf8": 29440, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 430, "bytes_utf8": 430, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20331, "cached_input_tokens": 19840, "output_tokens": 505, "reasoning_output_tokens": 390}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_m4_window_partition_avg\\nSELECT DISTINCT\\n  \\\"class\\\",\\n  AVG(CAST(\\\"char_freq_%3B\\\" AS REAL)) OVER (PARTITION BY \\\"class\\\") AS \\\"avg_measure\\\"\\nFROM \\\"n1\\\"\\nORDER BY \\\"avg_measure\\\" DESC;\",\"notes\":\"Uses the planned Window Partition Average template with group_col=\\\"class\\\" and measure_col=\\\"char_freq_%3B\\\". CAST to REAL is added because the schema snapshot shows the measure column stored as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_06a5b861b6b26a1c",
+  "api_calls": 0,
+  "input_tokens": 20331,
+  "cached_input_tokens": 19840,
+  "output_tokens": 505,
+  "total_tokens": 20836,
+  "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": 11207.34,
+  "sql_execution_elapsed_ms_total": 8.27,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06a5b861b6b26a1c/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/final_answer.txt

@@ -0,0 +1,2 @@
+SQL executed successfully for: Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_you.
+Result preview: [{"class": "1", "total_measure": 4105.61}, {"class": "0", "total_measure": 3541.71}]

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/generated_sql.sql

@@ -0,0 +1,19 @@
+-- sql_source_version: v2
+-- sql_source_label: v2_current
+-- sql_source_run_id: v2_cli_20260502_081223_d
+-- sql_source_dataset_id: n1
+-- family_id: tail_rarity_structure
+-- canonical_subitem_id: tail_set_consistency
+-- intended_facet_id: low_support_extremes
+-- variant_semantic_role: rare_extreme_view
+-- template_id: tpl_tpch_thresholded_group_ranking
+-- query_record_id: v2q_n1_06f410de776111c5
+-- problem_id: v2p_n1_8b35fa14b920400b
+-- realization_mode: agent
+-- source_kind: agent
+SELECT "class", SUM(CAST("word_freq_you" AS REAL)) AS total_measure
+FROM "n1"
+GROUP BY "class"
+HAVING SUM(CAST("word_freq_you" AS REAL)) > 2.64
+ORDER BY total_measure DESC
+LIMIT 12;

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/query_results.jsonl

@@ -0,0 +1 @@
+{"step_index": 1, "message_index": 0, "node_name": "v2-cli:codex", "tool_name": "sqlite_query", "query": "-- template_id: tpl_tpch_thresholded_group_ranking\nSELECT \"class\", SUM(CAST(\"word_freq_you\" AS REAL)) AS total_measure\nFROM \"n1\"\nGROUP BY \"class\"\nHAVING SUM(CAST(\"word_freq_you\" AS REAL)) > 2.64\nORDER BY total_measure DESC\nLIMIT 12;", "result": "{\"query\": \"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.64\\nORDER BY total_measure DESC\\nLIMIT 12;\", \"columns\": [\"class\", \"total_measure\"], \"rows\": [{\"class\": \"1\", \"total_measure\": 4105.61}, {\"class\": \"0\", \"total_measure\": 3541.71}], \"row_count_returned\": 2, \"row_limit\": 50, \"truncated\": false, \"elapsed_ms\": 3.25}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/run_manifest.json

@@ -0,0 +1,89 @@
+{
+  "run_id": "v2_cli_20260502_081223_d",
+  "dataset_id": "n1",
+  "started_at": "2026-05-19T16:03:47.788513+00:00",
+  "ended_at": "2026-05-19T16:03:59.776060+00:00",
+  "status": "completed",
+  "engine": "cli",
+  "question_record": {
+    "query_record_id": "v2q_n1_06f410de776111c5",
+    "problem_id": "v2p_n1_8b35fa14b920400b",
+    "dataset_id": "n1",
+    "template_id": "tpl_tpch_thresholded_group_ranking",
+    "template_name": "Thresholded Group Ranking",
+    "family_id": "tail_rarity_structure",
+    "canonical_subitem_id": "tail_set_consistency",
+    "intended_facet_id": "low_support_extremes",
+    "variant_semantic_role": "rare_extreme_view",
+    "subitem_assignment_source": "planner_selected",
+    "source_kind": "agent",
+    "realization_mode": "agent",
+    "gate_priority": "primary",
+    "extended_family": false,
+    "question": "Use template Thresholded Group Ranking to probe tail_set_consistency with semantic role rare_extreme_view. Focus on group_col=class, measure_col=word_freq_you.",
+    "bindings": {
+      "group_col": "class",
+      "measure_col": "word_freq_you",
+      "top_k": 12,
+      "top_n": 3,
+      "num_tiles": 10,
+      "percentile_value": 0.95,
+      "z_threshold": 2.0,
+      "fraction_threshold": 0.1,
+      "baseline_multiplier": 1.5,
+      "baseline_fraction": 0.1,
+      "min_group_size": 5,
+      "min_support": 5,
+      "measure_threshold": 2.64,
+      "time_grain": "month",
+      "lookback_rows": 3,
+      "current_period_start": "'2024-01-01'",
+      "current_period_end": "'2024-04-01'",
+      "previous_period_start": "'2023-10-01'",
+      "previous_period_end": "'2024-01-01'",
+      "drift_ratio_threshold": 0.8
+    },
+    "binding_roles": [
+      "group_col",
+      "measure_col"
+    ],
+    "coverage_target_min": "5",
+    "runtime_sql_skeleton": "SELECT {group_col}, SUM({measure_col}) AS total_measure\nFROM {table}\nGROUP BY {group_col}\nHAVING SUM({measure_col}) > {measure_threshold}\nORDER BY total_measure DESC\nLIMIT {top_k};",
+    "notes": [
+      "default_facets=low_support_extremes",
+      "template_selection_mode=rule",
+      "problem_index_within_template=1",
+      "sql_variant_index=1/2",
+      "binding_index=132"
+    ],
+    "template_selection_mode": "rule",
+    "selected_template_rank": 12,
+    "problem_index_within_template": 1,
+    "sql_variant_index": 1,
+    "sql_variant_total": 2
+  },
+  "mode": "subitem_workload_v2",
+  "sql_source_version": "v2",
+  "sql_source_label": "v2_current",
+  "generated_sql_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/sql/v2q_n1_06f410de776111c5.sql",
+  "usage_summary": {
+    "dataset_id": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_06f410de776111c5",
+    "api_calls": 0,
+    "input_tokens": 20360,
+    "cached_input_tokens": 12032,
+    "output_tokens": 544,
+    "total_tokens": 20904,
+    "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": 11979.35,
+    "sql_execution_elapsed_ms_total": 3.25,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/trace.jsonl

@@ -0,0 +1 @@
+{"timestamp": "2026-05-19T16:03:59.771043+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": 11979.35, "started_at": "2026-05-19T16:03:47.790570+00:00", "ended_at": "2026-05-19T16:03:59.769951+00:00", "prompt_metrics": {"chars": 29522, "bytes_utf8": 29522, "lines": 792, "estimated_tokens": null}, "response_metrics": {"chars": 470, "bytes_utf8": 470, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20360, "cached_input_tokens": 12032, "output_tokens": 544, "reasoning_output_tokens": 419}, "stderr_preview": "", "stdout_preview": "{\"sql\":\"-- template_id: tpl_tpch_thresholded_group_ranking\\nSELECT \\\"class\\\", SUM(CAST(\\\"word_freq_you\\\" AS REAL)) AS total_measure\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\"\\nHAVING SUM(CAST(\\\"word_freq_you\\\" AS REAL)) > 2.64\\nORDER BY total_measure DESC\\nLIMIT 12;\",\"notes\":\"Used the required Thresholded Group Ranking template with group_col=\\\"class\\\" and measure_col=\\\"word_freq_you\\\". CAST to REAL is included because the schema snapshot shows these columns stored as TEXT.\"}"}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/usage_summary.json

@@ -0,0 +1,20 @@
+{
+  "dataset_id": "n1",
+  "model": "v2-cli:codex",
+  "run_id": "v2q_n1_06f410de776111c5",
+  "api_calls": 0,
+  "input_tokens": 20360,
+  "cached_input_tokens": 12032,
+  "output_tokens": 544,
+  "total_tokens": 20904,
+  "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": 11979.35,
+  "sql_execution_elapsed_ms_total": 3.25,
+  "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_06f410de776111c5/cli/conversation.jsonl",
+  "note": "Executed through a local AI CLI with structured usage metadata."
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl

@@ -0,0 +1,2 @@
+{"attempt": 1, "phase": "sql_generation", "role": "user", "content_path": "cli/sql_prompt_attempt_1.txt", "metrics": {"chars": 29767, "bytes_utf8": 29767, "lines": 794, "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": 621, "bytes_utf8": 621, "lines": 1, "estimated_tokens": null}, "usage": {"input_tokens": 20437, "cached_input_tokens": 12032, "output_tokens": 461, "reasoning_output_tokens": 288}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/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": "n1",
+    "model": "v2-cli:codex",
+    "run_id": "v2q_n1_0846f22fe82fa8a6",
+    "api_calls": 0,
+    "input_tokens": 20437,
+    "cached_input_tokens": 12032,
+    "output_tokens": 461,
+    "total_tokens": 20898,
+    "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": 18779.78,
+    "sql_execution_elapsed_ms_total": 4.41,
+    "conversation_log_path": "/data/jialinzhang/TabQueryBench/sql_workloads/v2_current/runs_and_launches/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/conversation.jsonl",
+    "note": "Executed through a local AI CLI with structured usage metadata."
+  }
+}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/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": "2026-05-19T15:34:21.443511+00:00",
+  "ended_at": "2026-05-19T15:34:40.223317+00:00",
+  "elapsed_ms": 18779.78,
+  "prompt_metrics": {
+    "chars": 29767,
+    "bytes_utf8": 29767,
+    "lines": 794,
+    "estimated_tokens": null
+  },
+  "stdout_metrics": {
+    "chars": 1667,
+    "bytes_utf8": 1667,
+    "lines": 6,
+    "estimated_tokens": null
+  },
+  "stderr_metrics": {
+    "chars": 0,
+    "bytes_utf8": 0,
+    "lines": 0,
+    "estimated_tokens": null
+  },
+  "parsed_output": {
+    "format": "jsonl_events",
+    "text_metrics": {
+      "chars": 621,
+      "bytes_utf8": 621,
+      "lines": 1,
+      "estimated_tokens": null
+    },
+    "usage": {
+      "input_tokens": 20437,
+      "cached_input_tokens": 12032,
+      "output_tokens": 461,
+      "reasoning_output_tokens": 288
+    }
+  },
+  "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_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_prompt_attempt_1.txt

@@ -0,0 +1,794 @@
+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: n1
+- dataset_name: Spambase
+- table_name: n1
+- table_layout: single-table dataset (do not assume joins).
+- row_semantics: One row is one email represented by engineered token/character frequency and capitalization-run features.
+- task_type: classification
+- target_column: class
+- main_row_count: 4601
+- important_fields:
+- word_freq_make: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'make' in an email message.
+- word_freq_address: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'address' in an email message.
+- word_freq_all: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'all' in an email message.
+- word_freq_3d: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '3d' in an email message.
+- word_freq_our: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'our' in an email message.
+- word_freq_over: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'over' in an email message.
+- word_freq_remove: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'remove' in an email message.
+- word_freq_internet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'internet' in an email message.
+- word_freq_order: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'order' in an email message.
+- word_freq_mail: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'mail' in an email message.
+- word_freq_receive: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'receive' in an email message.
+- word_freq_will: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'will' in an email message.
+- word_freq_people: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'people' in an email message.
+- word_freq_report: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'report' in an email message.
+- word_freq_addresses: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'addresses' in an email message.
+- word_freq_free: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'free' in an email message.
+- word_freq_business: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'business' in an email message.
+- word_freq_email: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'email' in an email message.
+- word_freq_you: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'you' in an email message.
+- word_freq_credit: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'credit' in an email message.
+- word_freq_your: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'your' in an email message.
+- word_freq_font: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'font' in an email message.
+- word_freq_000: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '000' in an email message.
+- word_freq_money: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'money' in an email message.
+- word_freq_hp: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hp' in an email message.
+- word_freq_hpl: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'hpl' in an email message.
+- word_freq_george: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'george' in an email message.
+- word_freq_650: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '650' in an email message.
+- word_freq_lab: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'lab' in an email message.
+- word_freq_labs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'labs' in an email message.
+- word_freq_telnet: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'telnet' in an email message.
+- word_freq_857: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '857' in an email message.
+- word_freq_data: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'data' in an email message.
+- word_freq_415: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '415' in an email message.
+- word_freq_85: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '85' in an email message.
+- word_freq_technology: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'technology' in an email message.
+- word_freq_1999: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token '1999' in an email message.
+- word_freq_parts: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'parts' in an email message.
+- word_freq_pm: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'pm' in an email message.
+- word_freq_direct: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'direct' in an email message.
+- word_freq_cs: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'cs' in an email message.
+- word_freq_meeting: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'meeting' in an email message.
+- word_freq_original: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'original' in an email message.
+- word_freq_project: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'project' in an email message.
+- word_freq_re: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 're' in an email message.
+- word_freq_edu: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'edu' in an email message.
+- word_freq_table: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'table' in an email message.
+- word_freq_conference: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Frequency feature for token 'conference' in an email message.
+- char_freq_%3B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol ';'.
+- char_freq_%28: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '('.
+- char_freq_%5B: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '['.
+- char_freq_%21: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '!'.
+- char_freq_%24: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '$'.
+- char_freq_%23: role=feature, type=numeric_sparse_frequency. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Character frequency feature for symbol '#'.
+- capital_run_length_average: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Average length of uninterrupted capital-letter runs.
+- capital_run_length_longest: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Longest uninterrupted capital-letter run.
+- capital_run_length_total: role=feature, type=numeric_heavy_tail. tags=['condition_candidate', 'measure', 'rare_pattern_candidate'] desc=Total number of capital letters in runs.
+- class: role=target, type=binary_target. tags=['target_candidate'] desc=Binary spam label (1=spam, 0=non-spam).
+- useful_field_combinations: [['word_freq_free', 'word_freq_you', 'class'], ['char_freq_%21', 'capital_run_length_longest', 'class'], ['word_freq_remove', 'word_freq_money', 'class']]
+- fields_requiring_caution: ['capital_run_length_average', 'capital_run_length_longest', 'capital_run_length_total']
+- source_url: https://www.openml.org/d/44
+
+SQLite schema snapshot:
+{
+  "table_name": "n1",
+  "quoted_table_name": "\"n1\"",
+  "row_count": 4601,
+  "columns": [
+    {
+      "name": "word_freq_make",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_all",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_our",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_over",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_remove",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_internet",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_mail",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_receive",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "word_freq_will",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_people",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "word_freq_report",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+      "name": "word_freq_addresses",
+      "type": "TEXT",
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+      "pk": false
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+      "type": "TEXT",
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+      "pk": false
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+    {
+      "name": "word_freq_business",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
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+      "type": "TEXT",
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+      "pk": false
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+    {
+      "name": "word_freq_credit",
+      "type": "TEXT",
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+      "pk": false
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+    {
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+    {
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+    {
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+    {
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+      "type": "TEXT",
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+      "type": "TEXT",
+      "notnull": false,
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+    {
+      "name": "word_freq_meeting",
+      "type": "TEXT",
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+    {
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+      "type": "TEXT",
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+      "pk": false
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+    {
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+      "type": "TEXT",
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+      "pk": false
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+    {
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+      "pk": false
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+    {
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+      "type": "TEXT",
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+      "pk": false
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+      "type": "TEXT",
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "char_freq_%3B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "char_freq_%28",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "char_freq_%5B",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "char_freq_%21",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
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+    {
+      "name": "char_freq_%24",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
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+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_average",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_longest",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "capital_run_length_total",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    },
+    {
+      "name": "class",
+      "type": "TEXT",
+      "notnull": false,
+      "pk": false
+    }
+  ],
+  "sample_rows": [
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+      "word_freq_over": "0",
+      "word_freq_remove": "0",
+      "word_freq_internet": "0",
+      "word_freq_order": "0",
+      "word_freq_mail": "0",
+      "word_freq_receive": "0",
+      "word_freq_will": "0.64",
+      "word_freq_people": "0",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.32",
+      "word_freq_business": "0",
+      "word_freq_email": "1.29",
+      "word_freq_you": "1.93",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.96",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
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+      "word_freq_415": "0",
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+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
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+      "word_freq_meeting": "0",
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+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.778",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.756",
+      "capital_run_length_longest": "61",
+      "capital_run_length_total": "278",
+      "class": "1"
+    },
+    {
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+      "word_freq_address": "0.28",
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+      "word_freq_order": "0",
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+      "word_freq_receive": "0.21",
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+      "word_freq_people": "0.65",
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+      "word_freq_credit": "0",
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+      "char_freq_%23": "0.048",
+      "capital_run_length_average": "5.114",
+      "capital_run_length_longest": "101",
+      "capital_run_length_total": "1028",
+      "class": "1"
+    },
+    {
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+      "word_freq_3d": "0",
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+      "word_freq_over": "0.19",
+      "word_freq_remove": "0.19",
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+      "word_freq_mail": "0.25",
+      "word_freq_receive": "0.38",
+      "word_freq_will": "0.45",
+      "word_freq_people": "0.12",
+      "word_freq_report": "0",
+      "word_freq_addresses": "1.75",
+      "word_freq_free": "0.06",
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+      "word_freq_you": "1.36",
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+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
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+      "word_freq_labs": "0",
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+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0.06",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0.12",
+      "word_freq_project": "0",
+      "word_freq_re": "0.06",
+      "word_freq_edu": "0.06",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0.01",
+      "char_freq_%28": "0.143",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.276",
+      "char_freq_%24": "0.184",
+      "char_freq_%23": "0.01",
+      "capital_run_length_average": "9.821",
+      "capital_run_length_longest": "485",
+      "capital_run_length_total": "2259",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
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+      "word_freq_hpl": "0",
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+      "word_freq_650": "0",
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+      "word_freq_technology": "0",
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+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
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+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.137",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    },
+    {
+      "word_freq_make": "0",
+      "word_freq_address": "0",
+      "word_freq_all": "0",
+      "word_freq_3d": "0",
+      "word_freq_our": "0.63",
+      "word_freq_over": "0",
+      "word_freq_remove": "0.31",
+      "word_freq_internet": "0.63",
+      "word_freq_order": "0.31",
+      "word_freq_mail": "0.63",
+      "word_freq_receive": "0.31",
+      "word_freq_will": "0.31",
+      "word_freq_people": "0.31",
+      "word_freq_report": "0",
+      "word_freq_addresses": "0",
+      "word_freq_free": "0.31",
+      "word_freq_business": "0",
+      "word_freq_email": "0",
+      "word_freq_you": "3.18",
+      "word_freq_credit": "0",
+      "word_freq_your": "0.31",
+      "word_freq_font": "0",
+      "word_freq_000": "0",
+      "word_freq_money": "0",
+      "word_freq_hp": "0",
+      "word_freq_hpl": "0",
+      "word_freq_george": "0",
+      "word_freq_650": "0",
+      "word_freq_lab": "0",
+      "word_freq_labs": "0",
+      "word_freq_telnet": "0",
+      "word_freq_857": "0",
+      "word_freq_data": "0",
+      "word_freq_415": "0",
+      "word_freq_85": "0",
+      "word_freq_technology": "0",
+      "word_freq_1999": "0",
+      "word_freq_parts": "0",
+      "word_freq_pm": "0",
+      "word_freq_direct": "0",
+      "word_freq_cs": "0",
+      "word_freq_meeting": "0",
+      "word_freq_original": "0",
+      "word_freq_project": "0",
+      "word_freq_re": "0",
+      "word_freq_edu": "0",
+      "word_freq_table": "0",
+      "word_freq_conference": "0",
+      "char_freq_%3B": "0",
+      "char_freq_%28": "0.135",
+      "char_freq_%5B": "0",
+      "char_freq_%21": "0.135",
+      "char_freq_%24": "0",
+      "char_freq_%23": "0",
+      "capital_run_length_average": "3.537",
+      "capital_run_length_longest": "40",
+      "capital_run_length_total": "191",
+      "class": "1"
+    }
+  ]
+}
+
+Shortlisted templates:
+[
+  {
+    "template_id": "tpl_tpcds_within_group_share",
+    "template_name": "Within-Group Share of Total",
+    "primary_family": "conditional_dependency_structure",
+    "portability": "partial",
+    "sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;",
+    "required_roles": [
+      "group_col",
+      "item_col",
+      "measure_col"
+    ]
+  }
+]
+
+Problem instance:
+{
+  "dataset_id": "n1",
+  "question": "Use template Within-Group Share of Total to probe dependency_strength_similarity with semantic role focused_target_view. Focus on group_col=class, measure_col=word_freq_hpl.",
+  "planned_template_id": "tpl_tpcds_within_group_share",
+  "bindings": {
+    "group_col": "class",
+    "measure_col": "word_freq_hpl",
+    "item_col": "word_freq_george",
+    "top_k": 10,
+    "top_n": 4,
+    "num_tiles": 10,
+    "percentile_value": 0.9,
+    "z_threshold": 2.0,
+    "fraction_threshold": 0.1,
+    "baseline_multiplier": 1.5,
+    "baseline_fraction": 0.1,
+    "min_group_size": 5,
+    "min_support": 5,
+    "measure_threshold": 0.0,
+    "time_grain": "month",
+    "lookback_rows": 3,
+    "current_period_start": "'2024-01-01'",
+    "current_period_end": "'2024-04-01'",
+    "previous_period_start": "'2023-10-01'",
+    "previous_period_end": "'2024-01-01'",
+    "drift_ratio_threshold": 0.8
+  },
+  "can_vary": [],
+  "must_fix": [],
+  "runtime_sql_skeleton": "SELECT {group_col}, {item_col},\n       SUM({measure_col}) AS total_measure,\n       SUM({measure_col}) * 100.0 / SUM(SUM({measure_col})) OVER (PARTITION BY {group_col}) AS share_within_group\nFROM {table}\nGROUP BY {group_col}, {item_col}\nORDER BY share_within_group DESC;"
+}
+
+Repair context:
+{}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.raw.txt

@@ -0,0 +1,6 @@
+{"type":"thread.started","thread_id":"019e40df-af2d-7722-8d75-c068294916c5"}
+{"type":"turn.started"}
+{"type":"error","message":"Reconnecting... 2/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1985816, Requested 21072. Please try again in 206ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"error","message":"Reconnecting... 3/5 (stream disconnected before completion: Rate limit reached for gpt-5.4 in organization org-yXK0mBGOR7eXhtGEx8v7e3bf on tokens per min (TPM): Limit 2000000, Used 1993331, Requested 21072. Please try again in 432ms. Visit https://platform.openai.com/account/rate-limits to learn more.)"}
+{"type":"item.completed","item":{"id":"item_0","type":"agent_message","text":"{\"sql\":\"-- template_id: tpl_tpcds_within_group_share\\nSELECT \\\"class\\\", \\\"word_freq_george\\\",\\n       SUM(CAST(\\\"word_freq_hpl\\\" AS REAL)) AS total_measure,\\n       SUM(CAST(\\\"word_freq_hpl\\\" AS REAL)) * 100.0 / SUM(SUM(CAST(\\\"word_freq_hpl\\\" AS REAL))) OVER (PARTITION BY \\\"class\\\") AS share_within_group\\nFROM \\\"n1\\\"\\nGROUP BY \\\"class\\\", \\\"word_freq_george\\\"\\nORDER BY share_within_group DESC;\",\"notes\":\"Applied the provided template with group_col=\\\"class\\\", item_col=\\\"word_freq_george\\\", and measure_col=\\\"word_freq_hpl\\\". Cast the measure column to REAL because the schema stores feature values as TEXT in SQLite.\"}"}}
+{"type":"turn.completed","usage":{"input_tokens":20437,"cached_input_tokens":12032,"output_tokens":461,"reasoning_output_tokens":288}}

Query/sql/v2/runs/v2_cli_20260502_081223_d/n1/artifacts/v2q_n1_0846f22fe82fa8a6/cli/sql_response_attempt_1.txt

@@ -0,0 +1 @@
+{"sql":"-- template_id: tpl_tpcds_within_group_share\nSELECT \"class\", \"word_freq_george\",\n       SUM(CAST(\"word_freq_hpl\" AS REAL)) AS total_measure,\n       SUM(CAST(\"word_freq_hpl\" AS REAL)) * 100.0 / SUM(SUM(CAST(\"word_freq_hpl\" AS REAL))) OVER (PARTITION BY \"class\") AS share_within_group\nFROM \"n1\"\nGROUP BY \"class\", \"word_freq_george\"\nORDER BY share_within_group DESC;","notes":"Applied the provided template with group_col=\"class\", item_col=\"word_freq_george\", and measure_col=\"word_freq_hpl\". Cast the measure column to REAL because the schema stores feature values as TEXT in SQLite."}