{"seq": 1, "run_id": "janus-s11-20260725", "room": "room1", "tool": "briefing", "args": "{}", "result_digest": "51e033efed9f4e2a", "ok": true, "elapsed_ms": 0.011, "ts_wall": "2026-07-24T23:31:09.362481", "ts_mono": 1090531.468122875}
{"seq": 2, "run_id": "janus-s11-20260725", "room": "room1", "tool": "list_tables", "args": "{}", "result_digest": "b687f7ec10fb883f", "ok": true, "elapsed_ms": 0.228, "ts_wall": "2026-07-24T23:31:09.794346", "ts_mono": 1090531.8997595}
{"seq": 3, "run_id": "janus-s11-20260725", "room": "room1", "tool": "table_schema", "args": "{'name': 'daily'}", "result_digest": "3a3c568f8f3b05a9", "ok": true, "elapsed_ms": 0.191, "ts_wall": "2026-07-24T23:31:10.789824", "ts_mono": 1090532.895295134}
{"seq": 4, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': 'SELECT * FROM daily LIMIT 10;'}", "result_digest": "72fe123a226df81a", "ok": true, "elapsed_ms": 0.205, "ts_wall": "2026-07-24T23:31:11.618251", "ts_mono": 1090533.723708615}
{"seq": 5, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': \"SELECT 'resting_hr' as metric, AVG(resting_hr) as avg, STDDEV(resting_hr) as std FROM daily\\nUNION ALL\\nSELECT 'hrv', AVG(hrv), STDDEV(hrv) FROM daily\\nUNION ALL\\nSELECT 'sleep_hours', AVG(sleep_hours), STDDEV(sleep_hours) FROM daily\\nUNION ALL\\nSELECT 'glucose_mean', AVG(glucose_mean), STDDEV(glucose_mean) FROM daily\\nUNION ALL\\nSELECT 'steps', AVG(steps), STDDEV(steps) FROM daily;\"}", "result_digest": "019a9a6cf0a08e8a", "ok": false, "elapsed_ms": 0.183, "ts_wall": "2026-07-24T23:31:17.410423", "ts_mono": 1090539.515901902}
{"seq": 6, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': 'SELECT \\n    AVG(resting_hr) as avg_hr, MIN(resting_hr) as min_hr, MAX(resting_hr) as max_hr,\\n    AVG(hrv) as avg_hrv, MIN(hrv) as min_hrv, MAX(hrv) as max_hrv,\\n    AVG(sleep_hours) as avg_sleep, MIN(sleep_hours) as min_sleep, MAX(sleep_hours) as max_sleep,\\n    AVG(glucose_mean) as avg_gluc, MIN(glucose_mean) as min_gluc, MAX(glucose_mean) as max_gluc,\\n    AVG(steps) as avg_steps, MIN(steps) as min_steps, MAX(steps) as max_steps\\nFROM daily;'}", "result_digest": "4653b01a10848a66", "ok": true, "elapsed_ms": 0.26, "ts_wall": "2026-07-24T23:31:22.128507", "ts_mono": 1090544.233907912}
{"seq": 7, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': 'SELECT \\n    AVG(resting_hr * resting_hr) - AVG(resting_hr) * AVG(resting_hr) as var_hr,\\n    AVG(hrv * hrv) - AVG(hrv) * AVG(hrv) as var_hrv,\\n    AVG(sleep_hours * sleep_hours) - AVG(sleep_hours) * AVG(sleep_hours) as var_sleep,\\n    AVG(glucose_mean * glucose_mean) - AVG(glucose_mean) * AVG(glucose_mean) as var_gluc,\\n    AVG(steps * steps) - AVG(steps) * AVG(steps) as var_steps\\nFROM daily;'}", "result_digest": "95f51d3a0ea4c4d4", "ok": true, "elapsed_ms": 0.235, "ts_wall": "2026-07-24T23:31:32.805662", "ts_mono": 1090554.911090642}
{"seq": 8, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': 'SELECT * FROM daily ORDER BY date ASC;'}", "result_digest": "a496dfd9beb1ec1d", "ok": true, "elapsed_ms": 1.105, "ts_wall": "2026-07-24T23:31:36.537263", "ts_mono": 1090558.641819182}
{"seq": 9, "run_id": "janus-s11-20260725", "room": "room1", "tool": "run_sql", "args": "{'query': \"SELECT \\n    AVG(CASE WHEN date < '2026-03-10' THEN resting_hr END) as avg_before,\\n    AVG(CASE WHEN date BETWEEN '2026-03-10' AND '2026-03-21' THEN resting_hr END) as avg_during\\nFROM daily;\"}", "result_digest": "04fe0ef6bf3f6ea4", "ok": true, "elapsed_ms": 0.209, "ts_wall": "2026-07-24T23:31:55.179223", "ts_mono": 1090577.28467686}
{"seq": 10, "run_id": "janus-s11-20260725", "room": "room1", "tool": "submit_finding", "args": "{'metric': 'resting_hr', 'start_date': '2026-03-10', 'magnitude': 24.0}", "result_digest": "dcbd0fb7f2ebb490", "ok": true, "elapsed_ms": 0.132, "ts_wall": "2026-07-24T23:32:11.174006", "ts_mono": 1090593.279539025}
{"seq": 1, "run_id": "janus-s11-20260725", "room": "room2", "tool": "briefing", "args": "{}", "result_digest": "4210eb3d45cdcdc4", "ok": true, "elapsed_ms": 0.011, "ts_wall": "2026-07-24T23:32:14.094994", "ts_mono": 1090596.20063369}
{"seq": 2, "run_id": "janus-s11-20260725", "room": "room2", "tool": "read_inbox", "args": "{}", "result_digest": "489c78e58bdfa936", "ok": true, "elapsed_ms": 0.108, "ts_wall": "2026-07-24T23:32:14.602572", "ts_mono": 1090596.708126156}
{"seq": 3, "run_id": "janus-s11-20260725", "room": "room2", "tool": "read_spec", "args": "{}", "result_digest": "d813a70ddb6d25eb", "ok": true, "elapsed_ms": 0.07, "ts_wall": "2026-07-24T23:32:15.133775", "ts_mono": 1090597.239366886}
{"seq": 4, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # Date patterns: ISO (YYYY-MM-DD) or US Slash (MM/DD/YY)\\n    iso_pattern = r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\'\\n    us_slash_pattern = r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2}))\\'\\n    \\n    date_str = None\\n    match_iso = re.search(iso_pattern, line)\\n    if match_iso:\\n        date_str = match_iso.group(1)\\n    else:\\n        match_us = re.search(us_slash_pattern, line)\\n        if match_us:\\n            parts = match_us.group(0).split(\\'/\\')\\n            month, day, year = parts[0], parts[1], parts[2]\\n            date_str = f\"{year}-{month}-{day}\"\\n            # Handle 2-digit years (assume 20YY)\\n            if len(year) == 2:\\n                date_str = f\"20{year}-{month}-{day}\"\\n        else:\\n            return None\\n\\n    # Metric mapping\\n    metric_map = {\\n        \\'steps\\': \\'steps\\',\\n        \\'step count\\': \\'steps\\',\\n        \\'resting hr\\': \\'resting_hr\\',\\n        \\'rhr\\': \\'resting_hr\\',\\n        \\'resting heart rate\\': \\'resting_hr\\',\\n        \\'hrv\\': \\'hrv\\',\\n        \\'heart rate variability\\': \\'hrv\\',\\n        \\'sleep\\': \\'sleep_hours\\',\\n        \\'slept\\': \\'sleep_hours\\',\\n        \\'sleep hours\\': \\'sleep_hours\\',\\n        \\'glucose\\': \\'glucose_mean\\',\\n        \\'avg glucose\\': \\'glucose_mean\\'\\n    }\\n\\n    # Find the metric (longest match first)\\n    found_metric = None\\n    sorted_aliases = sorted(metric_map.keys(), key=len, reverse=True)\\n    for alias in sorted_aliases:\\n        if re.search(rf\\'\\\\b{re.escape(alias)}\\\\b\\', line, re.IGNORECASE):\\n            found_metric = metric_map[alias]\\n            break\\n    \\n    if not found_metric:\\n        return None\\n\\n    # Extract value: find number after removing date and metric? \\n    # Or just search for digits/decimals in the string excluding date parts.\\n    # Let\\'s strip known non-numeric characters from segments to be safe.\\n    \\n    # Cleaner approach: extract all numbers (including decimals) that are not part of the date\\n    ", "result_digest": "da5e5f08e9fa589a", "ok": true, "elapsed_ms": 0.085, "ts_wall": "2026-07-24T23:32:57.996466", "ts_mono": 1090640.102034858}
{"seq": 5, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Find Date\\n    iso_pattern = r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}\\'\\n    us_slash_pattern = r\\'(\\\\d{2})/(\\\\d{2})/(\\\\d{2,4})\\'\\n    \\n    date_str = None\\n    match_obj = None\\n\\n    # Try ISO first\\n    iso_match = re.search(iso_pattern, line)\\n    if iso_match:\\n        date_str = iso_match.group(0)\\n        match_obj = iso_match\\n    else:\\n        us_match = re.search(us_slash_pattern, line)\\n        if us_match:\\n            m, d, y = us_match.groups()\\n            if len(y) == 2:\\n                y = \"20\" + y\\n            date_str = f\"{y}-{m}-{d}\"\\n            match_obj = us_match\\n\\n    if not date_str:\\n        return None\\n\\n    # 2. Find Metric\\n    metric_map = {\\n        \\'steps\\': \\'steps\\',\\n        \\'step count\\': \\'steps\\',\\n        \\'resting hr\\': \\'resting_hr\\',\\n        \\'rhr\\': \\'resting_hr\\',\\n        \\'resting heart rate\\': \\'resting_hr\\',\\n        \\'hrv\\': \\'hrv\\',\\n        \\'heart rate variability\\': \\'hrv\\',\\n        \\'sleep\\': \\'sleep_hours\\',\\n        \\'slept\\': \\'sleep_hours\\',\\n        \\'sleep hours\\': \\'sleep_hours\\',\\n        \\'glucose\\': \\'glucose_mean\\',\\n        \\'avg glucose\\': \\'glucose_mean\\'\\n    }\\n\\n    found_metric = None\\n    # Sort aliases by length descending to ensure longest match (e.g., \\'resting heart rate\\' before \\'rhr\\')\\n    sorted_aliases = sorted(metric_map.keys(), key=len, reverse=True)\\n    for alias in sorted_aliases:\\n        if re.search(rf\\'\\\\b{re.escape(alias)}\\\\b\\', line, re.IGNORECASE):\\n            found_metric = metric_map[alias]\\n            break\\n    \\n    if not found_metric:\\n        return None\\n\\n    # 3. Find Value\\n    # Remove the date part from the string to avoid picking up numbers in the date\\n    remaining_text = line.replace(match_obj.group(0), \"\")\\n    \\n    # Regex for a number that might have commas or decimals\\n    # We want the last number sequence found in the remaining text (typical for these logs)\\n    num_pattern = r", "result_digest": "a59c7ef759f65cd4", "ok": true, "elapsed_ms": 0.102, "ts_wall": "2026-07-24T23:33:17.048347", "ts_mono": 1090659.153896184}
{"seq": 6, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "1e350c8e5d5584e3", "ok": true, "elapsed_ms": 155.017, "ts_wall": "2026-07-24T23:33:20.246853", "ts_mono": 1090662.197496697}
{"seq": 7, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    iso_match = re.search(r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}\\', line)\\n    us_match = re.search(r\\'(\\\\d{2})/(\\\\d{2})/(\\\\d{2,4})\\', line)\\n    \\n    if iso_match:\\n        date_str = iso_match.group(0)\\n        date_range = (iso_match.start(), iso_match.end())\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = f\"20{y}\" if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n        date_range = (us_match.start(), us_match.end())\\n    else:\\n        return None\\n\\n    # 2. Extract Metric\\n    metric_map = {\\n        \\'steps\\': \\'steps\\', \\'step count\\': \\'steps\\',\\n        \\'resting hr\\': \\'resting_hr\\', \\'rhr\\': \\'resting_hr\\', \\'resting heart rate\\': \\'resting_hr\\',\\n        \\'hrv\\': \\'hrv\\', \\'heart rate variability\\': \\'hrv\\',\\n        \\'sleep\\': \\'sleep_hours\\', \\'slept\\': \\'sleep_hours\\', \\'sleep hours\\': \\'sleep_hours\\',\\n        \\'glucose\\': \\'glucose_mean\\', \\'avg glucose\\': \\'glucose_mean\\'\\n    }\\n    found_metric = None\\n    sorted_aliases = sorted(metric_map.keys(), key=len, reverse=True)\\n    for alias in sorted_aliases:\\n        if re.search(rf\\'\\\\b{re.escape(alias)}\\\\b\\', line, re.IGNORECASE):\\n            found_metric = metric_map[alias]\\n            break\\n    if not found_metric:\\n        return None\\n\\n    # 3. Extract Value (the number that isn\\'t the date)\\n    # We find all numbers in the string and pick one that doesn\\'t overlap with the date range\\n    num_pattern = r\\'\\\\d{1,3}(?:,\\\\d{3})*(?:\\\\.\\\\d+)?|\\\\d+\\\\.\\\\d+|\\\\d+\\'\\n    for match in re.finditer(num_pattern, line):\\n        start, end = match.span()\\n        # Check if this number is part of the date string\\n        if start < date_range[1] and end > date_range[0]:\\n            continue\\n        try:\\n            val_str = match.group(0).replace(\\',\\', \\'\\')\\n            return {\\n                \"date\": date_str,\\n                \"metric\": found_metric,\\n                \"value\": float(val_", "result_digest": "b391c1bcfe923528", "ok": true, "elapsed_ms": 0.113, "ts_wall": "2026-07-24T23:33:58.630622", "ts_mono": 1090700.736165371}
{"seq": 8, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2})/(\\\\d{2})/(\\\\d{2,4})\\', line)\\n    \\n    date_str = None\\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        if len(y) == 2:\\n            y = \"20\" + y\\n        date_str = f\"{y}-{m}-{d}\"\\n    else:\\n        return None\\n\\n    # 2. Extract Metric\\n    metric_map = {\\n        \\'steps\\': \\'steps\\', \\'step count\\': \\'steps\\',\\n        \\'resting hr\\': \\'resting_hr\\', \\'rhr\\': \\'resting_hr\\', \\'resting heart rate\\': \\'resting_hr\\',\\n        \\'hrv\\': \\'hrv\\', \\'heart rate variability\\': \\'hrv\\',\\n        \\'sleep\\': \\'sleep_hours\\', \\'slept\\': \\'sleep_hours\\', \\'sleep hours\\': \\'sleep_hours\\',\\n        \\'glucose\\': \\'glucose_mean\\', \\'avg glucose\\': \\'glucose_mean\\'\\n    }\\n    found_metric = None\\n    sorted_aliases = sorted(metric_map.keys(), key=len, reverse=True)\\n    for alias in sorted_aliases:\\n        if re.search(rf\\'\\\\b{re.escape(alias)}\\\\b\\', line, re.IGNORECASE):\\n            found_metric = metric_map[alias]\\n            break\\n    if not found_metric:\\n        return None\\n\\n    # 3. Extract Value\\n    # Find the last number in the string that isn\\'t part of the date\\n    # First, identify the parts of the line that are NOT the date\\n    date_pattern = r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}|\\\\d{2}/\\\\d{2}/\\\\d{2,4}\\'\\n    parts = re.split(date_pattern, line)\\n    \\n    # The value should be in one of the parts (usually the last part or second to last)\\n    for part in reversed(parts):\\n        num_'}", "result_digest": "0d0b0bbb92deb7f5", "ok": true, "elapsed_ms": 0.103, "ts_wall": "2026-07-24T23:34:41.145946", "ts_mono": 1090743.251501219}
{"seq": 9, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Find Date\\n    iso_match = re.search(r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}\\', line)\\n    us_match = re.search(r\\'(\\\\d{2})/(\\\\d{2})/(\\\\d{2,4})\\', line)\\n    \\n    if iso_match:\\n        date_str = iso_match.group(0)\\n        d_start, d_end = iso_match.span()\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = f\"20{y}\" if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n        d_start, d_end = us_match.span()\\n    else:\\n        return None\\n\\n    # 2. Find Metric\\n    metric_map = {\\n        \\'steps\\': \\'steps\\', \\'step count\\': \\'steps\\',\\n        \\'resting hr\\': \\'resting_hr\\', \\'rhr\\': \\'resting_hr\\', \\'resting heart rate\\': \\'resting_hr\\',\\n        \\'hrv\\': \\'hrv\\', \\'heart rate variability\\': \\'hrv\\',\\n        \\'sleep\\': \\'sleep_hours\\', \\'slept\\': \\'sleep_hours\\', \\'sleep hours\\': \\'sleep_hours\\',\\n        \\'glucose\\': \\'glucose_mean\\', \\'avg glucose\\': \\'glucose_mean\\'\\n    }\\n    found_metric = None\\n    sorted_aliases = sorted(metric_map.keys(), key=len, reverse=True)\\n    for alias in sorted_aliases:\\n        if re.search(rf\\'\\\\b{re.escape(alias)}\\\\b\\', line, re.IGNORECASE):\\n            found_metric = metric_map[alias]\\n            break\\n    if not found_metric:\\n        return None\\n\\n    # 3. Find Value\\n    text_without_date = line[:d_start] + line[d_end:]\\n    num_pattern = r\\'\\\\d{1,3}(?:,\\\\d{3})*(?:\\\\.\\\\d+)?|\\\\d+\\\\.\\\\d+|\\\\d+\\'\\n    numbers = re.findall(num_pattern, text_without_date)\\n    if not numbers:\\n        return None\\n\\n    try:\\n        val_str = numbers[-1].replace(\\',\\', \\'\\')\\n        value = float(val_str)\\n        return {\"date\": date_str, \"metric\": found_metric, \"value\": value}\\n    except (ValueError, IndexError):\\n        return None\\n'}", "result_digest": "f55d73755b382772", "ok": true, "elapsed_ms": 0.118, "ts_wall": "2026-07-24T23:35:05.724440", "ts_mono": 1090767.829979951}
{"seq": 10, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\nfrom datetime import datetime\\n\\ndef parse_line(line: str) -> dict | None:\\n    # Regex patterns\\n    iso_pattern = r\\'(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\\'\\n    us_slash_pattern = r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\'\\n\\n    date_val = None\\n    norm_date = None\\n    \\n    # Try ISO first\\n    iso_match = re.search(iso_pattern, line)\\n    if iso_match:\\n        date_val = iso_match.group()\\n        norm_date = date_val\\n    else:\\n        # Try US Slash\\n        us_match = re.search(us_slash_pattern, line)\\n        if us_match:\\n            m, d, y = us_match.groups()\\n            if len(y) == 2:\\n                year = int(y) + 2000 if int(y) <= 69 else int(y) + 1900\\n                norm_date = f\"{year}-{m.zfill(2)}-{d.zfill(2)}\"\\n            else:\\n                norm_date = f\"{y}-{m.zfill(2)}-{d.zfill(2)}\"\\n            date_val = us_match.group()\\n\\n    if not date_val or not norm_date:\\n        return None\\n\\n    # Find the end position of the date match to only look at text after it\\n    # We use finditer to ensure we get the right occurrence if there are multiple, \\n    # but typically a line has one primary date.\\n    match = re.search(re.escape(date_val), line)\\n    if not match: # Fallback for complex regex matches\\n        return None\\n        \\n    end_index = match.end()\\n    remaining_text = line[end_index:]\\n\\n    # Find all numbers (integers or floats) in the remaining text\\n    numbers = re.findall(r\\'-?\\\\d+\\\\.?\\\\d*\\', remaining_text)\\n    total_value = sum(float(n) for n in numbers)\\n\\n    return {\\n        \\'date\\': norm_date,\\n        \\'value\\': total_value\\n    }\\n'}", "result_digest": "e9454fe01c1f7457", "ok": true, "elapsed_ms": 0.117, "ts_wall": "2026-07-24T23:36:10.683058", "ts_mono": 1090832.788599846}
{"seq": 11, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "cfd446c93e2e7e60", "ok": true, "elapsed_ms": 116.251, "ts_wall": "2026-07-24T23:36:12.700752", "ts_mono": 1090834.690158028}
{"seq": 12, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # Regex patterns for dates\\n    iso_pattern = r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}\\'\\n    us_slash_pattern = r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\'\\n\\n    date_val = None\\n    norm_date = None\\n    \\n    # Try ISO first\\n    iso_match = re.search(iso_pattern, line)\\n    if iso_match:\\n        date_val = iso_match.group()\\n        norm_date = date_val\\n    else:\\n        # Try US Slash\\n        us_match = re.search(us_slash_pattern, line)\\n        if us_match:\\n            m, d, y = us_match.groups()\\n            if len(y) == 2:\\n                year = int(y) + 2000 if int(y) <= 69 else int(y) + 1900\\n                norm_date = f\"{year}-{m.zfill(2)}-{d.zfill(2)}\"\\n            else:\\n                norm_date = f\"{y}-{m.zfill(2)}-{d.zfill(2)}\"\\n            date_val = us_match.group()\\n\\n    if not date_val or not norm_date:\\n        return None\\n\\n    # Find the end position of the date match to only look at text after it\\n    match = re.search(re.escape(date_val), line)\\n    if not match:\\n        return None\\n    end_index = match.end()\\n    remaining_text = line[end_index:]\\n\\n    # Find all numbers (integers or floats) in the remaining text\\n    numbers = re.findall(r\\'-?\\\\d+\\\\.?\\\\d*\\', remaining_text)\\n    if not numbers:\\n        return None # Or return 0.0? The tests suggest we need a metric too.\\n    \\n    total_value = sum(float(n) for n in numbers if n != \\'\\')\\n\\n    # Metric mapping\\n    metric = None\\n    lower_line = line.lower()\\n    if \\'rhr\\' in lower_line or \\'resting heart rate\\' in lower_line:\\n        metric = \\'resting_hr\\'\\n    elif \\'steps\\' in lower_line:\\n        metric = \\'steps\\'\\n    elif \\'heart rate variability\\' in lower_line:\\n        metric = \\'hrv\\'\\n    elif \\'avg glucose\\' in lower_line or \\'glucose\\' in lower_line:\\n        # The test case 4 and 9 specifically use \"avg glucose\" -> \"glucose_mean\"\\n        if \\'avg glucose\\' in lower_line or \\'glucose\\' in lower_line:\\n     ", "result_digest": "224df5bf960d75cb", "ok": true, "elapsed_ms": 0.11, "ts_wall": "2026-07-24T23:36:34.112833", "ts_mono": 1090856.218379504}
{"seq": 13, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Try to find the date (ISO or US slash)\\n    # ISO pattern: YYYY-MM-DD\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    # US pattern: MM/DD/YY or MM/DD/YYYY\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/\\\\d{2,4})\\', line)\\n    \\n    date_str = None\\n    match_end = 0\\n    \\n    if iso_match:\\n        date_str = iso_match.group(1)\\n        match_end = iso_match.end()\\n    elif us_match:\\n        date_str = us_match.group(1)\\n        # Handle US date format conversion if needed? \\n        # The spec usually wants the matched string or a standard one.\\n        # Let\\'s check the spec for requirements on date format in the output.\\n        # Since I can\\'t see it, I\\'ll assume the matched string is fine first.\\n        match_end = us_match.end()\\n    \\n    if not date_str:\\n        return None\\n\\n    # 2. Get the part after the date (ignoring brackets if present)\\n    # The line might look like \"[2026-03-08] steps 18682\" or \"2026-03-08 steps 18682\"\\n    # We\\'ll search for the metric and value in the rest of the string.\\n    remaining = line[match_end:].strip()\\n    if remaining.startswith(\\']\\'):\\n        remaining = remaining[1:].strip()\\n    elif remaining.startswith(\\' \\'):\\n        remaining = remaining.strip()\\n\\n    # The pattern for \"metric value\" is usually a word followed by a number.\\n    # Example: \"steps 18682\" or \"miles 50.0\"\\n    # We use \\\\w+ for the metric and [\\\\d.]+ for the value.\\n    data_match = re.search(r\\'([a-zA-Z]+)\\\\s+(\\\\d+(?:\\\\.\\\\d+)?)\\', remaining)\\n    if not data_match:\\n        return None\\n    \\n    metric = data_match.group(1)\\n    value = float(data_match.group(2))\\n\\n    # Special handling for US dates to normalize them if necessary? \\n    # Let\\'s look at the error again. The errors were about values, not dates.\\n    # But let\\'s be safe and check if we should convert MM/DD/YY to YYYY-MM-DD.\\n    if \\'/\\' in date_str:\\n        parts = date_str", "result_digest": "5636ed8d51406eb0", "ok": true, "elapsed_ms": 0.107, "ts_wall": "2026-07-24T23:38:03.115917", "ts_mono": 1090945.221467057}
{"seq": 14, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "98a87f8d846b8ea8", "ok": true, "elapsed_ms": 117.528, "ts_wall": "2026-07-24T23:38:04.560747", "ts_mono": 1090946.548878044}
{"seq": 15, "run_id": "janus-s11-20260725", "room": "room2", "tool": "read_spec", "args": "{}", "result_digest": "d813a70ddb6d25eb", "ok": true, "elapsed_ms": 0.069, "ts_wall": "2026-07-24T23:38:15.781531", "ts_mono": 1090957.887123677}
{"seq": 16, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2,4}))\\', line)\\n    \\n    date_str = None\\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        if len(y) == 2:\\n            y = \"20\" + y\\n        date_str = f\"{y}-{m}-{d}\"\\n    \\n    if not date_str:\\n        return None\\n\\n    # 2. Define Metric Mapping\\n    # Ordered from longest to shortest to avoid partial matches (e.g., \\'sleep hours\\' before \\'sleep\\')\\n    METRIC_MAPPING = [\\n        (\\'step count\\', \\'steps\\'),\\n        (\\'resting heart rate\\', \\'resting_hr\\'),\\n        (\\'heart rate variability\\', \\'hrv\\'),\\n        (\\'sleep hours\\', \\'sleep_hours\\'),\\n        (\\'avg glucose\\', \\'glucose_mean\\'),\\n        (\\'resting hr\\', \\'resting_hr\\'),\\n        (\\'rhr\\', \\'resting_hr\\'),\\n        (\\'steps\\', \\'steps\\'),\\n        (\\'hrv\\', \\'hrv\\'),\\n        (\\'slept\\', \\'sleep_hours\\'),\\n        (\\'sleep\\', \\'sleep_hours\\'),\\n        (\\'glucose\\', \\'glucose_mean\\'),\\n    ]\\n\\n    # 3. Search for Metric and Value\\n    # We need to find the metric keyword in the line, then look for a number nearby.\\n    # The spec says: \"Values may contain thousands commas, a unit suffix, or an \\'(approx)\\' tag\"\\n    \\n    found_metric = None\\n    found_value = None\\n\\n    # Try each mapping\\n    for alias, canonical in METRIC_MAPPING:\\n        # Use word boundaries for the metric to avoid matching \\'rhr\\' inside something else (if possible)\\n        # But some metrics are multi-word. Let\\'s use a case-insensitive search for the alias.\\n        pattern = r\\'\\\\b\\' + re.escape(alias) + r\\'\\\\b\\'\\n        match = re.search(pattern, line, re.IGNORECASE)\\n        if match:\\n            # Found an alias! Now find the number after it.\\n            # Look at the string following the metric name\\n            after_text = line[match", "result_digest": "441e40d098c2f84e", "ok": true, "elapsed_ms": 0.112, "ts_wall": "2026-07-24T23:38:37.959361", "ts_mono": 1090980.064905313}
{"seq": 17, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2,4}))\\', line)\\n    \\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = \"20\" + y if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n    else:\\n        return None\\n\\n    # 2. Define Metric Mapping (Longest aliases first to ensure correct matching)\\n    mapping = [\\n        (\\'step count\\', \\'steps\\'),\\n        (\\'resting heart rate\\', \\'resting_hr\\'),\\n        (\\'heart rate variability\\', \\'hrv\\'),\\n        (\\'sleep hours\\', \\'sleep_hours\\'),\\n        (\\'avg glucose\\', \\'glucose_mean\\'),\\n        (\\'resting hr\\', \\'resting_hr\\'),\\n        (\\'rhr\\', \\'resting_hr\\'),\\n        (\\'steps\\', \\'steps\\'),\\n        (\\'slept\\', \\'sleep_hours\\'),\\n        (\\'sleep\\', \\'sleep_hours\\'),\\n        (\\'glucose\\', \\'glucose_mean\\'),\\n        (\\'hrv\\', \\'hrv\\')\\n    ]\\n\\n    # 3. Search for Metric and Value\\n    for alias, canonical in mapping:\\n        pattern = r\\'\\\\b\\' + re.escape(alias) + r\\'\\\\b\\'\\n        match = re.search(pattern, line, re.IGNORECASE)\\n        if match:\\n            # Find the first number occurring after this metric name\\n            after_text = line[match.end():]\\n            num_match = re.search(r\\'(\\\\d[\\\\d,.]*)\\', after_text)\\n            if num_match:\\n                val_str = num_match.group(1).replace(\\',\\', \\'\\')\\n                try:\\n                    return {\\n                        \"date\": date_str,\\n                        \"metric\": canonical,\\n                        \"value\": float(val_str)\\n                    }\\n                except ValueError:\\n                    continue\\n    return None\\n'}", "result_digest": "cd9d121e656e379a", "ok": true, "elapsed_ms": 0.109, "ts_wall": "2026-07-24T23:39:32.922388", "ts_mono": 1091035.027938035}
{"seq": 18, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2,4}))\\', line)\\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = \"20\" + y if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n    else:\\n        return None\\n\\n    mapping = [\\n        (\\'step count\\', \\'steps\\'),\\n        (\\'resting heart rate\\', \\'resting_hr\\'),\\n        (\\'heart rate variability\\', \\'hrv\\'),\\n        (\\'sleep hours\\', \\'sleep_hours\\'),\\n        (\\'avg glucose\\', \\'glucose_mean\\'),\\n        (\\'resting hr\\', \\'resting_hr\\'),\\n        (\\'rhr\\', \\'resting_hr\\'),\\n        (\\'steps\\', \\'steps\\'),\\n        (\\'slept\\', \\'sleep_hours\\'),\\n        (\\'sleep\\', \\'sleep_hours\\'),\\n        (\\'glucose\\', \\'glucose_mean\\'),\\n        (\\'hrv\\', \\'hrv\\')\\n    ]\\n\\n    for alias, canonical in mapping:\\n        pattern = r\\'\\\\b\\' + re.escape(alias) + r\\'\\\\b\\'\\n        match = re.search(pattern, line, re.IGNORECASE)\\n        if match:\\n            after_text = line[match.end():]\\n            num_match = re.search(r\\'(\\\\d[\\\\d,.]*)\\', after_text)\\n            if num_match:\\n                val_str = num_match.group(1).replace(\\',\\', \\'\\')\\n                try:\\n                    return {\\n                        \"date\": date_str,\\n                        \"metric\": canonical,\\n                        \"value\": float(val_str)\\n                    }\\n                except ValueError:\\n                    continue\\n    return None\\n\\ntests = [\\n    \\'2026-03-07 - rhr: 79\\',\\n    \\'[2026-03-08] steps 18682\\',\\n    \\'[2026-03-09] heart rate variability 83 ms\\',\\n    \\'---\\',\\n    \\'avg glucose on 2026-03-11 = 117\\',\\n    \\'resting heart rate on 03/12/26 = 49 (approx)\\',\\n    \\'2026-03-13 - steps: 19050\\',\\n    \\'sync error @ device\\',\\n    \\'sleep hours on 03/15/26 = 5.8\\',\\n    \\'2026-03-16 - avg glucose: 124 (appr", "result_digest": "e1c036b3e6560d5a", "ok": true, "elapsed_ms": 0.115, "ts_wall": "2026-07-24T23:39:53.168565", "ts_mono": 1091055.274107677}
{"seq": 19, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2,4}))\\', line)\\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = \"20\" + y if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n    else:\\n        return None\\n\\n    mapping = [\\n        (\\'step count\\', \\'steps\\'),\\n        (\\'resting heart rate\\', \\'resting_hr\\'),\\n        (\\'heart rate variability\\', \\'hrv\\'),\\n        (\\'sleep hours\\', \\'sleep_hours\\'),\\n        (\\'avg glucose\\', \\'glucose_mean\\'),\\n        (\\'resting hr\\', \\'resting_hr\\'),\\n        (\\'rhr\\', \\'resting_hr\\'),\\n        (\\'steps\\', \\'steps\\'),\\n        (\\'slept\\', \\'sleep_hours\\'),\\n        (\\'sleep\\', \\'sleep_hours\\'),\\n        (\\'glucose\\', \\'glucose_mean\\'),\\n        (\\'hrv\\', \\'hrv\\')\\n    ]\\n\\n    for alias, canonical in mapping:\\n        pattern = r\\'\\\\b\\' + re.escape(alias) + r\\'\\\\b\\'\\n        match = re.search(pattern, line, re.IGNORECASE)\\n        if match:\\n            after_text = line[match.end():]\\n            num_match = re.search(r\\'(\\\\d[\\\\d,.]*)\\', after_text)\\n            if num_match:\\n                val_str = num_match.group(1).replace(\\',\\', \\'\\')\\n                try:\\n                    return {\\n                        \"date\": date_str,\\n                        \"metric\": canonical,\\n                        \"value\": float(val_str)\\n                    }\\n                except ValueError:\\n                    continue\\n    return None\\n\\nprint(parse_line(\\'[2026-03-09] heart rate variability 83 ms\\'))\\n'}", "result_digest": "88b50c08368f8152", "ok": true, "elapsed_ms": 0.115, "ts_wall": "2026-07-24T23:40:08.671163", "ts_mono": 1091070.77670596}
{"seq": 20, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date (ISO or US Slash)\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{2}/\\\\d{2}/(\\\\d{2,4}))\\', line)\\n    \\n    if iso_match:\\n        date_str = iso_match.group(1)\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = \"20\" + y if len(y) == 2 else y\\n        date_str = f\"{y}-{m}-{d}\"\\n    else:\\n        return None\\n\\n    # 2. Mapping of aliases to canonical names\\n    # We\\'ll use a list of tuples, ordered by alias length descending\\n    mapping = [\\n        (\\'step count\\', \\'steps\\'),\\n        (\\'resting heart rate\\', \\'resting_hr\\'),\\n        (\\'heart rate variability\\', \\'hrv\\'),\\n        (\\'sleep hours\\', \\'sleep_hours\\'),\\n        (\\'avg glucose\\', \\'glucose_mean\\'),\\n        (\\'resting hr\\', \\'resting_hr\\'),\\n        (\\'rhr\\', \\'resting_hr\\'),\\n        (\\'steps\\', \\'steps\\'),\\n        (\\'slept\\', \\'sleep_hours\\'),\\n        (\\'sleep\\', \\'sleep_hours\\'),\\n        (\\'glucose\\', \\'glucose_mean\\'),\\n        (\\'hrv\\', \\'hrv\\')\\n    ]\\n\\n    # 3. Find the metric and value\\n    for alias, canonical in mapping:\\n        # Use a regex that matches the alias as a whole word (case-insensitive)\\n        pattern = r\\'\\\\b\\' + re.escape(alias) + r\\'\\\\b\\'\\n        match = re.search(pattern, line, re.IGNORECASE)\\n        if match:\\n            # The value must follow the metric name eventually in the string\\n            after_text = line[match.end():]\\n            # Look for a number that may have commas or decimals\\n            num_match = re.search(r\\'(\\\\d[\\\\d,'}", "result_digest": "8f7e52c6d1ceeaed", "ok": true, "elapsed_ms": 0.107, "ts_wall": "2026-07-24T23:40:35.629750", "ts_mono": 1091097.735303534}
{"seq": 21, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\nfrom datetime import datetime\\n\\ndef parse_line(line: str) -> dict | None:\\n    \"\"\"Parses a line to extract date, metric name, and numerical value.\"\"\"\\n    date_obj = None\\n    rem = line\\n    \\n    # 1. Extract Date (ISO or US Slash)\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    if iso_match:\\n        try:\\n            date_obj = datetime.strptime(iso_match.group(1), \\'%Y-%m-%d\\')\\n            rem = line[:iso_match.start()] + line[iso_match.end():]\\n        except ValueError:\\n            pass\\n\\n    if date_obj is None:\\n        us_match = re.search(r\\'(\\\\d{1,2}/\\\\d{1,2}/(\\\\d{2,4}))\\', line)\\n        if us_match:\\n            try:\\n                ds = us_match.group(1)\\n                year_part = us_match.group(2)\\n                fmt = \\'%m/%d/%y\\' if len(year_part) <= 2 else \\'%m/%d/%Y\\'\\n                date_obj = datetime.strptime(ds, fmt)\\n                rem = line[:us_match.start()] + line[us_match.end():]\\n            except (ValueError, IndexError):\\n                pass\\n\\n    if not date_obj:\\n        return None\\n\\n    # 2. Extract Value (the last number in the remaining text)\\n    num_pattern = r\\'(-?\\\\d{1,3}(?:,\\\\d{3})*(?:\\\\.\\\\d+)?|\\\\d+(?:\\\\.\\\\d+)?)\\'\\n    matches = list(re.finditer(num_pattern, rem))\\n    if not matches:\\n        return None\\n    \\n    match = matches[-1]\\n    val_str = match.group(0).replace(\\',\\', \\'\\')\\n    try:\\n        value = float(val_str)\\n    except ValueError:\\n        return None\\n\\n    # 3. Extract Metric (text between date and value, cleaned)\\n    metric_part = rem[:match.start()].strip()\\n    # Remove common separators/artifacts like brackets, colons, dashes\\n    metric_part = re.sub(r\\'[\\\\[\\\\]\\\\:\\\\-]\\', \\'\\', metric_part).strip()\\n\\n    if not metric_part:\\n        return None\\n\\n    return {\\n        \\'date\\': date_obj,\\n        \\'metric\\': metric_part,\\n        \\'value\\': value\\n    }\\n'}", "result_digest": "a7385dbd0e7dd71d", "ok": true, "elapsed_ms": 0.109, "ts_wall": "2026-07-24T23:41:27.605316", "ts_mono": 1091149.710865288}
{"seq": 22, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "86e3f7bcd20f19e9", "ok": true, "elapsed_ms": 118.728, "ts_wall": "2026-07-24T23:41:30.436711", "ts_mono": 1091152.423640364}
{"seq": 23, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\nfrom datetime import datetime\\n\\ndef parse_line(line: str) -> dict | None:\\n    \"\"\"Parses a line to extract date (as YYYY-MM-DD), metric name, and numerical value.\"\"\"\\n    date_str = None\\n    rem = line\\n    \\n    # 1. Extract Date (ISO or US Slash)\\n    iso_match = re.search(r\\'(\\\\d{4}-\\\\d{2}-\\\\d{2})\\', line)\\n    if iso_match:\\n        try:\\n            date_str = iso_match.group(1)\\n            rem = line[:iso_match.start()] + line[iso_match.end():]\\n        except ValueError:\\n            pass\\n\\n    if date_str is None:\\n        us_match = re.search(r\\'(\\\\d{1,2}/\\\\d{1,2}/(\\\\d{2,4}))\\', line)\\n        if us_match:\\n            try:\\n                ds = us_match.group(1)\\n                year_part = us_match.group(2)\\n                fmt = \\'%m/%d/%y\\' if len(year_part) <= 2 else \\'%m/%d/%Y\\'\\n                dt = datetime.strptime(ds, fmt)\\n                date_str = dt.strftime(\\'%Y-%m-%d\\')\\n                rem = line[:us_match.start()] + line[us_match.end():]\\n            except (ValueError, IndexError):\\n                pass\\n\\n    if date_str is None:\\n        return None\\n\\n    # 2. Extract Value (the last number in the remaining text)\\n    num_pattern = r\\'(-?\\\\d{1,3}(?:,\\\\d{3})*(?:\\\\.\\\\d+)?|\\\\d+(?:\\\\.\\\\d+)?)\\'\\n    matches = list(re.finditer(num_pattern, rem))\\n    if not matches:\\n        return None\\n    \\n    match = matches[-1]\\n    val_str = match.group(0).replace(\\',\\', \\'\\')\\n    try:\\n        value = float(val_str)\\n    except ValueError:\\n        return None\\n\\n    # 3. Extract Metric (text between date and value, cleaned)\\n    metric_part = rem[:match.start()].strip()\\n    # Remove common separators/artifacts like brackets, colons, dashes, equals\\n    metric_part = re.sub(r\\'[\\\\[\\\\]\\\\:\\\\-\\\\=]\\', \\'\\', metric_part).strip()\\n\\n    if not metric_part:\\n        return None\\n\\n    return {\\n        \\'date\\': date_str,\\n        \\'metric\\': metric_part,\\n        \\'value\\': value\\n    }\\n'}", "result_digest": "28c50408d3c14df2", "ok": true, "elapsed_ms": 0.111, "ts_wall": "2026-07-24T23:41:46.255901", "ts_mono": 1091168.361449466}
{"seq": 24, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    date_str = None\\n    # ISO format YYYY-MM-DD\\n    iso_match = re.search(r\\'(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\\', line)\\n    if iso_match:\\n        date_str = f\"{iso_match.group(1)}-{iso_match.group(2).zfill(2)}-{iso_match.group(3).zfill(2)}\"\\n    else:\\n        # US format MM/DD/YY or MM/DD/YYYY\\n        us_match = re.search(r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\', line)\\n        if us_match:\\n            m = int('}", "result_digest": "7275a76b7dede950", "ok": true, "elapsed_ms": 0.107, "ts_wall": "2026-07-24T23:43:11.843008", "ts_mono": 1091253.948563255}
{"seq": 25, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date (ISO or US slash)\\n    date_str = None\\n    iso_match = re.search(r\\'(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\\', line)\\n    if iso_match:\\n        date_str = f\"{iso_match.group(1)}-{iso_match.group(2).zfill(2)}-{iso_match.group(3).zfill(2)}\"\\n    else:\\n        us_match = re.search(r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\', line)\\n        if us_match:\\n            m, d, y = us_match.groups()\\n            y = int(y)\\n            if y < 100:\\n                y += 2000 # Assumption for this task context\\n            date_str = f\"{y}-{int(m):02d}-{int(d):02d}\"\\n\\n    if not date_str:\\n        return None\\n\\n    # 2. Find Metric and Value\\n    # Mapping of aliases to canonical names\\n    metric_map = {\\n        \"steps\": \"steps\",\\n        \"distance\": \"distance\",\\n        \"calories\": \"calories\",\\n        \"heart rate\": \"heart_rate\",\\n        \"hr\": \"heart_rate\"\\n    }\\n\\n    # Look for metric patterns: name followed by value (number)\\n    # Examples: \\'steps 123\\', \\'distance: 45.6 km\\', \\'calories=500\\'\\n    # We also need to handle commas in numbers like \\'1,000\\'\\n    \\n    # Improved regex for finding metric and number\\n    # Matches \"metric_name [separator] [number]\"\\n    pattern = r\\'(steps|distance|calories|heart rate|hr)[:\\\\s=]+([\\\\d,.]+)\\'\\n    match = re.search(pattern, line, re.IGNORECASE)\\n    \\n    if not match:\\n        return None\\n\\n    metric_raw = match.group(1).lower()\\n    value_str = match.group(2).replace(\\',\\', \\'\\')\\n\\n    # Map to canonical name\\n    canonical_metric = metric_map.get(metric_raw, metric_raw)\\n    if canonical_metric == \"heart rate\":\\n        canonical_metric = \"heart_rate\"\\n\\n    try:\\n        value = float(value_str)\\n    except ValueError:\\n        return None\\n\\n    # Remove units if present (e.g., \\'km\\' in \\'45.6 km\\') - though regex above doesn\\'t catch them, let\\'s be safe\\n    # Actually the current regex is sufficient for the examples provided in previous", "result_digest": "72e4a22ad3ca4d24", "ok": true, "elapsed_ms": 0.106, "ts_wall": "2026-07-24T23:43:21.637485", "ts_mono": 1091263.743035755}
{"seq": 26, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "c99e1ef11aeef93c", "ok": true, "elapsed_ms": 117.431, "ts_wall": "2026-07-24T23:43:23.985420", "ts_mono": 1091265.973648777}
{"seq": 27, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date (ISO or US slash)\\n    date_str = None\\n    iso_match = re.search(r\\'(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\', line)\\n\\n    if iso_match:\\n        date_str = f\"{iso_match.group(1)}-{iso_match.group(2).zfill(2)}-{iso_match.group(3).zfill(2)}\"\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        y = int(y)\\n        if y < 100:\\n            y += 2000\\n        date_str = f\"{y}-{int(m):02d}-{int(d):02d}\"\\n\\n    if not date_str:\\n        return None\\n\\n    # 2. Extract Value (the first number found that isn\\'t part of the date)\\n    # We find all numbers and pick the one that isn\\'t inside the date string or at the start/end in a way that matches date\\n    # Easier: Find all numbers, but we know the date is already parsed. \\n    # Let\\'s remove the date from the line first to avoid confusion.\\n    line_no_date = re.sub(r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}|\\\\d{1,2}/\\\\d{1,2}/\\\\d{2,4}\\', \\'\\', line)\\n    \\n    # Find the number (allowing commas and decimals)\\n    value_match = re.search(r\\'(\\\\d[\\\\d,.]*)\\', line_no_date)\\n    if not value_match:\\n        return None\\n    \\n    value_str = value_match.group(1).replace(\\',\\', \\'\\')\\n    try:\\n        value = float(value_str)\\n    except ValueError:\\n        return None\\n\\n    # 3. Extract Metric Name\\n    # The metric is the text that remains after removing date, value, and units/noise.\\n    # Remove date part from original line to get \"context\"\\n    context = re.sub(r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}|\\\\d{1,2}/\\\\d{1,2}/\\\\d{2,4}\\', \\'\\', line)\\n    # Remove the value and any following text that looks like units (e.g., \"bpm\", \"ms\", \"(approx)\")\\n    # We find where the number was in \\'context\\'\\n    remaining = re.sub(r\\'\\\\d[\\\\d,.]*\\\\s*[a-zA-Z]*(\\\\s*\\\\(.*\\\\))?\\', \\'\\', context).strip()\\n    # Also remove separators and filler words\\n    for word in [\\'on\\', \\'at\\', \\'is\\', \\'was\\', \\'=\\', \\':\\',", "result_digest": "76d5402146f0c2fb", "ok": true, "elapsed_ms": 0.111, "ts_wall": "2026-07-24T23:44:09.847552", "ts_mono": 1091311.953093828}
{"seq": 28, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date (ISO or US Slash)\\n    iso = re.search(r\\'\\\\d{4}-\\\\d{2}-\\\\d{2}\\', line)\\n    us = re.search(r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2,4})\\', line)\\n    if iso:\\n        date_str = iso.group()\\n    elif us:\\n        m, d, y = us.groups()\\n        y = f\"20{y}\" if len(y) == 2 else y\\n        date_str = f\"{y}-{m.zfill(2)}-{d.zfill(2)}\"\\n    else:\\n        return None\\n\\n    # 2. Metric Mapping\\n    mapping = {\\n        \\'steps\\': \\'steps\\',\\n        \\'heart rate\\': \\'heart_rate\\', \\'hr\\': \\'heart_rate\\',\\n        \\'resting hr\\': \\'resting_hr\\',\\n        \\'heart rate variability\\': \\'heart_rate_variability\\', \\'hrv\\': \\'heart_rate_variability\\',\\n        \\'glucose\\': \\'glucose\\'\\n    }\\n\\n    # 3. Extract Metric and Value\\n    content = line.replace(iso.group() if iso else us.group(), \"\").strip()\\n    for pattern, canonical in mapping.items():\\n        match = re.search(rf\\'\\\\b{pattern}\\\\b\\\\s*:?\\\\s*([\\\\d,.]+)\\', content, re.I)\\n        if match:\\n            try:\\n                val_str = match.group(1).replace(\\',\\', \\'\\')\\n                return {\"date\": date_str, \"metric\": canonical, \"value\": float(val_str)}\\n            except (ValueError, IndexError):\\n                continue\\n    return None\\n'}", "result_digest": "4abf5c5ea0de420b", "ok": true, "elapsed_ms": 0.105, "ts_wall": "2026-07-24T23:46:31.105875", "ts_mono": 1091453.211429303}
{"seq": 29, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "5167c4b29dbbb017", "ok": true, "elapsed_ms": 116.838, "ts_wall": "2026-07-24T23:46:36.091230", "ts_mono": 1091458.080054027}
{"seq": 30, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\nfrom datetime import datetime\\n\\ndef'}", "result_digest": "4b04c49d3b3525f2", "ok": true, "elapsed_ms": 0.11, "ts_wall": "2026-07-24T23:47:58.108132", "ts_mono": 1091540.213687705}
{"seq": 31, "run_id": "janus-s11-20260725", "room": "room2", "tool": "read_spec", "args": "{}", "result_digest": "d813a70ddb6d25eb", "ok": true, "elapsed_ms": 0.068, "ts_wall": "2026-07-24T23:48:05.763664", "ts_mono": 1091547.869257818}
{"seq": 32, "run_id": "janus-s11-20260725", "room": "room2", "tool": "write_tool", "args": "{'source': 'import re\\n\\ndef parse_line(line: str) -> dict | None:\\n    # 1. Extract Date\\n    iso_match = re.search(r\\'(\\\\d{4})-(\\\\d{2})-(\\\\d{2})\\', line)\\n    us_match = re.search(r\\'(\\\\d{1,2})/(\\\\d{1,2})/(\\\\d{2})\\', line)\\n    \\n    if iso_match:\\n        date_str = iso_match.group(0)\\n        rem = line.replace(date_str, \"\")\\n    elif us_match:\\n        m, d, y = us_match.groups()\\n        date_str = f\"{y} {m}-{d}\" # placeholder for sorting but need YYYY-MM-DD\\n        # Correct US date to YYYY-MM-DD\\n        year = int(y) + 2000\\n        month = m.zfill(2)\\n        day = d.zfill(2)\\n        date_str = f\"{year}-{month}-{day}\"\\n        rem = line.replace(us_match.group(0), \"\")\\n    else:\\n        return None\\n\\n    # 2. Define Metrics Mapping (case-insensitive matching)\\n    metric_patterns = [\\n        (r\\'steps|step count\\', \\'steps\\'),\\n        (r\\'resting hr|rhr|resting heart rate\\', \\'resting_hr\\'),\\n        (r\\'hrv|heart rate variability\\', \\'hrv\\'),\\n        (r\\'sleep|slept|sleep hours\\', \\'sleep_hours\\'),\\n        (r\\'glucose|avg glucose\\', \\'glucose_mean\\')\\n    ]\\n\\n    found_metric = None\\n    for pattern, canonical in metric_patterns:\\n        if re.search(pattern, rem, re.IGNORECASE):\\n            found_metric = canonical\\n            break\\n    \\n    if not found_metric:\\n        return None\\n\\n    # 3. Extract Value\\n    # The number is likely the part that isn\\'t the date or metric name.\\n    # It might follow a colon, equals sign, or just space.\\n    # We need to find the last number in the string after stripping metadata.\\n    \\n    # Remove everything up to the metric/value area? \\n    # Actually, let\\'s search for all numbers in \\'rem\\'.\\n    # A number can have commas and dots: e.g., \"18,682\" or \"5.8\".\\n    \\n    # Let\\'s try a more surgical approach:\\n    # Find the part of the string that contains numbers after removing date/metric? \\n    # No, let\\'s just find all potential number strings in \\'rem\\'.\\n    \\n    # A better way to handle \"va", "result_digest": "9630d3dacc120301", "ok": true, "elapsed_ms": 0.112, "ts_wall": "2026-07-24T23:48:25.864283", "ts_mono": 1091567.96982516}
{"seq": 33, "run_id": "janus-s11-20260725", "room": "room2", "tool": "run_tests", "args": "{}", "result_digest": "214682a5797b87be", "ok": true, "elapsed_ms": 106.87, "ts_wall": "2026-07-24T23:48:30.018395", "ts_mono": 1091572.01718773}
{"seq": 34, "run_id": "janus-s11-20260725", "room": "room2", "tool": "submit_tool", "args": "{}", "result_digest": "6cb57443caf97667", "ok": true, "elapsed_ms": 118.571, "ts_wall": "2026-07-24T23:48:30.780025", "ts_mono": 1091572.767117086}
{"seq": 1, "run_id": "janus-s11-20260725", "room": "room3", "tool": "briefing", "args": "{}", "result_digest": "08f4be1ba2fbfec0", "ok": true, "elapsed_ms": 0.011, "ts_wall": "2026-07-24T23:48:33.810377", "ts_mono": 1091575.916017769}
{"seq": 2, "run_id": "janus-s11-20260725", "room": "room3", "tool": "read_log", "args": "{}", "result_digest": "15c19d78e5aaa049", "ok": true, "elapsed_ms": 0.065, "ts_wall": "2026-07-24T23:48:34.266552", "ts_mono": 1091576.372148759}
{"seq": 3, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '[04/17/26] resting hr 71 bpm'}", "result_digest": "6baa61305c797735", "ok": true, "elapsed_ms": 10.373, "ts_wall": "2026-07-24T23:48:40.014662", "ts_mono": 1091582.109950578}
{"seq": 4, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '2026-04-18 - steps: 19626 (approx)'}", "result_digest": "5e73b01d2786698b", "ok": true, "elapsed_ms": 9.777, "ts_wall": "2026-07-24T23:48:40.897656", "ts_mono": 1091582.993539847}
{"seq": 5, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'hrv on 2026-04-19 = 62 (approx)'}", "result_digest": "9738db4439cd8580", "ok": true, "elapsed_ms": 9.725, "ts_wall": "2026-07-24T23:48:41.836179", "ts_mono": 1091583.932115426}
{"seq": 6, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '--- avg glucose on 2026-04-21 = 90 ---'}", "result_digest": "c4284d8e50602099", "ok": true, "elapsed_ms": 9.553, "ts_wall": "2026-07-24T23:48:42.576241", "ts_mono": 1091584.672349186}
{"seq": 7, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'avg glucose on 2026-04-21 = 90'}", "result_digest": "c4284d8e50602099", "ok": true, "elapsed_ms": 9.462, "ts_wall": "2026-07-24T23:48:56.623384", "ts_mono": 1091598.719583894}
{"seq": 8, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'resting hr on 04/22/26 = 77'}", "result_digest": "012dd0411917d377", "ok": true, "elapsed_ms": 9.204, "ts_wall": "2026-07-24T23:48:57.587095", "ts_mono": 1091599.683551985}
{"seq": 9, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'steps on 04/23/26 = 18214'}", "result_digest": "684c1e73b53f7b9b", "ok": true, "elapsed_ms": 9.827, "ts_wall": "2026-07-24T23:48:58.303976", "ts_mono": 1091600.399809065}
{"seq": 10, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'sync error @ device'}", "result_digest": "74234e98afe7498f", "ok": true, "elapsed_ms": 9.509, "ts_wall": "2026-07-24T23:48:58.854213", "ts_mono": 1091600.950364505}
{"seq": 11, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': 'slept on 2026-04-25 = 3.2'}", "result_digest": "c862e9de58a538b0", "ok": true, "elapsed_ms": 9.76, "ts_wall": "2026-07-24T23:48:59.711115", "ts_mono": 1091601.807015592}
{"seq": 12, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '2026-04-26 - avg glucose: 93'}", "result_digest": "839c6f3be3b95911", "ok": true, "elapsed_ms": 9.681, "ts_wall": "2026-07-24T23:49:00.435855", "ts_mono": 1091602.531835061}
{"seq": 13, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '[04/27/26] resting hr 71 --- note: felt great today --- [2026-04-29] heart rate variability 59 sleep hours on 2026-04-30 = 6.5 2026-05-01 - avg glucose: 102 sync error @ device [05/03/26] step count 18897 [2026-05-04] hrv 67'}", "result_digest": "5f816439dc529126", "ok": true, "elapsed_ms": 9.241, "ts_wall": "2026-07-24T23:49:02.320819", "ts_mono": 1091604.417238802}
{"seq": 14, "run_id": "janus-s11-20260725", "room": "room3", "tool": "use_tool", "args": "{'line': '[04/27/26] resting hr 71'}", "result_digest": "5a71ced890159f6f", "ok": true, "elapsed_ms": 9.252, "ts_wall": "2026-07-24T23:49:15.466880", "ts_mono": 1091617.563291318}
{"seq": 15, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-17', 'metric': 'resting_hr', 'value': 71.0}", "result_digest": "a870a3078242b94c", "ok": true, "elapsed_ms": 0.469, "ts_wall": "2026-07-24T23:49:33.168160", "ts_mono": 1091635.273356992}
{"seq": 16, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-18', 'metric': 'steps', 'value': 19626.0}", "result_digest": "6d8dfdbd44f12754", "ok": true, "elapsed_ms": 0.081, "ts_wall": "2026-07-24T23:50:19.882940", "ts_mono": 1091681.988523371}
{"seq": 17, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-19', 'metric': 'hrv', 'value': 62.0}", "result_digest": "c2088df68a7c59cf", "ok": true, "elapsed_ms": 0.082, "ts_wall": "2026-07-24T23:50:22.157276", "ts_mono": 1091684.262857779}
{"seq": 18, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-21', 'metric': 'glucose_mean', 'value': 90.0}", "result_digest": "2b3eb342c431d9f9", "ok": true, "elapsed_ms": 0.087, "ts_wall": "2026-07-24T23:50:22.926067", "ts_mono": 1091685.031643731}
{"seq": 19, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-22', 'metric': 'resting_hr', 'value': 77.0}", "result_digest": "f8abb956f1866bc9", "ok": true, "elapsed_ms": 0.081, "ts_wall": "2026-07-24T23:50:23.691526", "ts_mono": 1091685.797109245}
{"seq": 20, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-23', 'metric': 'steps', 'value': 18214.0}", "result_digest": "afd9b8fdf87b1896", "ok": true, "elapsed_ms": 0.08, "ts_wall": "2026-07-24T23:50:24.472507", "ts_mono": 1091686.578090639}
{"seq": 21, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-25', 'metric': 'sleep_hours', 'value': 3.2}", "result_digest": "e13fc5bb061f8e58", "ok": true, "elapsed_ms": 0.091, "ts_wall": "2026-07-24T23:50:25.363814", "ts_mono": 1091687.469386779}
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{"seq": 23, "run_id": "janus-s11-20260725", "room": "room3", "tool": "enter_record", "args": "{'date': '2026-04-27', 'metric': 'resting_hr', 'value': 71.0}", "result_digest": "7a6d0b8b7362076d", "ok": true, "elapsed_ms": 0.093, "ts_wall": "2026-07-24T23:50:32.503793", "ts_mono": 1091694.60936303}
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