Explore the Butler project at /root/projects/butler to understand the existing health analytics infrastructure and Slack messaging patterns. I need to implement a weekly health report that runs on a schedule and sends results to Slack.

Please read and summarize the following files in detail:

1. /root/projects/butler/health/analytics/engine.py - What analysis functions exist? What do they return?
2. /root/projects/butler/health/services/query.py - What query methods exist for fetching health data?
3. /root/projects/butler/health/models/ - List all model files and their key fields
4. /root/projects/butler/slack_bot/main.py - How does the Health Bot send messages to Slack? What Slack client setup is used?
5. /root/projects/butler/health/services/manual_log_storage.py - What methods exist for querying manual logs?
6. /root/projects/butler/scripts/ - List all scripts and identify any that are similar to a report/notification script
7. /root/projects/butler/health/cli/sync.py - How is the CLI structured? Can I reuse this pattern?

For each file, provide the key function signatures, return types, and any important patterns I should follow.

This is research only - no changes needed.


I'll systematically explore the Butler project to understand the health analytics infrastructure and Slack messaging patterns. Let me start by examining the project structure and then dive into the specific files you've requested.


Bash

find /root/projects/butler -type f -name "*.py" | head -20


/root/projects/butler/experiments/compare_dispatchers.py
/root/projects/butler/experiments/dispatcher_clean.py
/root/projects/butler/experiments/test_llm_intelligence.py
/root/projects/butler/health/analytics/__init__.py
/root/projects/butler/health/analytics/engine.py
/root/projects/butler/health/cli/__init__.py
/root/projects/butler/health/cli/manual.py
/root/projects/butler/health/cli/obsidian.py
/root/projects/butler/health/cli/query.py
/root/projects/butler/health/cli/sync.py
/root/projects/butler/health/db/__init__.py
/root/projects/butler/health/db/repository.py
/root/projects/butler/health/db/schema.py
/root/projects/butler/health/models/__init__.py
/root/projects/butler/health/models/activity.py
/root/projects/butler/health/models/body_metrics.py
/root/projects/butler/health/models/daily_metrics.py
/root/projects/butler/health/models/manual_log.py
/root/projects/butler/health/models/sync_metadata.py
/root/projects/butler/health/services/__init__.py


Let me continue exploring the project structure and read the key files:


ls -la /root/projects/butler/health/models/ | grep -E "\.py$"


     1→import os
     2→import sys
     3→import click
     4→from slack_bolt import App
     5→from slack_bolt.adapter.socket_mode import SocketModeHandler
     6→from dotenv import load_dotenv
     7→
     8→# Add project root to path
     9→sys.path.append(os.getcwd())
    10→
    11→from health.utils.logging_config import setup_logger
    12→from health.utils.env_loader import load_env_with_extras
    13→from slack_bot.dispatcher import MessageDispatcher
    14→from slack_bot.context.storage import ContextStorage
    15→
    16→logger = setup_logger(__name__)
    17→
    18→# Load environment variables
    19→load_env_with_extras()
    20→
    21→
    22→@click.command()
    23→@click.option("--model", default=None, help="Gemini model to use (overrides .env)")
    24→def main(model: str):
    25→    """Butler Slack Bot - Gemini Edition."""
    26→
    27→    # Override model in environment only if specified via CLI
    28→    if model:
    29→        os.environ["GEMINI_MODEL"] = model
    30→        logger.info(f"Using model from CLI: {model}")
    31→
    32→    # Get the actual model being used (from CLI or .env)
    33→    actual_model = os.environ.get("GEMINI_MODEL", "gemini-2.0-flash-exp")
    34→    base_url = os.environ.get("GEMINI_BASE_URL", "Not set (will use direct API)")
    35→    config_path = os.environ.get("GEMINI_CONFIG_PATH", ".env")
    36→
    37→    logger.info(f"Starting Butler Bot with configuration:")
    38→    logger.info(f"  Config file: {config_path}")
    39→    logger.info(f"  Model: {actual_model}")
    40→    logger.info(f"  Base URL: {base_url}")
    41→    logger.info(f"  Using proxy: {'Yes' if base_url and 'http' in base_url else 'No (Direct Google API)'}")
    42→
    43→    # Initialize Slack App
    44→    bot_token = os.environ.get("SLACK_BOT_TOKEN")
    45→    app_token = os.environ.get("SLACK_APP_TOKEN")
    46→
    47→    if not bot_token or not app_token:
    48→        logger.error("Missing SLACK_BOT_TOKEN or SLACK_APP_TOKEN in .env")
    49→        sys.exit(1)
    50→
    51→    app = App(token=bot_token)
    52→
    53→    # Tool mode: 'none' (no tools), 'light' (5), 'standard' (10), 'full' (15), 'all' (21)
    54→    # Use 'none' if your proxy doesn't support function calling
    55→    tool_mode = os.environ.get("TOOL_MODE", "light")
    56→    dispatcher = MessageDispatcher(tool_mode=tool_mode)
    57→    logger.info(f"Dispatcher initialized in '{tool_mode}' mode")
    58→
    59→    # Message deduplication: Track processed message IDs
    60→    processed_messages = set()
    61→    MAX_TRACKED_MESSAGES = 1000
    62→
    63→    # Command Handler: Clear Context
    64→    @app.message(r"^/(clear|reset)$")
    65→    def handle_clear_context(message, say):
    66→        """Clear conversation context for the current channel."""
    67→        try:
    68→            channel = message["channel"]
    69→            storage = ContextStorage(channel)
    70→
    71→            # Get context size before clearing
    72→            old_context = storage.get_context()
    73→            old_count = len(old_context)
    74→
    75→            storage.clear()
    76→
    77→            logger.info(f"✅ Context cleared for channel {channel} (removed {old_count} messages)")
    78→            say(f"✅ 对话历史已清空,我们重新开始!\n\n_Context cleared ({old_count} messages removed). Starting fresh conversation._")
    79→        except Exception as e:
    80→            logger.error(f"Error clearing context: {e}", exc_info=True)
    81→            say(f"❌ 清空对话历史失败: {e}")
    82→
    83→    # Command Handler: Show Context Stats
    84→    @app.message(r"^/stats$")
    85→    def handle_context_stats(message, say):
    86→        """Show conversation context statistics."""
    87→        try:
    88→            channel = message["channel"]
    89→            storage = ContextStorage(channel)
    90→            context = storage.get_context()
    91→
    92→            # Calculate token estimate (rough: 1 token ≈ 3 chars for Chinese/English mix)
    93→            total_chars = sum(len(msg.get("content", "")) for msg in context)
    94→            estimated_tokens = total_chars // 3
    95→
    96→            stats_msg = (
    97→                f"📊 **对话上下文统计 (Context Stats)**\n\n"
    98→                f"• 消息数量 (Messages): {len(context)}\n"
    99→                f"• 总字符数 (Total chars): {total_chars:,}\n"
   100→                f"• 预估 Token (Est. tokens): ~{estimated_tokens:,}\n\n"
   101→                f"_使用 `/clear` 可以清空对话历史_"
   102→            )
   103→
   104→            say(stats_msg)
   105→            logger.info(f"Context stats for {channel}: {len(context)} messages, ~{estimated_tokens} tokens")
   106→        except Exception as e:
   107→            logger.error(f"Error getting context stats: {e}", exc_info=True)
   108→            say(f"❌ 获取统计信息失败: {e}")
   109→
   110→    # Message Handler
   111→    @app.event("message")
   112→    def handle_message(message, say):
   113→
   114→        try:
   115→            # Skip command messages (they're handled by command handlers)
   116→            text = message.get("text", "")
   117→            if text.startswith("/clear") or text.startswith("/reset") or text.startswith("/stats"):
   118→                logger.debug("Skipping command message in general handler")
   119→                return
   120→
   121→            # Deduplicate using client_msg_id or event_ts
   122→            msg_id = message.get("client_msg_id") or message.get("ts")
   123→
   124→            if msg_id in processed_messages:
   125→                logger.debug(f"Skipping duplicate message: {msg_id}")
   126→                return
   127→
   128→            # Add to processed set
   129→            processed_messages.add(msg_id)
   130→
   131→            # Limit memory usage by keeping only recent message IDs
   132→            if len(processed_messages) > MAX_TRACKED_MESSAGES:
   133→                # Remove oldest half
   134→                to_remove = list(processed_messages)[:MAX_TRACKED_MESSAGES // 2]
   135→                for old_id in to_remove:
   136→                    processed_messages.discard(old_id)
   137→
   138→            text = message.get("text", "")
   139→            channel = message["channel"]
   140→            user = message["user"]
   141→            files = message.get("files", [])
   142→
   143→            # Generate Request ID
   144→            import uuid
   145→            import time
   146→            request_id = str(uuid.uuid4())[:8]
   147→            start_time = time.time()
   148→
   149→            logger.info(f"[{request_id}] 📥 Received message from {user} in {channel}: {text} (Files: {len(files)})")
   150→
   151→            # Send temporary "Processing..." message
   152→            ack_response = say(f"⏳ Butler is thinking...")
   153→            ack_ts = ack_response["ts"]
   154→
   155→            # Dispatch to Gemini with the timestamp to update
   156→            dispatcher.dispatch(text, channel, user, response_ts=ack_ts, request_id=request_id, files=files)
   157→
   158→            elapsed = time.time() - start_time
   159→            logger.info(f"[{request_id}] ✅ Request completed in {elapsed:.2f}s")
   160→
   161→        except Exception as e:
   162→            logger.error(f"Error handling message: {e}", exc_info=True)
   163→            try:
   164→                say(f"🐛 Butler encountered an error: {e}")
   165→            except Exception:
   166→                pass
   167→
   168→    # Start Socket Mode
   169→    try:
   170→        handler = SocketModeHandler(app, app_token)
   171→        logger.info("⚡ Butler (Gemini) is connected to Slack!")
   172→        handler.start()
   173→    except Exception as e:
   174→        logger.error(f"Failed to start SocketModeHandler: {e}")
   175→        sys.exit(1)
   176→
   177→
   178→if __name__ == "__main__":
   179→    main()
   180→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→import pandas as pd
     2→import numpy as np
     3→from datetime import date, timedelta
     4→from typing import Dict, Any, List, Optional
     5→from health.services.query import HealthDataQuery
     6→from health.services.manual_log_storage import ManualLogStorage
     7→from health.utils.logging_config import setup_logger
     8→
     9→logger = setup_logger(__name__)
    10→
    11→class HealthAnalyst:
    12→    """
    13→    Advanced analytics engine for health data using Pandas.
    14→    Calculates correlations, trends, and lifestyle impacts.
    15→    """
    16→
    17→    def __init__(self):
    18→        self.query = HealthDataQuery()
    19→        self.manual_storage = ManualLogStorage()
    20→
    21→    def get_dataframe(self, days: int = 30) -> pd.DataFrame:
    22→        """
    23→        Fetch all relevant data for the last N days and merge into a single DataFrame.
    24→        """
    25→        end_date = date.today()
    26→        start_date = end_date - timedelta(days=days)
    27→
    28→        # 1. Fetch Daily Metrics (Steps, Sleep, HR, Stess, Body Battery)
    29→        # We need to fetch each metric range and merge by date
    30→        metrics_to_fetch = ["sleep", "heart_rate", "stress", "body_battery", "steps", "hrv", "rhr"]
    31→
    32→        data_map = {} # date -> dict of metrics
    33→
    34→        # Initialize dates
    35→        curr = start_date
    36→        while curr <= end_date:
    37→            data_map[curr.isoformat()] = {"date": curr.isoformat()}
    38→            curr += timedelta(days=1)
    39→
    40→        # Batch fetch metrics (this might be slow if we do it one by one,
    41→        # but HealthDataQuery is built for this. Ideally we optimize query later)
    42→        for metric in metrics_to_fetch:
    43→            points = self.query.get_metric_range(metric, start_date, end_date)
    44→            for p in points:
    45→                # Key extraction logic similar to Reader
    46→                # Simplified extraction for DataFrame
    47→                val = np.nan
    48→                if hasattr(p, 'value'): val = p.value
    49→                elif isinstance(p, dict):
    50→                    # Try common keys
    51→                    if 'average_heart_rate' in p: val = p['average_heart_rate']
    52→                    elif 'overall_sleep_score' in p: val = p['overall_sleep_score']
    53→                    elif 'resting_heart_rate' in p: val = p['resting_heart_rate']
    54→                    elif 'average_stress_level' in p: val = p['average_stress_level']
    55→                    elif 'charged' in p: val = p.get('charged', 0) # Body battery charged?
    56→                    # Actually for body battery we usually want max or charged
    57→
    58→                # Store in map
    59→                d_str = p.get('calendar_date') or p.get('date')
    60→                if d_str and d_str in data_map:
    61→                    data_map[d_str][metric] = val
    62→
    63→        # 2. Fetch Manual Logs (Alcohol, Fasting, Supplements)
    64→        logs = self.manual_storage.get_logs_in_range(start_date, end_date)
    65→        for log in logs:
    66→            d_str = log.log_date
    67→            if d_str in data_map:
    68→                # Alcohol (Boolean & Amount)
    69→                data_map[d_str]["alcohol_units"] = sum([1 for _ in log.alcohol_entries])
    70→                data_map[d_str]["has_alcohol"] = 1 if log.alcohol_entries else 0
    71→
    72→                # Fasting (Categorical)
    73→                data_map[d_str]["fasting_mode"] = log.fasting_mode or "Normal"
    74→
    75→                # Supplements (One-hotish checks)
    76→                data_map[d_str]["has_magnesium"] = 1 if any("magnesium" in s.supplement_name.lower() for s in log.supplement_entries) else 0
    77→
    78→        # Convert to DataFrame
    79→        df = pd.DataFrame(list(data_map.values()))
    80→        df['date'] = pd.to_datetime(df['date'])
    81→        df = df.sort_values('date').set_index('date')
    82→        return df
    83→
    84→    def analyze_recovery_correlations(self, days: int = 90) -> Dict[str, Any]:
    85→        """
    86→        Analyze impact of Alcohol and Activity on Next-Day Recovery (Sleep, HRV, RHR).
    87→        """
    88→        df = self.get_dataframe(days)
    89→
    90→        if df.empty:
    91→            return {"error": "No data available"}
    92→
    93→        # Shift recovery metrics to represent "Next Day"
    94→        # We want to see if Alcohol on Day T affects Sleep on Day T (technically sleep starts on Day T night)
    95→        # Actually usually Alcohol on Day T affects Sleep Score of the night of Day T (which serves Day T+1 morning)
    96→        # Garmin assigns Sleep Date to the morning it ends.
    97→        # So Alcohol on Jan 1 affects Sleep of Jan 2.
    98→
    99→        # Let's align:
   100→        # Feature: Alcohol(T)
   101→        # Target: Sleep(T+1), RHR(T+1)
   102→
   103→        df['next_sleep'] = df['sleep'].shift(-1)
   104→        df['next_rhr'] = df['rhr'].shift(-1)
   105→        df['next_hrv'] = df['hrv'].shift(-1)
   106→
   107→        results = {}
   108→
   109→        # 1. Alcohol Impact
   110→        alcohol_days = df[df['has_alcohol'] == 1]
   111→        sober_days = df[df['has_alcohol'] == 0]
   112→
   113→        if len(alcohol_days) > 0 and len(sober_days) > 0:
   114→            results['alcohol_impact'] = {
   115→                "sample_size_alcohol": len(alcohol_days),
   116→                "sample_size_sober": len(sober_days),
   117→                "avg_sleep_alcohol": float(alcohol_days['next_sleep'].mean()),
   118→                "avg_sleep_sober": float(sober_days['next_sleep'].mean()),
   119→                "avg_rhr_alcohol": float(alcohol_days['next_rhr'].mean()),
   120→                "avg_rhr_sober": float(sober_days['next_rhr'].mean()),
   121→                "sleep_diff": float(alcohol_days['next_sleep'].mean() - sober_days['next_sleep'].mean())
   122→            }
   123→
   124→        return results
   125→
   126→    def analyze_fitness_trends(self, days: int = 90) -> Dict[str, Any]:
   127→        """
   128→        Analyze RHR vs Activity trends.
   129→        """
   130→        df = self.get_dataframe(days)
   131→        if df.empty: return {"error": "No data"}
   132→
   133→        # Calculate rolling averages (7-day)
   134→        df['rhr_roll'] = df['rhr'].rolling(7).mean()
   135→        df['steps_roll'] = df['steps'].rolling(7).mean()
   136→
   137→        # Simple Linear Trend
   138→        # Drop NaNs
   139→        valid = df.dropna(subset=['rhr'])
   140→        if len(valid) > 10:
   141→            # Simple slope (not robust but indicative)
   142→            # rhr change over period
   143→            start_rhr = valid.iloc[0]['rhr_roll'] if not np.isnan(valid.iloc[0]['rhr_roll']) else valid.iloc[0]['rhr']
   144→            end_rhr = valid.iloc[-1]['rhr_roll'] if not np.isnan(valid.iloc[-1]['rhr_roll']) else valid.iloc[-1]['rhr']
   145→
   146→            return {
   147→                "period_days": days,
   148→                "start_rhr_7d_avg": float(start_rhr),
   149→                "end_rhr_7d_avg": float(end_rhr),
   150→                "trend": "improving" if end_rhr < start_rhr else "declining"
   151→            }
   152→
   153→        return {"msg": "Not enough data for trend analysis"}
   154→
   155→    def analyze_lifestyle_impact(self, days: int = 30) -> Dict[str, Any]:
   156→        """
   157→        Analyze Fasting and Supplements.
   158→        """
   159→        df = self.get_dataframe(days)
   160→        if df.empty: return {"error": "No data"}
   161→
   162→        results = {}
   163→
   164→        # Fasting Impact
   165→        # Compare "Normal" vs other modes
   166→        fasting_stats = df.groupby('fasting_mode')['sleep'].mean().to_dict()
   167→        results['fasting_sleep_scores'] = fasting_stats
   168→
   169→        return results
   170→
   171→    def compare_groups(self, condition_col: str, target_col: str, days: int = 90) -> Dict[str, Any]:
   172→        """
   173→        Compare average of 'target_col' when 'condition_col' is non-zero vs zero.
   174→        Example: Sleep Score when has_magnesium=1 vs 0.
   175→        """
   176→        df = self.get_dataframe(days)
   177→        if df.empty: return {"error": "No data"}
   178→
   179→        if condition_col not in df.columns or target_col not in df.columns:
   180→            return {"error": f"Columns not found: {condition_col} or {target_col}"}
   181→
   182→        group_true = df[df[condition_col] > 0][target_col]
   183→        group_false = df[df[condition_col] == 0][target_col]
   184→
   185→        if len(group_true) == 0 or len(group_false) == 0:
   186→            return {"error": "One group has no data (e.g. never took supplement)"}
   187→
   188→        avg_true = group_true.mean()
   189→        avg_false = group_false.mean()
   190→        diff_pct = ((avg_true - avg_false) / avg_false) * 100 if avg_false != 0 else 0
   191→
   192→        return {
   193→            "condition": condition_col,
   194→            "target": target_col,
   195→            "avg_with_condition": float(avg_true),
   196→            "avg_without_condition": float(avg_false),
   197→            "sample_with": len(group_true),
   198→            "sample_without": len(group_false),
   199→            "difference_pct": float(diff_pct),
   200→            "verdict": "better" if avg_true > avg_false else "worse"
   201→        }
   202→    def analyze_lagged_correlation(self, driver: str, target: str, lag: int = 1, days: int = 90) -> Dict[str, Any]:
   203→        """
   204→        Analyze if 'driver' on Day T correlates with 'target' on Day T+lag.
   205→        Example: Alcohol (T) -> Sleep Score (T+1).
   206→
   207→        Args:
   208→            driver: Column name of the driver (e.g., 'alcohol_units', 'stress').
   209→            target: Column name of the target (e.g., 'sleep', 'hrv', 'rhr').
   210→            lag: Days to shift target (1 means next day).
   211→            days: Lookback window.
   212→        """
   213→        df = self.get_dataframe(days)
   214→        if df.empty: return {"error": "No data"}
   215→
   216→        # Verify columns exist
   217→        if driver not in df.columns or target not in df.columns:
   218→            return {"error": f"Columns not found: {driver} or {target}"}
   219→
   220→        # Shift target
   221→        # lag=1 means we align Driver(T) with Target(T+1)
   222→        target_col = f"target_lag_{lag}"
   223→        df[target_col] = df[target].shift(-lag)
   224→
   225→        # Filter valid rows
   226→        valid = df.dropna(subset=[driver, target_col])
   227→        if len(valid) < 5:
   228→            return {"error": "Not enough data points (<5)"}
   229→
   230→        # Calculate Correlation
   231→        corr = valid[driver].corr(valid[target_col])
   232→
   233→        return {
   234→            "driver": driver,
   235→            "target": target,
   236→            "lag_days": lag,
   237→            "correlation": float(corr) if not pd.isna(corr) else 0.0,
   238→            "sample_size": len(valid),
   239→            "msg": f" Correlation {corr:.2f} (1.0 is perfect positive, -1.0 is perfect negative)"
   240→        }
   241→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→"""
     2→Query service for health data analysis.
     3→
     4→Provides methods to query and analyze health data from storage.
     5→"""
     6→
     7→import json
     8→from datetime import date, timedelta
     9→from pathlib import Path
    10→from typing import Dict, List, Optional, Any, Union
    11→
    12→from health import config
    13→from health.db.repository import HealthRepository
    14→from health.services.storage import HealthStorage
    15→from health.utils.logging_config import setup_logger
    16→
    17→logger = setup_logger(__name__)
    18→
    19→
    20→class HealthDataQuery:
    21→    """Service for querying and analyzing health data."""
    22→
    23→    def __init__(
    24→        self, storage: Optional[HealthStorage] = None, repo: Optional[HealthRepository] = None
    25→    ) -> None:
    26→        """Initialize query service.
    27→
    28→        Args:
    29→            storage: Optional storage service instance
    30→            repo: Optional repository instance
    31→        """
    32→        self.storage = storage or HealthStorage()
    33→        self.repo = repo or HealthRepository()
    34→
    35→    def get_daily_summary(self, target_date: date) -> Dict[str, Any]:
    36→        """Get a summary of all health metrics for a specific day.
    37→
    38→        Args:
    39→            target_date: Date to query
    40→
    41→        Returns:
    42→            Dictionary containing all available metrics for the day
    43→        """
    44→        logger.info(f"Querying health data summary for {target_date}")
    45→
    46→        summary = {
    47→            "date": target_date.isoformat(),
    48→            "metrics": {},
    49→            "activities": [],
    50→        }
    51→
    52→        # Query all daily metrics
    53→        for metric_type in config.DATA_TYPE_CONFIG.keys():
    54→            # Skip activities (handled separately) and weight (body metric)
    55→            if metric_type in ["activities", "weight"]:
    56→                continue
    57→
    58→            data = self.storage.load_daily_metric(metric_type, target_date)
    59→            if data:
    60→                summary["metrics"][metric_type] = data
    61→
    62→        # Query activities for the day
    63→        activities = self.repo.get_activities_by_date_range(target_date, target_date)
    64→        for activity in activities:
    65→            activity_data = self.storage.load_activity(activity["activity_id"])
    66→            if activity_data:
    67→                summary["activities"].append(activity_data)
    68→
    69→        # Query weight (if available)
    70→        weight_data = self.storage.load_daily_metric("weight", target_date)
    71→        if weight_data:
    72→            summary["metrics"]["weight"] = weight_data
    73→
    74→        logger.info(
    75→            f"Found {len(summary['metrics'])} metrics and {len(summary['activities'])} activities"
    76→        )
    77→
    78→        return summary
    79→
    80→    def get_metric_range(
    81→        self, metric_type: str, start_date: date, end_date: date
    82→    ) -> List[Dict[str, Any]]:
    83→        """Get a specific metric's data over a date range.
    84→
    85→        Args:
    86→            metric_type: Type of metric (e.g., "steps", "sleep", "heart_rate")
    87→            start_date: Start date of range
    88→            end_date: End date of range
    89→
    90→        Returns:
    91→            List of metric data dictionaries, ordered by date
    92→
    93→        Raises:
    94→            ValueError: If metric_type is invalid
    95→        """
    96→        if metric_type not in config.DATA_TYPE_CONFIG:
    97→            valid_types = ", ".join(config.DATA_TYPE_CONFIG.keys())
    98→            raise ValueError(
    99→                f"Invalid metric type: {metric_type}. Valid types: {valid_types}"
   100→            )
   101→
   102→        logger.info(f"Querying {metric_type} from {start_date} to {end_date}")
   103→
   104→        results = []
   105→        current_date = start_date
   106→
   107→        while current_date <= end_date:
   108→            data = self.storage.load_daily_metric(metric_type, current_date)
   109→            if data:
   110→                results.append(data)
   111→            current_date += timedelta(days=1)
   112→
   113→        logger.info(f"Found {len(results)} records for {metric_type}")
   114→
   115→        return results
   116→
   117→    def get_activities_range(
   118→        self, start_date: date, end_date: date, activity_type: Optional[str] = None
   119→    ) -> List[Dict[str, Any]]:
   120→        """Get all activities within a date range.
   121→
   122→        Args:
   123→            start_date: Start date of range
   124→            end_date: End date of range
   125→            activity_type: Optional filter by activity type
   126→
   127→        Returns:
   128→            List of activity data dictionaries
   129→        """
   130→        logger.info(f"Querying activities from {start_date} to {end_date}")
   131→
   132→        # Get activity index entries
   133→        activity_indices = self.repo.get_activities_by_date_range(start_date, end_date)
   134→
   135→        # Load full activity data
   136→        activities = []
   137→        for idx in activity_indices:
   138→            # Filter by activity type if specified
   139→            if activity_type and idx.get("activity_type") != activity_type:
   140→                continue
   141→
   142→            activity_data = self.storage.load_activity(idx["activity_id"])
   143→            if activity_data:
   144→                activities.append(activity_data)
   145→
   146→        logger.info(f"Found {len(activities)} activities")
   147→
   148→        return activities
   149→
   150→    def get_metric_statistics(
   151→        self, metric_type: str, start_date: date, end_date: date, fields: List[str]
   152→    ) -> Dict[str, Dict[str, Any]]:
   153→        """Calculate statistics for specific metric fields over a date range.
   154→
   155→        Args:
   156→            metric_type: Type of metric
   157→            start_date: Start date of range
   158→            end_date: End date of range
   159→            fields: List of field names to calculate statistics for
   160→
   161→        Returns:
   162→            Dictionary mapping field names to their statistics (min, max, avg, count)
   163→        """
   164→        logger.info(
   165→            f"Calculating statistics for {metric_type} fields: {fields} from {start_date} to {end_date}"
   166→        )
   167→
   168→        # Get all data for the range
   169→        data_list = self.get_metric_range(metric_type, start_date, end_date)
   170→
   171→        # Calculate statistics for each field
   172→        stats = {}
   173→        for field in fields:
   174→            values = []
   175→            for data in data_list:
   176→                if field in data and data[field] is not None:
   177→                    values.append(data[field])
   178→
   179→            if values:
   180→                stats[field] = {
   181→                    "min": min(values),
   182→                    "max": max(values),
   183→                    "avg": sum(values) / len(values),
   184→                    "count": len(values),
   185→                    "sum": sum(values),
   186→                }
   187→            else:
   188→                stats[field] = {
   189→                    "min": None,
   190→                    "max": None,
   191→                    "avg": None,
   192→                    "count": 0,
   193→                    "sum": None,
   194→                }
   195→
   196→        return stats
   197→
   198→    def get_available_dates(self, metric_type: str) -> List[str]:
   199→        """Get all available dates for a metric type.
   200→
   201→        Args:
   202→            metric_type: Type of metric
   203→
   204→        Returns:
   205→            List of date strings in ISO format
   206→        """
   207→        # This is a simplified implementation - in a real scenario,
   208→        # you might want to query the database index for better performance
   209→        storage_path = config.DATA_TYPE_CONFIG[metric_type]["storage_path"]
   210→        base_path = config.DATA_DIR / storage_path
   211→
   212→        dates = []
   213→        if base_path.exists():
   214→            # Walk through year/month directories
   215→            for year_dir in sorted(base_path.iterdir()):
   216→                if not year_dir.is_dir():
   217→                    continue
   218→                for month_dir in sorted(year_dir.iterdir()):
   219→                    if not month_dir.is_dir():
   220→                        continue
   221→                    for file_path in sorted(month_dir.glob("*.json")):
   222→                        # Extract date from filename (YYYY-MM-DD.json)
   223→                        date_str = file_path.stem
   224→                        dates.append(date_str)
   225→
   226→        return sorted(dates)
   227→
   228→    def get_latest_data(self, metric_type: str, days: int = 7) -> List[Dict[str, Any]]:
   229→        """Get the most recent data for a metric.
   230→
   231→        Args:
   232→            metric_type: Type of metric
   233→            days: Number of days to look back (default: 7)
   234→
   235→        Returns:
   236→            List of metric data dictionaries for the last N days
   237→        """
   238→        end_date = date.today()
   239→        start_date = end_date - timedelta(days=days - 1)
   240→
   241→        return self.get_metric_range(metric_type, start_date, end_date)
   242→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


-rw-r--r-- 1  501 games 3245 Jan 19 19:23 activity.py
-rw-r--r-- 1  501 games 2393 Jan 19 18:04 body_metrics.py
-rw-r--r-- 1  501 games 9177 Jan 19 19:23 daily_metrics.py
-rw-r--r-- 1  501 games   50 Jan 19 14:07 __init__.py
-rw-r--r-- 1  501 games 3474 Jan 20 15:01 manual_log.py
-rw-r--r-- 1  501 games 1130 Jan 19 14:07 sync_metadata.py


Now let me continue reading the remaining files:


ls -la /root/projects/butler/scripts/ | grep -E "\.py$"


     1→"""
     2→Storage service for manual health logs.
     3→
     4→Handles reading/writing user-entered diet, alcohol, supplement, and feeling data.
     5→"""
     6→
     7→import json
     8→from datetime import date, datetime
     9→from pathlib import Path
    10→from typing import Optional, List
    11→
    12→from health import config
    13→from health.models.manual_log import (
    14→    DailyManualLog,
    15→    DietEntry,
    16→    AlcoholEntry,
    17→    SupplementEntry,
    18→    BodyFeelingEntry,
    19→)
    20→from health.utils.logging_config import setup_logger
    21→
    22→logger = setup_logger(__name__)
    23→
    24→
    25→class ManualLogStorage:
    26→    """Service for storing and retrieving manual health logs."""
    27→
    28→    def __init__(self, data_dir: Optional[Path] = None) -> None:
    29→        """Initialize manual log storage service.
    30→
    31→        Args:
    32→            data_dir: Optional data directory path (defaults to config.DATA_DIR)
    33→        """
    34→        self.data_dir = data_dir or config.DATA_DIR
    35→        self.manual_log_dir = self.data_dir / "manual_logs"
    36→        self.manual_log_dir.mkdir(parents=True, exist_ok=True)
    37→
    38→    def _get_file_path(self, target_date: date, create_dirs: bool = True) -> Path:
    39→        """Get file path for a manual log.
    40→
    41→        Args:
    42→            target_date: Date of the log
    43→            create_dirs: Whether to create directories if they don't exist
    44→
    45→        Returns:
    46→            Full path to JSON file (data/health/manual_logs/YYYY/MM/YYYY-MM-DD.json)
    47→        """
    48→        year = str(target_date.year)
    49→        month = f"{target_date.month:02d}"
    50→        filename = f"{target_date.isoformat()}.json"
    51→
    52→        file_path = self.manual_log_dir / year / month / filename
    53→
    54→        if create_dirs:
    55→            file_path.parent.mkdir(parents=True, exist_ok=True)
    56→
    57→        return file_path
    58→
    59→    def load_log(self, target_date: date) -> DailyManualLog:
    60→        """Load manual log for a specific date.
    61→
    62→        Args:
    63→            target_date: Date to load
    64→
    65→        Returns:
    66→            DailyManualLog instance (creates new if doesn't exist)
    67→        """
    68→        file_path = self._get_file_path(target_date, create_dirs=False)
    69→
    70→        if not file_path.exists():
    71→            # Return empty log for this date
    72→            logger.debug(f"No manual log found for {target_date}, creating new")
    73→            return DailyManualLog(log_date=target_date)
    74→
    75→        try:
    76→            with open(file_path, "r", encoding="utf-8") as f:
    77→                data = json.load(f)
    78→
    79→            log = DailyManualLog(**data)
    80→            logger.debug(f"Loaded manual log for {target_date}")
    81→            return log
    82→
    83→        except Exception as e:
    84→            logger.error(f"Failed to load manual log for {target_date}: {e}")
    85→            # Return empty log on error
    86→            return DailyManualLog(log_date=target_date)
    87→
    88→    def save_log(self, log: DailyManualLog) -> Path:
    89→        """Save manual log to JSON file.
    90→
    91→        Args:
    92→            log: DailyManualLog instance to save
    93→
    94→        Returns:
    95→            Path to saved file
    96→        """
    97→        try:
    98→            # Update timestamp
    99→            log.updated_at = datetime.now()
   100→
   101→            file_path = self._get_file_path(log.log_date)
   102→
   103→            # Convert to dict and save as JSON
   104→            data_dict = log.model_dump(mode="json")
   105→
   106→            with open(file_path, "w", encoding="utf-8") as f:
   107→                json.dump(data_dict, f, indent=2, ensure_ascii=False)
   108→
   109→            logger.info(f"Saved manual log for {log.log_date} to {file_path}")
   110→            return file_path
   111→
   112→        except Exception as e:
   113→            logger.error(f"Failed to save manual log for {log.log_date}: {e}")
   114→            raise
   115→
   116→    def add_diet_entry(
   117→        self, target_date: date, entry: DietEntry
   118→    ) -> DailyManualLog:
   119→        """Add a diet entry to the log.
   120→
   121→        Args:
   122→            target_date: Date of the entry
   123→            entry: DietEntry to add
   124→
   125→        Returns:
   126→            Updated DailyManualLog
   127→        """
   128→        log = self.load_log(target_date)
   129→        log.diet_entries.append(entry)
   130→        self.save_log(log)
   131→        logger.info(f"Added diet entry for {target_date} at {entry.time}")
   132→        return log
   133→
   134→    def add_alcohol_entry(
   135→        self, target_date: date, entry: AlcoholEntry
   136→    ) -> DailyManualLog:
   137→        """Add an alcohol entry to the log.
   138→
   139→        Args:
   140→            target_date: Date of the entry
   141→            entry: AlcoholEntry to add
   142→
   143→        Returns:
   144→            Updated DailyManualLog
   145→        """
   146→        log = self.load_log(target_date)
   147→        log.alcohol_entries.append(entry)
   148→        self.save_log(log)
   149→        logger.info(f"Added alcohol entry for {target_date} at {entry.time}")
   150→        return log
   151→
   152→    def add_supplement_entry(
   153→        self, target_date: date, entry: SupplementEntry
   154→    ) -> DailyManualLog:
   155→        """Add a supplement entry to the log.
   156→
   157→        Args:
   158→            target_date: Date of the entry
   159→            entry: SupplementEntry to add
   160→
   161→        Returns:
   162→            Updated DailyManualLog
   163→        """
   164→        log = self.load_log(target_date)
   165→        log.supplement_entries.append(entry)
   166→        self.save_log(log)
   167→        logger.info(f"Added supplement entry for {target_date}: {entry.supplement_name}")
   168→        return log
   169→
   170→    def add_feeling_entry(
   171→        self, target_date: date, entry: BodyFeelingEntry
   172→    ) -> DailyManualLog:
   173→        """Add a body feeling entry to the log.
   174→
   175→        Args:
   176→            target_date: Date of the entry
   177→            entry: BodyFeelingEntry to add
   178→
   179→        Returns:
   180→            Updated DailyManualLog
   181→        """
   182→        log = self.load_log(target_date)
   183→        log.feeling_entries.append(entry)
   184→        self.save_log(log)
   185→        logger.info(f"Added feeling entry for {target_date}: {entry.feeling_type}")
   186→        return log
   187→
   188→    def set_fasting_mode(self, target_date: date, mode: str) -> DailyManualLog:
   189→        """Set the fasting mode for a day.
   190→
   191→        Args:
   192→            target_date: Date to set
   193→            mode: Fasting mode (PSMF/OMAD/Water Fast/Normal)
   194→
   195→        Returns:
   196→            Updated DailyManualLog
   197→        """
   198→        log = self.load_log(target_date)
   199→        log.fasting_mode = mode
   200→        self.save_log(log)
   201→        logger.info(f"Set fasting mode for {target_date}: {mode}")
   202→        return log
   203→
   204→    def get_logs_in_range(
   205→        self, start_date: date, end_date: date
   206→    ) -> List[DailyManualLog]:
   207→        """Get all manual logs in a date range.
   208→
   209→        Args:
   210→            start_date: Start date (inclusive)
   211→            end_date: End date (inclusive)
   212→
   213→        Returns:
   214→            List of DailyManualLog instances
   215→        """
   216→        logs = []
   217→        current_date = start_date
   218→
   219→        while current_date <= end_date:
   220→            log = self.load_log(current_date)
   221→            # Only include logs that have actual data
   222→            if (
   223→                log.diet_entries
   224→                or log.alcohol_entries
   225→                or log.supplement_entries
   226→                or log.feeling_entries
   227→                or log.fasting_mode
   228→            ):
   229→                logs.append(log)
   230→
   231→            # Move to next day
   232→            from datetime import timedelta
   233→            current_date += timedelta(days=1)
   234→
   235→        return logs
   236→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→"""
     2→CLI tool for Garmin health data synchronization.
     3→
     4→Usage:
     5→    python -m health.cli.sync status           # Show sync status
     6→    python -m health.cli.sync sync-now         # Sync today
     7→    python -m health.cli.sync sync --days 7    # Sync last 7 days
     8→    python -m health.cli.sync backfill --start-date 2024-01-01  # Historical backfill
     9→"""
    10→
    11→import sys
    12→from datetime import date, timedelta
    13→from typing import Optional
    14→
    15→import click
    16→
    17→from health import config
    18→from health.services.data_sync import HealthDataSync
    19→from health.utils.date_utils import parse_date, get_yesterday
    20→from health.utils.logging_config import setup_logger
    21→from health.utils.exceptions import GarminAuthError, SyncError
    22→
    23→logger = setup_logger(__name__)
    24→
    25→
    26→@click.group()
    27→def cli() -> None:
    28→    """Garmin Health Data Sync CLI."""
    29→    pass
    30→
    31→
    32→@cli.command()
    33→def status() -> None:
    34→    """Show synchronization status for all data types."""
    35→    click.echo("📊 Health Data Sync Status\n")
    36→
    37→    sync_service = HealthDataSync()
    38→    status_info = sync_service.get_sync_status()
    39→
    40→    # Group by status
    41→    synced = []
    42→    never_synced = []
    43→
    44→    for data_type, info in status_info.items():
    45→        if info["status"] == "synced":
    46→            synced.append((data_type, info))
    47→        else:
    48→            never_synced.append(data_type)
    49→
    50→    # Display synced data types
    51→    if synced:
    52→        click.echo("✅ Synced Data Types:")
    53→        for data_type, info in synced:
    54→            description = config.DATA_TYPE_CONFIG.get(data_type, {}).get(
    55→                "description", ""
    56→            )
    57→            click.echo(
    58→                f"  • {data_type:20} Last sync: {info['last_sync_date']}  "
    59→                f"({info['total_records']} records) - {description}"
    60→            )
    61→
    62→    # Display never synced
    63→    if never_synced:
    64→        click.echo("\n⚠  Never Synced:")
    65→        for data_type in never_synced:
    66→            description = config.DATA_TYPE_CONFIG.get(data_type, {}).get(
    67→                "description", ""
    68→            )
    69→            click.echo(f"  • {data_type:20} {description}")
    70→
    71→    click.echo(f"\n📁 Data directory: {config.DATA_DIR}")
    72→    click.echo(f"🗄  Database: {config.DB_PATH}")
    73→
    74→
    75→@cli.command()
    76→@click.option(
    77→    "--days",
    78→    "-d",
    79→    type=int,
    80→    default=1,
    81→    help="Number of days to sync (from yesterday backwards)",
    82→)
    83→@click.option("--force", "-f", is_flag=True, help="Force re-sync existing data")
    84→def sync(days: int, force: bool) -> None:
    85→    """Sync health data for recent days."""
    86→    try:
    87→        # Calculate date range
    88→        end_date = get_yesterday()
    89→        start_date = end_date - timedelta(days=days - 1)
    90→
    91→        click.echo(f"🔄 Syncing health data from {start_date} to {end_date}")
    92→        if force:
    93→            click.echo("   (Force mode: re-syncing existing data)")
    94→
    95→        # Initialize sync service
    96→        sync_service = HealthDataSync()
    97→
    98→        # Authenticate
    99→        click.echo("🔐 Authenticating with Garmin Connect China...")
   100→        sync_service.authenticate()
   101→        click.echo("✅ Authentication successful\n")
   102→
   103→        # Sync all metrics
   104→        results = sync_service.sync_all_metrics(
   105→            start_date, end_date, force=force
   106→        )
   107→
   108→        # Display results
   109→        click.echo("\n📊 Sync Results:")
   110→        total_synced = 0
   111→        total_errors = 0
   112→
   113→        for metric_type, stats in results.items():
   114→            synced = stats.get("synced", 0)
   115→            skipped = stats.get("skipped", 0)
   116→            errors = stats.get("errors", 0)
   117→
   118→            total_synced += synced
   119→            total_errors += errors
   120→
   121→            status_icon = "✓" if errors == 0 else "⚠"
   122→            click.echo(
   123→                f"  {status_icon} {metric_type:20} "
   124→                f"Synced: {synced:3}, Skipped: {skipped:3}, Errors: {errors:3}"
   125→            )
   126→
   127→        click.echo(
   128→            f"\n{'✅' if total_errors == 0 else '⚠ '} "
   129→            f"Total: {total_synced} records synced, {total_errors} errors"
   130→        )
   131→
   132→        sys.exit(0 if total_errors == 0 else 1)
   133→
   134→    except GarminAuthError as e:
   135→        click.echo(f"\n❌ Authentication failed: {e}", err=True)
   136→        click.echo(
   137→            "   Please check your Garmin credentials in the .env file.", err=True
   138→        )
   139→        sys.exit(1)
   140→    except Exception as e:
   141→        click.echo(f"\n❌ Sync failed: {e}", err=True)
   142→        logger.exception("Sync failed")
   143→        sys.exit(1)
   144→
   145→
   146→@cli.command()
   147→@click.option(
   148→    "--start-date",
   149→    "-s",
   150→    required=True,
   151→    help="Start date for backfill (YYYY-MM-DD)",
   152→)
   153→@click.option(
   154→    "--end-date",
   155→    "-e",
   156→    default=None,
   157→    help="End date for backfill (defaults to yesterday)",
   158→)
   159→@click.option(
   160→    "--batch-size",
   161→    "-b",
   162→    type=int,
   163→    default=config.DEFAULT_BATCH_SIZE_DAYS,
   164→    help=f"Days per batch (default: {config.DEFAULT_BATCH_SIZE_DAYS})",
   165→)
   166→@click.option(
   167→    "--metrics",
   168→    "-m",
   169→    multiple=True,
   170→    help="Specific metrics to sync (can be specified multiple times)",
   171→)
   172→def backfill(
   173→    start_date: str,
   174→    end_date: Optional[str],
   175→    batch_size: int,
   176→    metrics: tuple,
   177→) -> None:
   178→    """Backfill historical health data."""
   179→    try:
   180→        # Parse dates
   181→        start = parse_date(start_date)
   182→        end = parse_date(end_date) if end_date else get_yesterday()
   183→
   184→        # Validate date range
   185→        if start > end:
   186→            click.echo("❌ Start date must be before end date", err=True)
   187→            sys.exit(1)
   188→
   189→        total_days = (end - start).days + 1
   190→
   191→        click.echo(f"📚 Historical Backfill")
   192→        click.echo(f"   Start date: {start}")
   193→        click.echo(f"   End date: {end}")
   194→        click.echo(f"   Total days: {total_days}")
   195→        click.echo(f"   Batch size: {batch_size} days")
   196→
   197→        if metrics:
   198→            click.echo(f"   Metrics: {', '.join(metrics)}")
   199→
   200→        # Confirm large backfills
   201→        if total_days > 90:
   202→            click.confirm(
   203→                f"\n⚠  This will backfill {total_days} days of data. Continue?",
   204→                abort=True,
   205→            )
   206→
   207→        # Initialize sync service
   208→        sync_service = HealthDataSync()
   209→
   210→        # Authenticate
   211→        click.echo("\n🔐 Authenticating with Garmin Connect China...")
   212→        sync_service.authenticate()
   213→        click.echo("✅ Authentication successful\n")
   214→
   215→        # Perform backfill
   216→        metric_list = list(metrics) if metrics else None
   217→        results = sync_service.backfill_historical(
   218→            start, end, metric_types=metric_list, batch_size=batch_size
   219→        )
   220→
   221→        # Display final results
   222→        click.echo("\n📊 Backfill Complete!")
   223→        total_synced = sum(s.get("synced", 0) for s in results.values())
   224→        total_errors = sum(s.get("errors", 0) for s in results.values())
   225→
   226→        for metric_type, stats in results.items():
   227→            synced = stats.get("synced", 0)
   228→            errors = stats.get("errors", 0)
   229→            status_icon = "✓" if errors == 0 else "⚠"
   230→
   231→            click.echo(
   232→                f"  {status_icon} {metric_type:20} {synced:4} records  "
   233→                f"({errors} errors)" if errors else f"  {status_icon} {metric_type:20} {synced:4} records"
   234→            )
   235→
   236→        click.echo(
   237→            f"\n{'✅' if total_errors == 0 else '⚠ '} "
   238→            f"Total: {total_synced} records synced"
   239→        )
   240→
   241→        sys.exit(0 if total_errors == 0 else 1)
   242→
   243→    except GarminAuthError as e:
   244→        click.echo(f"\n❌ Authentication failed: {e}", err=True)
   245→        sys.exit(1)
   246→    except Exception as e:
   247→        click.echo(f"\n❌ Backfill failed: {e}", err=True)
   248→        logger.exception("Backfill failed")
   249→        sys.exit(1)
   250→
   251→
   252→@cli.command()
   253→def sync_incremental() -> None:
   254→    """Perform incremental sync (sync new data since last sync)."""
   255→    try:
   256→        click.echo("🔄 Incremental Sync (syncing new data only)\n")
   257→
   258→        # Initialize sync service
   259→        sync_service = HealthDataSync()
   260→
   261→        # Authenticate
   262→        click.echo("🔐 Authenticating...")
   263→        sync_service.authenticate()
   264→        click.echo("✅ Authenticated\n")
   265→
   266→        # Perform incremental sync
   267→        results = sync_service.sync_incremental()
   268→
   269→        # Display results
   270→        click.echo("📊 Sync Results:")
   271→        total_synced = 0
   272→        total_errors = 0
   273→
   274→        for metric_type, stats in results.items():
   275→            synced = stats.get("synced", 0)
   276→            errors = stats.get("errors", 0)
   277→
   278→            if synced > 0 or errors > 0:
   279→                total_synced += synced
   280→                total_errors += errors
   281→
   282→                status_icon = "✓" if errors == 0 else "⚠"
   283→                click.echo(
   284→                    f"  {status_icon} {metric_type:20} {synced:3} new records  "
   285→                    f"({errors} errors)" if errors else f"  {status_icon} {metric_type:20} {synced:3} new records"
   286→                )
   287→
   288→        if total_synced == 0:
   289→            click.echo("  ℹ  All data is up to date!")
   290→
   291→        click.echo(
   292→            f"\n{'✅' if total_errors == 0 else '⚠ '} "
   293→            f"Synced {total_synced} new records"
   294→        )
   295→
   296→        sys.exit(0 if total_errors == 0 else 1)
   297→
   298→    except GarminAuthError as e:
   299→        click.echo(f"\n❌ Authentication failed: {e}", err=True)
   300→        sys.exit(1)
   301→    except Exception as e:
   302→        click.echo(f"\n❌ Incremental sync failed: {e}", err=True)
   303→        logger.exception("Incremental sync failed")
   304→        sys.exit(1)
   305→
   306→
   307→if __name__ == "__main__":
   308→    cli()
   309→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


-rw-r--r--  1  501 games  6849 Feb  6 15:18 analyze_cost.py
-rwxr-xr-x  1  501 games  7934 Jan 20 16:15 analyze_recent_health.py
-rw-r--r--  1  501 games  4445 Jan 23 13:03 bot_manager.py
-rw-r--r--  1  501 games  5269 Feb  6 14:30 compare_local_vs_openrouter.py
-rw-r--r--  1  501 games  1406 Feb  6 11:23 compare_with_without_tools.py
-rwxr-xr-x  1  501 games  2718 Jan 20 12:25 daily_sync.py
-rwxr-xr-x  1  501 games  1690 Feb  6 11:21 debug_empty_response.py
-rw-r--r--  1  501 games  3784 Feb  6 14:31 deep_analyze_local_proxy.py
-rwxr-xr-x  1  501 games  6838 Feb  6 10:55 diagnose_gemini.py
-rw-r--r--  1  501 games  3514 Jan 29 17:29 diagnose_shell_bot.py
-rw-r--r--  1  501 games   821 Jan 20 16:18 force_sync_today.py
-rwxr-xr-x  1  501 games  8123 Feb  6 15:08 health_check.py
-rw-r--r--  1  501 games  3537 Jan 22 10:06 migrate_commands.py
-rwxr-xr-x  1  501 games  5468 Feb  6 15:20 monitor_proxy_health.py
-rw-r--r--  1  501 games  2245 Jan 23 11:51 obsidian_bot.py
-rw-r--r--  1  501 games  3241 Jan 22 14:48 obsidian_distiller.py
-rw-r--r--  1  501 games   745 Jan 22 11:38 obsidian_tool.py
-rw-r--r--  1  501 games  3868 Jan 20 10:26 query_examples.py
-rw-r--r--  1  501 games  2500 Jan 20 10:55 resync_steps.py
-rwxr-xr-x  1  501 games  1763 Jan 23 10:20 run_tests.py
-rwxr-xr-x  1  501 games 10072 Feb  6 15:27 select_gemini.py
-rwxr-xr-x  1  501 games  2550 Jan 19 14:14 setup_health.py
-rw-r--r--  1  501 games  2603 Feb  6 14:40 simulate_full_bot_flow.py
-rw-r--r--  1  501 games  2508 Feb  6 11:46 test_binary_search_prompt.py
-rw-r--r--  1  501 games  2007 Feb  6 14:34 test_clean_minimal.py
-rw-r--r--  1  501 games  2191 Feb  6 11:42 test_direct_google_api.py
-rw-r--r--  1  501 games  2894 Feb  6 11:36 test_exact_scenario.py
-rw-r--r--  1  501 games  1406 Feb  6 14:13 test_length_limit.py
-rw-r--r--  1  501 games  1338 Feb  6 11:00 test_light_mode.py
-rw-r--r--  1  501 games  1572 Feb  6 14:35 test_one_simple_tool.py
-rw-r--r--  1  501 games  1670 Feb  6 11:45 test_progressive_prompt.py
-rw-r--r--  1  501 games  1770 Feb  6 11:37 test_progressive_tools.py
-rw-r--r--  1  501 games  1084 Feb  6 11:44 test_proxy_workaround.py
-rw-r--r--  1  501 games  1781 Feb  6 11:31 test_safety_override.py
-rw-r--r--  1  501 games  2082 Feb  6 11:39 test_smart_quotes.py
-rw-r--r--  1  501 games  3750 Feb  6 14:38 test_stability.py
-rw-r--r--  1  501 games  3509 Feb  6 11:38 test_system_prompt_impact.py
-rw-r--r--  1  501 games  2374 Feb  6 11:35 test_tool_choice.py
-rw-r--r--  1  501 games  2113 Feb  6 15:06 test_tool_count_impact.py
-rwxr-xr-x  1  501 games  2627 Jan 23 11:32 tm.py
-rw-r--r--  1  501 games   900 Jan 28 16:14 verify_imports.py
-rw-r--r--  1  501 games   685 Jan 28 16:35 verify_obsidian_tools.py
-rw-r--r--  1  501 games  1980 Feb  2 10:09 verify_sync.py
-rw-r--r--  1  501 games  5578 Jan 19 18:05 verify_system.py
-rw-r--r--  1  501 games   163 Jan 28 16:10 verify_time.py


Now let me read the model files to understand the data structures:


     1→"""
     2→Pydantic models for daily health metrics.
     3→
     4→Includes models for steps, heart rate, sleep, stress, body battery, and more.
     5→"""
     6→
     7→from datetime import date as date_type, time as time_type
     8→from typing import Optional, List, Dict, Any
     9→from pydantic import BaseModel, Field
    10→
    11→
    12→class StepsData(BaseModel):
    13→    """Daily step count and related metrics."""
    14→
    15→    date: date_type = Field(..., description="Date of the data")
    16→    total_steps: Optional[int] = Field(default=None, description="Total steps taken")
    17→    total_distance_meters: Optional[float] = Field(
    18→        default=None, description="Total distance in meters"
    19→    )
    20→    calories_burned: Optional[int] = Field(
    21→        default=None, description="Calories burned from steps"
    22→    )
    23→    step_goal: Optional[int] = Field(default=None, description="Daily step goal")
    24→    raw_data: Optional[Dict[str, Any]] = Field(
    25→        default=None, description="Raw API response"
    26→    )
    27→
    28→
    29→class HeartRateData(BaseModel):
    30→    """Daily heart rate metrics."""
    31→
    32→    date: date_type = Field(..., description="Date of the data")
    33→    resting_heart_rate: Optional[int] = Field(
    34→        default=None, description="Resting heart rate in bpm"
    35→    )
    36→    min_heart_rate: Optional[int] = Field(
    37→        default=None, description="Minimum heart rate in bpm"
    38→    )
    39→    max_heart_rate: Optional[int] = Field(
    40→        default=None, description="Maximum heart rate in bpm"
    41→    )
    42→    average_heart_rate: Optional[int] = Field(
    43→        default=None, description="Average heart rate in bpm"
    44→    )
    45→    heart_rate_zones: Optional[Dict[str, int]] = Field(
    46→        default=None, description="Time in each heart rate zone (minutes)"
    47→    )
    48→    raw_data: Optional[Dict[str, Any]] = Field(
    49→        default=None, description="Raw API response"
    50→    )
    51→
    52→
    53→class SleepData(BaseModel):
    54→    """Daily sleep metrics."""
    55→
    56→    date: date_type = Field(..., description="Date of the sleep (night)")
    57→    total_sleep_seconds: Optional[int] = Field(
    58→        default=None, description="Total sleep time in seconds"
    59→    )
    60→    deep_sleep_seconds: Optional[int] = Field(
    61→        default=None, description="Deep sleep time in seconds"
    62→    )
    63→    light_sleep_seconds: Optional[int] = Field(
    64→        default=None, description="Light sleep time in seconds"
    65→    )
    66→    rem_sleep_seconds: Optional[int] = Field(
    67→        default=None, description="REM sleep time in seconds"
    68→    )
    69→    awake_seconds: Optional[int] = Field(
    70→        default=None, description="Time awake in seconds"
    71→    )
    72→    sleep_score: Optional[int] = Field(
    73→        default=None, description="Overall sleep quality score (0-100)"
    74→    )
    75→    sleep_start_time: Optional[time_type] = Field(
    76→        default=None, description="Sleep start time"
    77→    )
    78→    sleep_end_time: Optional[time_type] = Field(default=None, description="Sleep end time")
    79→    sleep_levels: Optional[List[Dict[str, Any]]] = Field(
    80→        default=None, description="Detailed sleep level timeline"
    81→    )
    82→    raw_data: Optional[Dict[str, Any]] = Field(
    83→        default=None, description="Raw API response"
    84→    )
    85→
    86→
    87→class StressData(BaseModel):
    88→    """Daily stress level metrics."""
    89→
    90→    date: date_type = Field(..., description="Date of the data")
    91→    average_stress_level: Optional[int] = Field(
    92→        default=None, description="Average stress level (0-100)"
    93→    )
    94→    max_stress_level: Optional[int] = Field(
    95→        default=None, description="Maximum stress level (0-100)"
    96→    )
    97→    rest_stress_duration_seconds: Optional[int] = Field(
    98→        default=None, description="Time in rest/low stress state"
    99→    )
   100→    activity_stress_duration_seconds: Optional[int] = Field(
   101→        default=None, description="Time in activity stress state"
   102→    )
   103→    low_stress_duration_seconds: Optional[int] = Field(
   104→        default=None, description="Time in low stress state"
   105→    )
   106→    medium_stress_duration_seconds: Optional[int] = Field(
   107→        default=None, description="Time in medium stress state"
   108→    )
   109→    high_stress_duration_seconds: Optional[int] = Field(
   110→        default=None, description="Time in high stress state"
   111→    )
   112→    stress_timeline: Optional[List[Any]] = Field(
   113→        default=None, description="Detailed stress timeline (list of [timestamp, value] pairs)"
   114→    )
   115→    raw_data: Optional[Dict[str, Any]] = Field(
   116→        default=None, description="Raw API response"
   117→    )
   118→
   119→
   120→class BodyBatteryData(BaseModel):
   121→    """Daily Body Battery metrics."""
   122→
   123→    date: date_type = Field(..., description="Date of the data")
   124→    charged: Optional[int] = Field(
   125→        default=None, description="Total Body Battery charged"
   126→    )
   127→    drained: Optional[int] = Field(
   128→        default=None, description="Total Body Battery drained"
   129→    )
   130→    highest_value: Optional[int] = Field(
   131→        default=None, description="Highest Body Battery value (0-100)"
   132→    )
   133→    lowest_value: Optional[int] = Field(
   134→        default=None, description="Lowest Body Battery value (0-100)"
   135→    )
   136→    most_recent_value: Optional[int] = Field(
   137→        default=None, description="Most recent Body Battery value"
   138→    )
   139→    timeline: Optional[List[Any]] = Field(
   140→        default=None, description="Detailed Body Battery timeline (list of [timestamp, value] pairs)"
   141→    )
   142→    raw_data: Optional[Dict[str, Any]] = Field(
   143→        default=None, description="Raw API response"
   144→    )
   145→
   146→
   147→class SpO2Data(BaseModel):
   148→    """Daily blood oxygen saturation metrics."""
   149→
   150→    date: date_type = Field(..., description="Date of the data")
   151→    average_spo2: Optional[float] = Field(
   152→        default=None, description="Average SpO2 percentage"
   153→    )
   154→    min_spo2: Optional[float] = Field(
   155→        default=None, description="Minimum SpO2 percentage"
   156→    )
   157→    max_spo2: Optional[float] = Field(
   158→        default=None, description="Maximum SpO2 percentage"
   159→    )
   160→    readings: Optional[List[Dict[str, Any]]] = Field(
   161→        default=None, description="Individual SpO2 readings"
   162→    )
   163→    raw_data: Optional[Dict[str, Any]] = Field(
   164→        default=None, description="Raw API response"
   165→    )
   166→
   167→
   168→class RespirationData(BaseModel):
   169→    """Daily respiration rate metrics."""
   170→
   171→    date: date_type = Field(..., description="Date of the data")
   172→    average_respiration_rate: Optional[float] = Field(
   173→        default=None, description="Average breaths per minute"
   174→    )
   175→    min_respiration_rate: Optional[float] = Field(
   176→        default=None, description="Minimum breaths per minute"
   177→    )
   178→    max_respiration_rate: Optional[float] = Field(
   179→        default=None, description="Maximum breaths per minute"
   180→    )
   181→    raw_data: Optional[Dict[str, Any]] = Field(
   182→        default=None, description="Raw API response"
   183→    )
   184→
   185→
   186→class HydrationData(BaseModel):
   187→    """Daily hydration tracking."""
   188→
   189→    date: date_type = Field(..., description="Date of the data")
   190→    total_intake_ml: Optional[int] = Field(
   191→        default=None, description="Total water intake in milliliters"
   192→    )
   193→    goal_ml: Optional[int] = Field(default=None, description="Daily hydration goal")
   194→    raw_data: Optional[Dict[str, Any]] = Field(
   195→        default=None, description="Raw API response"
   196→    )
   197→
   198→
   199→class FloorsData(BaseModel):
   200→    """Daily floors climbed."""
   201→
   202→    date: date_type = Field(..., description="Date of the data")
   203→    floors_climbed: Optional[int] = Field(
   204→        default=None, description="Number of floors climbed"
   205→    )
   206→    floors_descended: Optional[int] = Field(
   207→        default=None, description="Number of floors descended"
   208→    )
   209→    floor_goal: Optional[int] = Field(default=None, description="Daily floor goal")
   210→    raw_data: Optional[Dict[str, Any]] = Field(
   211→        default=None, description="Raw API response"
   212→    )
   213→
   214→
   215→class IntensityMinutesData(BaseModel):
   216→    """Daily intensity minutes."""
   217→
   218→    date: date_type = Field(..., description="Date of the data")
   219→    moderate_minutes: Optional[int] = Field(
   220→        default=None, description="Moderate intensity minutes"
   221→    )
   222→    vigorous_minutes: Optional[int] = Field(
   223→        default=None, description="Vigorous intensity minutes"
   224→    )
   225→    total_minutes: Optional[int] = Field(
   226→        default=None, description="Total intensity minutes"
   227→    )
   228→    weekly_goal: Optional[int] = Field(
   229→        default=None, description="Weekly intensity goal"
   230→    )
   231→    raw_data: Optional[Dict[str, Any]] = Field(
   232→        default=None, description="Raw API response"
   233→    )
   234→
   235→
   236→class HRVData(BaseModel):
   237→    """Daily heart rate variability metrics."""
   238→
   239→    date: date_type = Field(..., description="Date of the data")
   240→    hrv_value: Optional[float] = Field(
   241→        default=None, description="HRV value in milliseconds"
   242→    )
   243→    baseline_hrv: Optional[float] = Field(
   244→        default=None, description="Baseline HRV for comparison"
   245→    )
   246→    status: Optional[str] = Field(
   247→        default=None, description="HRV status (balanced, unbalanced, etc.)"
   248→    )
   249→    raw_data: Optional[Dict[str, Any]] = Field(
   250→        default=None, description="Raw API response"
   251→    )
   252→
   253→
   254→class RHRData(BaseModel):
   255→    """Daily resting heart rate."""
   256→
   257→    date: date_type = Field(..., description="Date of the data")
   258→    resting_heart_rate: Optional[int] = Field(
   259→        default=None, description="Resting heart rate in bpm"
   260→    )
   261→    raw_data: Optional[Dict[str, Any]] = Field(
   262→        default=None, description="Raw API response"
   263→    )
   264→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→"""
     2→Pydantic models for exercise activities and workouts.
     3→"""
     4→
     5→from datetime import datetime, date as date_type
     6→from typing import Optional, List, Dict, Any
     7→from pydantic import BaseModel, Field
     8→
     9→
    10→class Activity(BaseModel):
    11→    """Exercise activity summary."""
    12→
    13→    activity_id: str = Field(..., description="Unique activity ID")
    14→    activity_type: str = Field(..., description="Type of activity (running, cycling, etc.)")
    15→    activity_name: Optional[str] = Field(default=None, description="Activity name")
    16→    date: date_type = Field(..., description="Date of the activity")
    17→    start_time: Optional[datetime] = Field(default=None, description="Activity start time")
    18→    duration_seconds: Optional[float] = Field(
    19→        default=None, description="Activity duration in seconds"
    20→    )
    21→    distance_meters: Optional[float] = Field(
    22→        default=None, description="Distance covered in meters"
    23→    )
    24→    elevation_gain_meters: Optional[float] = Field(
    25→        default=None, description="Elevation gain in meters"
    26→    )
    27→    elevation_loss_meters: Optional[float] = Field(
    28→        default=None, description="Elevation loss in meters"
    29→    )
    30→    calories: Optional[int] = Field(default=None, description="Calories burned")
    31→    average_heart_rate: Optional[int] = Field(
    32→        default=None, description="Average heart rate in bpm"
    33→    )
    34→    max_heart_rate: Optional[int] = Field(
    35→        default=None, description="Maximum heart rate in bpm"
    36→    )
    37→    average_speed_mps: Optional[float] = Field(
    38→        default=None, description="Average speed in meters per second"
    39→    )
    40→    max_speed_mps: Optional[float] = Field(
    41→        default=None, description="Maximum speed in meters per second"
    42→    )
    43→    average_pace_min_per_km: Optional[float] = Field(
    44→        default=None, description="Average pace in minutes per kilometer"
    45→    )
    46→    average_cadence: Optional[float] = Field(
    47→        default=None, description="Average cadence (steps/min or rpm)"
    48→    )
    49→    training_effect: Optional[float] = Field(
    50→        default=None, description="Aerobic training effect"
    51→    )
    52→    anaerobic_training_effect: Optional[float] = Field(
    53→        default=None, description="Anaerobic training effect"
    54→    )
    55→    location_name: Optional[str] = Field(
    56→        default=None, description="Location of the activity"
    57→    )
    58→    description: Optional[str] = Field(
    59→        default=None, description="Activity description"
    60→    )
    61→    raw_data: Optional[Dict[str, Any]] = Field(
    62→        default=None, description="Raw API response"
    63→    )
    64→
    65→
    66→class ActivityDetail(BaseModel):
    67→    """Detailed activity data including splits and laps."""
    68→
    69→    activity_id: str = Field(..., description="Activity ID")
    70→    summary: Activity = Field(..., description="Activity summary")
    71→    laps: Optional[List[Dict[str, Any]]] = Field(
    72→        default=None, description="Lap data"
    73→    )
    74→    splits: Optional[List[Dict[str, Any]]] = Field(
    75→        default=None, description="Split data"
    76→    )
    77→    samples: Optional[List[Dict[str, Any]]] = Field(
    78→        default=None, description="Time-series samples (HR, speed, etc.)"
    79→    )
    80→    gear: Optional[str] = Field(default=None, description="Gear used")
    81→    raw_data: Optional[Dict[str, Any]] = Field(
    82→        default=None, description="Raw API response"
    83→    )
    84→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→"""
     2→Manual health log models for user-entered data.
     3→
     4→Tracks diet, alcohol, supplements, and body feelings.
     5→"""
     6→
     7→from __future__ import annotations
     8→
     9→from datetime import date, datetime
    10→from typing import Optional, List
    11→from pydantic import BaseModel, Field, ConfigDict
    12→
    13→
    14→class DietEntry(BaseModel):
    15→    """Single diet entry for a meal or snack."""
    16→
    17→    time: str = Field(..., description="Time of meal (HH:MM format)")
    18→    description: str = Field(..., description="What was eaten")
    19→    meal_type: Optional[str] = Field(None, description="breakfast/lunch/dinner/snack")
    20→    estimated_calories: Optional[int] = Field(None, description="Estimated calories")
    21→    carbs: Optional[str] = Field(None, description="Low/Medium/High carb content")
    22→    notes: Optional[str] = Field(None, description="Additional notes")
    23→
    24→
    25→class AlcoholEntry(BaseModel):
    26→    """Alcohol consumption entry."""
    27→
    28→    time: str = Field(..., description="Time of consumption (HH:MM format)")
    29→    drink_type: str = Field(..., description="Type of alcohol (wine/spirits/beer/cocktail)")
    30→    amount: str = Field(..., description="Amount consumed (e.g., '2 glasses', '3 shots')")
    31→    food_with_alcohol: Optional[str] = Field(None, description="What food was eaten")
    32→    nac_taken: bool = Field(False, description="Whether NAC was taken pre-drinking")
    33→    notes: Optional[str] = Field(None, description="Additional notes")
    34→
    35→
    36→class SupplementEntry(BaseModel):
    37→    """Supplement intake entry."""
    38→
    39→    time: str = Field(..., description="Time taken (HH:MM format)")
    40→    supplement_name: str = Field(..., description="Name of supplement")
    41→    dosage: Optional[str] = Field(None, description="Dosage taken")
    42→    timing: Optional[str] = Field(None, description="morning/afternoon/evening/before_bed")
    43→    notes: Optional[str] = Field(None, description="Additional notes")
    44→
    45→
    46→class BodyFeelingEntry(BaseModel):
    47→    """Body feeling and symptom entry."""
    48→
    49→    time: str = Field(..., description="Time recorded (HH:MM format)")
    50→    feeling_type: str = Field(
    51→        ...,
    52→        description="Type of feeling (chest_tightness/fatigue/pain/energy/other)"
    53→    )
    54→    severity: Optional[int] = Field(
    55→        None,
    56→        ge=1,
    57→        le=10,
    58→        description="Severity 1-10"
    59→    )
    60→    location: Optional[str] = Field(None, description="Body location if applicable")
    61→    description: str = Field(..., description="Detailed description")
    62→    triggers: Optional[str] = Field(None, description="Possible triggers")
    63→    notes: Optional[str] = Field(None, description="Additional notes")
    64→
    65→
    66→class DailyManualLog(BaseModel):
    67→    """Complete manual log for a single day."""
    68→
    69→    log_date: date = Field(..., description="Date of the log")
    70→    fasting_mode: Optional[str] = Field(
    71→        None,
    72→        description="PSMF/OMAD/Water Fast/Normal"
    73→    )
    74→    diet_entries: List[DietEntry] = Field(default_factory=list)
    75→    alcohol_entries: List[AlcoholEntry] = Field(default_factory=list)
    76→    supplement_entries: List[SupplementEntry] = Field(default_factory=list)
    77→    feeling_entries: List[BodyFeelingEntry] = Field(default_factory=list)
    78→
    79→    daily_summary: Optional[str] = Field(
    80→        None,
    81→        description="Overall summary of the day"
    82→    )
    83→    created_at: datetime = Field(default_factory=datetime.now)
    84→    updated_at: datetime = Field(default_factory=datetime.now)
    85→
    86→    model_config = ConfigDict(
    87→        json_encoders={
    88→            date: lambda v: v.isoformat(),
    89→            datetime: lambda v: v.isoformat(),
    90→        }
    91→    )
    92→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→"""
     2→Pydantic models for body metrics (weight, body composition).
     3→"""
     4→
     5→from datetime import date as date_type, datetime
     6→from typing import Optional, Dict, Any
     7→from pydantic import BaseModel, Field
     8→
     9→
    10→class WeightData(BaseModel):
    11→    """Body weight measurement."""
    12→
    13→    date: date_type = Field(..., description="Date of measurement")
    14→    weight_kg: Optional[float] = Field(default=None, description="Weight in kilograms")
    15→    bmi: Optional[float] = Field(default=None, description="Body Mass Index")
    16→    body_fat_percentage: Optional[float] = Field(
    17→        default=None, description="Body fat percentage"
    18→    )
    19→    body_water_percentage: Optional[float] = Field(
    20→        default=None, description="Body water percentage"
    21→    )
    22→    bone_mass_kg: Optional[float] = Field(
    23→        default=None, description="Bone mass in kilograms"
    24→    )
    25→    muscle_mass_kg: Optional[float] = Field(
    26→        default=None, description="Muscle mass in kilograms"
    27→    )
    28→    timestamp: Optional[datetime] = Field(
    29→        default=None, description="Exact measurement time"
    30→    )
    31→    source: Optional[str] = Field(
    32→        default=None, description="Source of measurement (scale model, manual, etc.)"
    33→    )
    34→    raw_data: Optional[Dict[str, Any]] = Field(
    35→        default=None, description="Raw API response"
    36→    )
    37→
    38→
    39→class BodyComposition(BaseModel):
    40→    """Detailed body composition metrics."""
    41→
    42→    date: date_type = Field(..., description="Date of measurement")
    43→    weight_kg: Optional[float] = Field(default=None, description="Weight in kilograms")
    44→    bmi: Optional[float] = Field(default=None, description="Body Mass Index")
    45→    body_fat_percentage: Optional[float] = Field(
    46→        default=None, description="Body fat percentage"
    47→    )
    48→    skeletal_muscle_mass_kg: Optional[float] = Field(
    49→        default=None, description="Skeletal muscle mass in kilograms"
    50→    )
    51→    bone_mass_kg: Optional[float] = Field(
    52→        default=None, description="Bone mass in kilograms"
    53→    )
    54→    body_water_percentage: Optional[float] = Field(
    55→        default=None, description="Body water percentage"
    56→    )
    57→    visceral_fat_rating: Optional[int] = Field(
    58→        default=None, description="Visceral fat rating"
    59→    )
    60→    metabolic_age: Optional[int] = Field(
    61→        default=None, description="Metabolic age in years"
    62→    )
    63→    raw_data: Optional[Dict[str, Any]] = Field(
    64→        default=None, description="Raw API response"
    65→    )
    66→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Now let me look at scripts that might be similar to what you're building:


     1→#!/usr/bin/env python3
     2→"""Analyze recent health data (sleep, diet, exercise)."""
     3→
     4→import sys
     5→from datetime import date, timedelta
     6→from pathlib import Path
     7→
     8→# Add parent directory to path
     9→sys.path.insert(0, str(Path(__file__).parent.parent))
    10→
    11→from health.services.query import HealthDataQuery
    12→from health.services.manual_log_storage import ManualLogStorage
    13→
    14→
    15→def analyze_sleep(query: HealthDataQuery, days: int = 7) -> None:
    16→    """Analyze sleep data."""
    17→    print("=" * 60)
    18→    print("😴 SLEEP ANALYSIS")
    19→    print("=" * 60)
    20→
    21→    sleep_data = query.get_latest_data("sleep", days=days)
    22→
    23→    if not sleep_data:
    24→        print("❌ No sleep data available\n")
    25→        return
    26→
    27→    total_sleep_times = []
    28→    deep_sleep_times = []
    29→    rem_sleep_times = []
    30→    light_sleep_times = []
    31→    sleep_scores = []
    32→
    33→    print(f"\n📊 Recent {len(sleep_data)} days of sleep:\n")
    34→
    35→    for data in sleep_data[-7:]:  # Last 7 days
    36→        date_str = data.get("date", "Unknown")
    37→        deep_sleep = data.get("deep_sleep_seconds", 0) / 3600
    38→        rem_sleep = data.get("rem_sleep_seconds", 0) / 3600
    39→        light_sleep = data.get("light_sleep_seconds", 0) / 3600
    40→        awake_sleep = data.get("awake_sleep_seconds", 0) / 3600
    41→
    42→        # Calculate total sleep from components (more reliable)
    43→        total_sleep = deep_sleep + rem_sleep + light_sleep
    44→
    45→        sleep_score = data.get("overall_sleep_score", 0)
    46→        if sleep_score == 0:
    47→            sleep_score = data.get("sleep_score", 0)  # Try alternative field name
    48→
    49→        if total_sleep > 0:
    50→            total_sleep_times.append(total_sleep)
    51→        if deep_sleep > 0:
    52→            deep_sleep_times.append(deep_sleep)
    53→        if rem_sleep > 0:
    54→            rem_sleep_times.append(rem_sleep)
    55→        if light_sleep > 0:
    56→            light_sleep_times.append(light_sleep)
    57→        if sleep_score > 0:
    58→            sleep_scores.append(sleep_score)
    59→
    60→        print(f"📅 {date_str}")
    61→        print(f"   Total: {total_sleep:.1f}h | Deep: {deep_sleep:.1f}h | REM: {rem_sleep:.1f}h | Light: {light_sleep:.1f}h")
    62→        if sleep_score > 0:
    63→            print(f"   Sleep Score: {sleep_score}/100")
    64→        print()
    65→
    66→    # Calculate averages
    67→    if total_sleep_times:
    68→        avg_total = sum(total_sleep_times) / len(total_sleep_times)
    69→        print(f"📊 Average Total Sleep: {avg_total:.1f}h/night")
    70→
    71→        if deep_sleep_times and avg_total > 0:
    72→            avg_deep = sum(deep_sleep_times) / len(deep_sleep_times)
    73→            print(f"   Average Deep Sleep: {avg_deep:.1f}h ({avg_deep/avg_total*100:.0f}%)")
    74→
    75→        if rem_sleep_times and avg_total > 0:
    76→            avg_rem = sum(rem_sleep_times) / len(rem_sleep_times)
    77→            print(f"   Average REM Sleep: {avg_rem:.1f}h ({avg_rem/avg_total*100:.0f}%)")
    78→
    79→        if sleep_scores:
    80→            avg_score = sum(sleep_scores) / len(sleep_scores)
    81→            print(f"   Average Sleep Score: {avg_score:.0f}/100")
    82→
    83→    print()
    84→
    85→
    86→def analyze_exercise(query: HealthDataQuery, days: int = 7) -> None:
    87→    """Analyze exercise data."""
    88→    print("=" * 60)
    89→    print("🏃 EXERCISE ANALYSIS")
    90→    print("=" * 60)
    91→
    92→    # Get steps
    93→    steps_data = query.get_latest_data("steps", days=days)
    94→
    95→    # Get intensity minutes
    96→    intensity_data = query.get_latest_data("intensity_minutes", days=days)
    97→
    98→    # Get activities
    99→    end_date = date.today()
   100→    start_date = end_date - timedelta(days=days - 1)
   101→    activities = query.get_activities_range(start_date, end_date)
   102→
   103→    print(f"\n📊 Recent {days} days:\n")
   104→
   105→    # Steps analysis
   106→    if steps_data:
   107→        daily_steps = []
   108→        for data in steps_data[-7:]:
   109→            date_str = data.get("date", "Unknown")
   110→            steps = data.get("total_steps", 0)
   111→            daily_steps.append(steps)
   112→            print(f"📅 {date_str}: {steps:,} steps")
   113→
   114→        if daily_steps:
   115→            avg_steps = sum(daily_steps) / len(daily_steps)
   116→            print(f"\n📊 Average Daily Steps: {avg_steps:,.0f}")
   117→
   118→    # Intensity minutes
   119→    print("\n💪 Intensity Minutes:")
   120→    if intensity_data:
   121→        for data in intensity_data[-7:]:
   122→            date_str = data.get("date", "Unknown")
   123→            moderate = data.get("moderate_intensity_minutes", 0)
   124→            vigorous = data.get("vigorous_intensity_minutes", 0)
   125→            if moderate > 0 or vigorous > 0:
   126→                print(f"📅 {date_str}: Moderate {moderate}min | Vigorous {vigorous}min")
   127→
   128→    # Activities
   129→    print(f"\n🎯 Activities ({len(activities)} total):")
   130→    if activities:
   131→        for activity in activities[-10:]:  # Last 10 activities
   132→            name = activity.get("activity_name", "Unknown")
   133→            activity_type = activity.get("activity_type", "")
   134→            duration = activity.get("duration_seconds", 0) / 60  # Convert to minutes
   135→            distance = activity.get("distance_meters", 0) / 1000  # Convert to km
   136→            calories = activity.get("calories", 0)
   137→            start_time = activity.get("start_time", "Unknown")
   138→            date_str = activity.get("date", "Unknown")
   139→
   140→            print(f"   • {name} ({activity_type})")
   141→            print(f"     {date_str} {start_time} | {duration:.0f}min", end="")
   142→            if distance > 0:
   143→                print(f" | {distance:.1f}km", end="")
   144→            if calories > 0:
   145→                print(f" | {calories}cal", end="")
   146→            print()
   147→
   148→    print()
   149→
   150→
   151→def analyze_diet(storage: ManualLogStorage, days: int = 7) -> None:
   152→    """Analyze diet data from manual logs."""
   153→    print("=" * 60)
   154→    print("🍽  DIET ANALYSIS")
   155→    print("=" * 60)
   156→
   157→    end_date = date.today()
   158→    start_date = end_date - timedelta(days=days - 1)
   159→
   160→    print(f"\n📊 Recent {days} days:\n")
   161→
   162→    fasting_days = []
   163→    supplement_summary = {}
   164→    alcohol_days = []
   165→
   166→    current_date = start_date
   167→    while current_date <= end_date:
   168→        log = storage.load_log(current_date)
   169→
   170→        date_str = current_date.isoformat()
   171→        print(f"📅 {date_str}")
   172→
   173→        # Fasting mode
   174→        if log.fasting_mode:
   175→            fasting_days.append(date_str)
   176→            print(f"   🚫 Fasting Mode: {log.fasting_mode}")
   177→
   178→        # Supplements
   179→        if log.supplement_entries:
   180→            print(f"   💊 Supplements: {len(log.supplement_entries)} entries")
   181→            for entry in log.supplement_entries:
   182→                supp_name = entry.supplement_name
   183→                supplement_summary[supp_name] = supplement_summary.get(supp_name, 0) + 1
   184→
   185→        # Alcohol
   186→        if log.alcohol_entries:
   187→            alcohol_days.append(date_str)
   188→            print(f"   🍷 Alcohol: {len(log.alcohol_entries)} entries")
   189→            for entry in log.alcohol_entries:
   190→                print(f"      - {entry.drink_type}: {entry.amount}")
   191→
   192→        # Diet/Meals
   193→        if log.diet_entries:
   194→            print(f"   🍽  Meals: {len(log.diet_entries)} entries")
   195→
   196→        # Body feelings
   197→        if log.feeling_entries:
   198→            feeling_types = [f.feeling_type for f in log.feeling_entries]
   199→            print(f"   💭 Body Feelings: {', '.join(feeling_types)}")
   200→
   201→        print()
   202→
   203→        current_date += timedelta(days=1)
   204→
   205→    # Summary
   206→    print("📊 Summary:")
   207→    print(f"   Fasting Days: {len(fasting_days)}/{days}")
   208→    print(f"   Alcohol Days: {len(alcohol_days)}/{days}")
   209→
   210→    if supplement_summary:
   211→        print(f"\n   💊 Supplement Adherence:")
   212→        for supp, count in sorted(supplement_summary.items(), key=lambda x: -x[1]):
   213→            print(f"      {supp}: {count}/{days} days ({count/days*100:.0f}%)")
   214→
   215→    print()
   216→
   217→
   218→def main() -> None:
   219→    """Main analysis function."""
   220→    print("\n🏥 HEALTH DATA ANALYSIS - RECENT 7 DAYS\n")
   221→
   222→    query = HealthDataQuery()
   223→    storage = ManualLogStorage()
   224→
   225→    # Analyze sleep
   226→    analyze_sleep(query, days=7)
   227→
   228→    # Analyze exercise
   229→    analyze_exercise(query, days=7)
   230→
   231→    # Analyze diet
   232→    analyze_diet(storage, days=7)
   233→
   234→    print("=" * 60)
   235→    print("✅ Analysis Complete")
   236→    print("=" * 60)
   237→
   238→
   239→if __name__ == "__main__":
   240→    main()
   241→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→#!/usr/bin/env python3
     2→"""
     3→Daily sync script for Garmin health data.
     4→
     5→Performs incremental sync to fetch new data since last sync.
     6→Suitable for running as a cron job.
     7→
     8→Usage:
     9→    python scripts/daily_sync.py
    10→
    11→Cron example (daily at 8:00 AM):
    12→    0 8 * * * cd /path/to/project && /path/to/venv/bin/python scripts/daily_sync.py
    13→"""
    14→
    15→import sys
    16→from pathlib import Path
    17→from datetime import datetime
    18→
    19→# Add parent directory to path
    20→sys.path.insert(0, str(Path(__file__).parent.parent))
    21→
    22→from health.services.data_sync import HealthDataSync
    23→from health.utils.logging_config import setup_logger
    24→from health.utils.exceptions import GarminAuthError
    25→
    26→logger = setup_logger(__name__, log_file="daily_sync.log")
    27→
    28→
    29→def main() -> int:
    30→    """Run daily incremental sync.
    31→
    32→    Returns:
    33→        Exit code (0 for success, 1 for failure)
    34→    """
    35→    start_time = datetime.now()
    36→    logger.info("=" * 60)
    37→    logger.info(f"Daily Sync Started: {start_time.isoformat()}")
    38→    logger.info("=" * 60)
    39→
    40→    try:
    41→        # Initialize sync service
    42→        sync_service = HealthDataSync()
    43→
    44→        # Authenticate
    45→        logger.info("Authenticating with Garmin Connect China...")
    46→        sync_service.authenticate()
    47→        logger.info("Authentication successful")
    48→
    49→        # Perform incremental sync (including today)
    50→        logger.info("Starting incremental sync...")
    51→        from datetime import date
    52→        results = sync_service.sync_incremental(until_date=date.today())
    53→
    54→        # Log results
    55→        total_synced = sum(s.get("synced", 0) for s in results.values())
    56→        total_errors = sum(s.get("errors", 0) for s in results.values())
    57→
    58→        logger.info("\nSync Results:")
    59→        for metric_type, stats in results.items():
    60→            synced = stats.get("synced", 0)
    61→            errors = stats.get("errors", 0)
    62→
    63→            if synced > 0 or errors > 0:
    64→                logger.info(
    65→                    f"  {metric_type}: {synced} synced, {errors} errors"
    66→                )
    67→
    68→        # Final status
    69→        end_time = datetime.now()
    70→        duration = (end_time - start_time).total_seconds()
    71→
    72→        logger.info(f"\nDaily Sync Complete!")
    73→        logger.info(f"Total records synced: {total_synced}")
    74→        logger.info(f"Total errors: {total_errors}")
    75→        logger.info(f"Duration: {duration:.2f} seconds")
    76→        logger.info("=" * 60)
    77→
    78→        return 0 if total_errors == 0 else 1
    79→
    80→    except GarminAuthError as e:
    81→        logger.error(f"Authentication failed: {e}")
    82→        logger.error("Please check Garmin credentials in .env file")
    83→        return 1
    84→
    85→    except Exception as e:
    86→        logger.exception(f"Daily sync failed: {e}")
    87→        return 1
    88→
    89→
    90→if __name__ == "__main__":
    91→    exit_code = main()
    92→    sys.exit(exit_code)
    93→

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


     1→from typing import Optional, List, Dict, Any
     2→from slack_sdk import WebClient
     3→import os
     4→
     5→from health.utils.logging_config import setup_logger
     6→from slack_bot.llm.gemini import GeminiLLM
     7→from slack_bot.context.storage import ContextStorage
     8→from slack_bot.tools.registry import TOOLS_SCHEMA, TOOL_FUNCTIONS
     9→from slack_bot.tools.groups import get_tool_preset
    10→
    11→logger = setup_logger(__name__)
    12→
    13→
    14→class MessageDispatcher:
    15→    """Routes incoming Slack messages to Gemini and executes tools."""
    16→
    17→    def __init__(self, bot_token: Optional[str] = None, system_instruction: Optional[str] = None, tools: Optional[List[Dict]] = None, tool_mode: str = "light"):
    18→        """
    19→        Initialize dispatcher.
    20→        Args:
    21→            bot_token: Optional specific Slack token
    22→            system_instruction: Custom system prompt (e.g. for Shell Bot)
    23→            tools: specific tools list to use (if None, uses preset based on tool_mode)
    24→            tool_mode: 'none' (no tools), 'light' (5 tools), 'standard' (10 tools), 'full' (15 tools), 'all' (21 tools)
    25→        """
    26→        self.llm = GeminiLLM(system_instruction=system_instruction)
    27→
    28→        # Use tool preset if tools not explicitly provided
    29→        if tools is not None:
    30→            self.tools = tools
    31→            self.tool_functions = TOOL_FUNCTIONS
    32→        elif tool_mode == "none":
    33→            self.tools = None
    34→            self.tool_functions = {}
    35→            logger.info("Running in NO-TOOLS mode (pure text)")
    36→        else:
    37→            self.tools, self.tool_functions = get_tool_preset(tool_mode)
    38→            logger.info(f"Using tool preset: {tool_mode} ({len(self.tools)} tools)")
    39→
    40→        # Initialize Slack WebClient for posting replies
    41→        self.bot_token = bot_token or os.environ.get("SLACK_BOT_TOKEN")
    42→        self.client = WebClient(token=self.bot_token)
    43→
    44→        logger.info("Dispatcher initialized with Gemini")
    45→
    46→    def dispatch(self, message_text: str, channel_id: str, user_id: str, response_ts: Optional[str] = None, request_id: str = "N/A", files: Optional[List[Dict]] = None) -> None:
    47→        """
    48→        Process a message and trigger responses.
    49→        """
    50→        import time
    51→        t0 = time.time()
    52→
    53→        prefix = f"[{request_id}]"
    54→        logger.info(f"{prefix} Processing message in {channel_id} from {user_id}")
    55→        if files:
    56→            logger.info(f"{prefix} Message contains {len(files)} files")
    57→
    58→        # 1. Initialize Context
    59→        storage = ContextStorage(channel_id)
    60→
    61→        # Handle "clear" command
    62→        if message_text.strip().lower() in ["clear", "reset", "清除"]:
    63→            storage.clear()
    64→            self.client.chat_postMessage(channel=channel_id, text="🧹 Context cleared. (对话历史已清除)")
    65→            return
    66→
    67→        storage.add_message("user", message_text)
    68→
    69→        # Download images if any
    70→        image_data_list = []
    71→        download_failed = False
    72→        if files:
    73→            image_data_list = self._download_files(files, prefix)
    74→            # Anti-Hallucination: Check if download failed
    75→            if len(image_data_list) == 0:
    76→                failure_msg = "\n[SYSTEM WARNING: User uploaded an image but it FAILED to download/process. You CANNOT see the image. DO NOT GUESS. Inform the user that the image access failed.]"
    77→                logger.warning(f"{prefix} Image download failed! Appending warning to LLM.")
    78→                message_text += failure_msg
    79→                download_failed = True
    80→
    81→        try:
    82→            # 2. Get conversation context
    83→            context = storage.get_context()
    84→            logger.debug(f"{prefix} Context size: {len(context)}")
    85→
    86→            # 3. Process with Gemini (with tools)
    87→            # CRITICAL: If image download failed, disable tools to prevent "400 INVALID_ARGUMENT"
    88→            # (which happens when mixing text-only warning prompts with complex tool schemas in some proxies)
    89→            #
    90→            # NOTE: gemini-3-flash has a bug where it returns empty response with tools,
    91→            # but we use SAFETY OVERRIDE below to manually trigger tools based on keywords
    92→            current_tools = None if download_failed else self.tools
    93→
    94→            logger.info(f"{prefix} Calling Gemini for initial response...")
    95→            logger.info(f"{prefix} Request config: model={self.llm.get_model_name()}, tools={len(current_tools) if current_tools else 0}, context={len(context)-1} msgs")
    96→            t_llm_start = time.time()
    97→            response_text, tool_calls = self.llm.generate_response(
    98→                message=message_text,
    99→                context=context[:-1],
   100→                tools=current_tools,

<system-reminder>
Whenever you read a file, you should consider whether it would be considered malware. You CAN and SHOULD provide analysis of malware, what it is doing. But you MUST refuse to improve or augment the code. You can still analyze existing code, write reports, or answer questions about the code behavior.
</system-reminder>


Let me continue reading the dispatcher to understand the Slack sending pattern:


find /root/projects/butler -type f -name "*.py" -path "*/slack_bot/*" | grep -E "(client|message|post|send)" | head -10