total 120
drwxr-xr-x 3  501 games  4096 Mar  2 18:27 .
drwxr-xr-x 9  501 games  4096 Mar  2 16:56 ..
-rw-r--r-- 1  501 games  3549 Jan 21 14:26 analytics.py
-rw-r--r-- 1  501 games  2676 Jan 28 15:54 file_ops.py
-rw-r--r-- 1  501 games  1733 Jan 21 12:15 garmin.py
-rw-r--r-- 1  501 games  2091 Feb  6 14:59 groups.py
-rw-r--r-- 1 root root  41719 Mar  2 18:27 health_read.py
-rw-r--r-- 1  501 games  4523 Jan 21 08:55 health_write.py
-rw-r--r-- 1  501 games   849 Jan 20 17:27 obsidian.py
-rw-r--r-- 1  501 games  3017 Jan 28 16:17 process_ops.py
drwxr-xr-x 2 root root   4096 Mar  2 18:44 __pycache__
-rw-r--r-- 1  501 games 16669 Feb  4 16:14 registry.py
-rw-r--r-- 1  501 games  1142 Jan 23 11:27 shell_tool.py
-rw-r--r-- 1  501 games  3128 Jan 28 16:13 system_ops.py
-rw-r--r-- 1  501 games  1589 Jan 28 16:14 web.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/scripts/test_light_mode.py
/root/projects/butler/tag20260215/health/models/__init__.py
/root/projects/butler/tag20260215/health/models/activity.py
/root/projects/butler/tag20260215/health/models/body_metrics.py


Let me explore the generators and tool registry:


Bash

Preview obsidian generators code


     1→import os
     2→from typing import List, Dict, Tuple, Optional
     3→from slack_bot.llm.gemini import GeminiLLM
     4→from slack_bot.obsidian.indexer import ObsidianIndexer
     5→from slack_bot.tools.web import WebSearchTool
     6→from health.utils.logging_config import setup_logger
     7→from health.utils.time_utils import get_current_time_str
     8→
     9→logger = setup_logger(__name__)
    10→
    11→
    12→OBSIDIAN_SYSTEM_PROMPT = """You are Butler (Obsidian Edition), an intelligent knowledge assistant connected to the user's second brain.
    13→Current Time: {current_time}
    14→
    15→Your Role:
    16→You are NOT a health assistant. You are a Knowledge Partner designed to help the user think, write, and communicate. You draw directly from the user's local Obsidian notes, values, and methodology.
    17→
    18→Core Capabilities:
    19→1. Writing: You mimic the user's unique writing style (as defined in `writing_style.md`).
    20→2. Communication: You draft high-EQ, logically rigorous replies for professional contexts (as defined in `REPLY-SAMPLE.md`).
    21→3. Decision: You act as a "Devil's Advocate" and strategic advisor using the user's decision frameworks (GPA, IPO).
    22→
    23→Guidelines:
    24→- **Style Alignment**: Strictly adhere to the tone and sentence structures found in the provided samples.
    25→- **Data Source**: Rely primarily on the provided context (RAG) and loaded markdown files.
    26→- **Identity**: You are pragmatic, rational, and value "technological optimism" and "intellectual honesty".
    27→"""
    28→
    29→class BaseGenerator:
    30→    def __init__(self, indexer: ObsidianIndexer):
    31→        formatted_prompt = OBSIDIAN_SYSTEM_PROMPT.format(current_time=get_current_time_str())
    32→        self.llm = GeminiLLM(system_instruction=formatted_prompt)
    33→        self.indexer = indexer
    34→        self.workspace_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))
    35→
    36→    def _read_file(self, filename: str) -> str:
    37→        """Reads a file from the workspace root."""
    38→        path = os.path.join(self.workspace_root, filename)
    39→        try:
    40→            with open(path, "r", encoding="utf-8") as f:
    41→                return f.read()
    42→        except Exception as e:
    43→            logger.error(f"Failed to read {filename}: {e}")
    44→            return ""
    45→
    46→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
    47→        """
    48→        Processes chat input.
    49→        If history is empty, treats input as a Topic/Query and builds a Rich Prompt.
    50→        If history exists, treats input as follow-up instruction.
    51→        Returns (response_text, updated_history)
    52→        """
    53→        raise NotImplementedError
    54→
    55→class WritingAssistant(BaseGenerator):
    56→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
    57→
    58→        is_first_turn = (len(history) == 0)
    59→
    60→        if is_first_turn:
    61→            # === First Turn: Build Rich Prompt ===
    62→            # user_input is treated as "Topic"
    63→
    64→            # Naive splitting for context if formatted as "Topic | Context"
    65→            parts = user_input.split("|", 1)
    66→            topic = parts[0].strip()
    67→            extra_context = parts[1].strip() if len(parts) > 1 else ""
    68→
    69→            # 1. RAG & Samples
    70→            style_samples = self.indexer.get_writing_samples(count=3)
    71→            style_text = "\n\n".join(style_samples)
    72→
    73→            rag_notes = self.indexer.search(topic, limit=5)
    74→            rag_text = "\n\n".join(rag_notes)
    75→
    76→            writing_style_guide = self._read_file("writing_style.md")
    77→            methodology = self._read_file("methodology.md")
    78→
    79→            prompt = f"""You are a ghostwriter for the user. Your goal is to write an article on the TOPIC provided.
    80→
    81→            === YOUR IDENTITY & METHODOLOGY ===
    82→            {methodology}
    83→
    84→            === YOUR WRITING STYLE GUIDE ===
    85→            {writing_style_guide}
    86→
    87→            === YOUR WRITING SAMPLES (MIMIC THIS TONE) ===
    88→            {style_text}
    89→
    90→            === RELEVANT NOTES FROM OBSIDIAN VAULT ===
    91→            {rag_text}
    92→
    93→            === TASK ===
    94→            Topic: {topic}
    95→            Extra Context: {extra_context}
    96→
    97→            Write the article in Chinese (unless the topic implies English).
    98→            Adhere strictly to the "Identity" and "Style Guide".
    99→            """
   100→
   101→            logger.info(f"Generating article for topic: {topic}")
   102→            actual_user_message = prompt # We inject the big prompt
   103→
   104→        else:
   105→            # === Follow-up Turn ===
   106→            # user_input is just instructions like "Make it shorter"
   107→            logger.info(f"Follow-up instruction: {user_input}")
   108→            actual_user_message = user_input
   109→            prompt = user_input
   110→
   111→        # Call LLM
   112→        response, _ = self.llm.generate_response(prompt, history)
   113→
   114→        # Update History
   115→        new_history = history.copy()
   116→        new_history.append({"role": "user", "content": actual_user_message})
   117→        new_history.append({"role": "assistant", "content": response})
   118→
   119→        return response, new_history
   120→
   121→class ReplyGenerator(BaseGenerator):
   122→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   123→
   124→        is_first_turn = (len(history) == 0)
   125→
   126→        if is_first_turn:
   127→             # === First Turn ===
   128→            static_samples = self._read_file("REPLY-SAMPLE.md")
   129→            dynamic_samples = self.indexer.get_reply_samples(count=3)
   130→            dynamic_samples_text = "\n\n".join(dynamic_samples)
   131→            methodology = self._read_file("methodology.md")
   132→
   133→            prompt = f"""You are an experienced Product/R&D Manager.
   134→            Your task is to draft a reply to a challenging situation.
   135→
   136→            === CORE PHILOSOPHY ===
   137→            {methodology}
   138→
   139→            === REPLY GUIDELINES ===
   140→            1. Logic: Be clear and structured (e.g., numbered lists).
   141→            2. Responsibility: Don't take unnecessary blame, but don't shirk core duties. Explain objective causes.
   142→            3. Emotion: Be empathetic but professional.
   143→            4. Conciseness: No fluff.
   144→
   145→            === REFERENCE SAMPLES (FEW-SHOT) ===
   146→            {static_samples}
   147→
   148→            {dynamic_samples_text}
   149→
   150→            === THE SITUATION ===
   151→            {user_input}
   152→
   153→            Draft a reply. If the context implies a specific role (PM, Dev Lead), assume that role.
   154→            """
   155→            logger.info(f"Generating reply for query: {user_input}")
   156→            actual_user_message = prompt
   157→
   158→        else:
   159→             # === Follow-up Turn ===
   160→            logger.info(f"Follow-up reply instruction: {user_input}")
   161→            actual_user_message = user_input
   162→            prompt = user_input
   163→
   164→        response, _ = self.llm.generate_response(prompt, history)
   165→
   166→        new_history = history.copy()
   167→        new_history.append({"role": "user", "content": actual_user_message})
   168→        new_history.append({"role": "assistant", "content": response})
   169→
   170→        return response, new_history
   171→
   172→class DecisionSupport(BaseGenerator):
   173→    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
   174→
   175→        is_first_turn = (len(history) == 0)
   176→
   177→        if is_first_turn:
   178→            decision_guide = self._read_file("decision.md")
   179→            methodology = self._read_file("methodology.md")
   180→
   181→            rag_notes = self.indexer.search(user_input, limit=3)
   182→            rag_text = "\n\n".join(rag_notes)
   183→
   184→            prompt = f"""You are a Decision Support Assistant.
   185→            Your goal is to help the user think through a complex decision.
   186→
   187→            === DECISION FRAMEWORK ===
   188→            {decision_guide}
   189→
   190→            === CORE PHILOSOPHY ===
   191→            {methodology}
   192→
   193→            === RELEVANT CONTEXT FROM NOTES ===
   194→            {rag_text}
   195→
   196→            === THE DECISION / ISSUE ===
   197→            {user_input}
   198→
   199→            === TASK ===
   200→            1. Challenge the premise: Is this the right problem to solve?

<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 List, Dict, Any, Callable
     2→from .health_read import HealthReader
     3→from .health_write import HealthWriter
     4→from .obsidian import ObsidianTool
     5→from .garmin import GarminTool
     6→from .garmin import GarminTool
     7→from .analytics import AnalyticsTool
     8→from .web import WebSearchTool
     9→from .shell_tool import execute_shell
    10→from .file_ops import download_file, save_file
    11→from .system_ops import get_system_status
    12→from .process_ops import kill_process, get_process_list
    13→
    14→# Tool Definitions
    15→TOOLS_SCHEMA = [
    16→    # --- Garmin & Metrics Read ---
    17→    {
    18→        "type": "function",
    19→        "function": {
    20→            "name": "get_daily_detailed_stats",
    21→            "description": "Get a comprehensive ONE-DAY SUMMARY (Metrics + Activity + Log Counts). Use for 'how was my day'. Do NOT use for filtering specific logs (use get_manual_history).",
    22→            "parameters": {
    23→                "type": "object",
    24→                "properties": {"target_date": {"type": "string", "description": "YYYY-MM-DD"}},
    25→                "required": ["target_date"]
    26→            }
    27→        }
    28→    },
    29→    {
    30→        "type": "function",
    31→        "function": {
    32→            "name": "get_metric_history",
    33→            "description": "**USE THIS when user asks about time-based trends**: 'past X days/weeks/months', 'recent changes', 'last year', 'how has X changed', 'X trend over time'. Returns analyzed trends with statistics for time ranges >7 days, raw data for ≤7 days. Supported metrics: rhr (resting heart rate), hrv, sleep, stress, steps, heart_rate, weight. Example: user asks 'past 2 months RHR' → call this with metric_type='rhr', start_date=60 days ago, end_date=today.",
    34→            "parameters": {
    35→                "type": "object",
    36→                "properties": {
    37→                    "metric_type": {"type": "string", "description": "Metric name: rhr, hrv, sleep, stress, steps, heart_rate, weight"},
    38→                    "start_date": {"type": "string", "description": "Start date YYYY-MM-DD (calculate from user's time range, e.g. 'past 2 months' = 60 days ago)"},
    39→                    "end_date": {"type": "string", "description": "End date YYYY-MM-DD (typically today)"}
    40→                },
    41→                "required": ["metric_type", "start_date", "end_date"]
    42→            }
    43→        }
    44→    },
    45→    {
    46→        "type": "function",
    47→        "function": {
    48→            "name": "get_activity_history",
    49→            "description": "Get list of workouts/activities in a date range.",
    50→            "parameters": {
    51→                "type": "object",
    52→                "properties": {
    53→                    "start_date": {"type": "string", "description": "YYYY-MM-DD"},
    54→                    "end_date": {"type": "string", "description": "YYYY-MM-DD"}
    55→                },
    56→                "required": ["start_date", "end_date"]
    57→            }
    58→        }
    59→    },
    60→    {
    61→        "type": "function",
    62→        "function": {
    63→            "name": "get_aggregated_analysis",
    64→            "description": "**REQUIRED for time-range questions**: When user asks 'past X days/weeks/months/years', 'recent trend', 'change over time', 'how has X changed', use THIS tool (not get_metric_history). Returns analyzed trends with statistics, yearly comparisons, and 30-day rolling averages. Examples: 'past 2 months RHR', 'HRV trend this year', 'sleep quality last 90 days'.",
    65→            "parameters": {
    66→                "type": "object",
    67→                "properties": {
    68→                    "start_date": {
    69→                        "type": "string",
    70→                        "description": "Start date YYYY-MM-DD (optional, defaults to 2 years ago)"
    71→                    },
    72→                    "end_date": {
    73→                        "type": "string",
    74→                        "description": "End date YYYY-MM-DD (optional, defaults to today)"
    75→                    },
    76→                    "metrics": {
    77→                        "type": "array",
    78→                        "items": {"type": "string"},
    79→                        "description": "Metrics to analyze: ['rhr', 'hrv', 'sleep', 'stress', 'steps']. If omitted, analyzes all."
    80→                    }
    81→                },
    82→                "required": []
    83→            }
    84→        }
    85→    },
    86→    # --- Manual Read ---
    87→    {
    88→        "type": "function",
    89→        "function": {
    90→            "name": "get_manual_history",
    91→            "description": "query manual logs (diet, alcohol, etc) over a range.",
    92→            "parameters": {
    93→                "type": "object",
    94→                "properties": {
    95→                    "start_date": {"type": "string", "description": "YYYY-MM-DD"},
    96→                    "end_date": {"type": "string", "description": "YYYY-MM-DD"},
    97→                    "category": {
    98→                        "type": "string",
    99→                        "enum": ["diet", "alcohol", "supplements", "feelings", "fasting", "all"],
   100→                        "description": "Category of logs to retrieve"
   101→                    }
   102→                },
   103→                "required": ["start_date", "end_date"]
   104→            }
   105→        }
   106→    },
   107→    # --- Manual Write (Operate) ---
   108→    {
   109→        "type": "function",
   110→        "function": {
   111→            "name": "log_diet",
   112→            "description": "Log food/meal.",
   113→            "parameters": {
   114→                "type": "object",
   115→                "properties": {
   116→                    "target_date": {"type": "string"},
   117→                    "description": {"type": "string"},
   118→                    "meal_type": {"type": "string", "enum": ["breakfast", "lunch", "dinner", "snack"]},
   119→                    "time": {"type": "string", "description": "HH:MM (optional)"}
   120→                },
   121→                "required": ["target_date", "description"]
   122→            }
   123→        }
   124→    },
   125→    {
   126→        "type": "function",
   127→        "function": {
   128→            "name": "log_alcohol",
   129→            "description": "Log alcohol.",
   130→            "parameters": {
   131→                "type": "object",
   132→                "properties": {
   133→                    "target_date": {"type": "string"},
   134→                    "alcohol_type": {"type": "string"},
   135→                    "amount": {"type": "string"},
   136→                    "time": {"type": "string", "description": "HH:MM (optional)"}
   137→                },
   138→                "required": ["target_date", "alcohol_type", "amount"]
   139→            }
   140→        }
   141→    },
   142→    {
   143→        "type": "function",
   144→        "function": {
   145→            "name": "log_supplement",
   146→            "description": "Log supplement.",
   147→            "parameters": {
   148→                "type": "object",
   149→                "properties": {
   150→                    "target_date": {"type": "string"},

<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>


import os
from typing import List, Dict, Tuple, Optional
from slack_bot.llm.gemini import GeminiLLM
from slack_bot.obsidian.indexer import ObsidianIndexer
from slack_bot.tools.web import WebSearchTool
from health.utils.logging_config import setup_logger
from health.utils.time_utils import get_current_time_str

logger = setup_logger(__name__)

OBSIDIAN_SYSTEM_PROMPT = """You are Butler (Obsidian Edition), an intelligent knowledge assistant connected to the user's second brain.
Current Time: {current_time}

Your Role:
You are NOT a health assistant. You are a Knowledge Partner designed to help the user think, write, and communicate. You draw directly from the user's local Obsidian notes, values, and methodology.

Core Capabilities:
1. Writing: You mimic the user's unique writing style (as defined in `writing_style.md`).
2. Communication: You draft high-EQ, logically rigorous replies for professional contexts (as defined in `REPLY-SAMPLE.md`).
3. Decision: You act as a "Devil's Advocate" and strategic advisor using the user's decision frameworks (GPA, IPO).

Guidelines:
- **Style Alignment**: Strictly adhere to the tone and sentence structures found in the provided samples.
- **Data Source**: Rely primarily on the provided context (RAG) and loaded markdown files.
- **Identity**: You are pragmatic, rational, and value "technological optimism" and "intellectual honesty".
"""

class BaseGenerator:
    def __init__(self, indexer: ObsidianIndexer):
        formatted_prompt = OBSIDIAN_SYSTEM_PROMPT.format(current_time=get_current_time_str())
        self.llm = GeminiLLM(system_instruction=formatted_prompt)
        self.indexer = indexer
        self.workspace_root = os.path.dirname(os.path.dirname(os.path.dirname(__file__)))

    def _read_file(self, filename: str) -> str:
        """Reads a file from the workspace root."""
        path = os.path.join(self.workspace_root, filename)
        try:
            with open(path, "r", encoding="utf-8") as f:
                return f.read()
        except Exception as e:
            logger.error(f"Failed to read {filename}: {e}")
            return ""

    def chat(self, user_input: str, history: List[Dict]) -> Tuple[str, List[Dict]]:
        """
        Processes chat input.
        If history is empty, treats input as a Topic/Query and builds a Rich Prompt.
        If history exists, treats input as follow-up instruction.


Now let me check for vector DB or embedding code and gather more info on how tools are structured:


find /root/projects/butler -type f -name "*.py" | xargs grep -l "embedding\|vector\|rag\|retriev" 2>/dev/null | head -10

Search for vector DB or embedding related code


     1→import json
     2→from datetime import date, datetime, timedelta
     3→from typing import Dict, Any, Optional, List
     4→from pathlib import Path
     5→
     6→from health import config
     7→from health.services.query import HealthDataQuery
     8→from health.services.manual_log_storage import ManualLogStorage, DailyManualLog
     9→from health.utils.logging_config import setup_logger
    10→
    11→logger = setup_logger(__name__)
    12→
    13→class HealthReader:
    14→    @staticmethod
    15→    def get_daily_detailed_stats(target_date: str = None, **kwargs) -> str:
    16→        """
    17→        Get all available health metrics and activities for a specific day.
    18→        Args:
    19→            target_date: ISO format date string (YYYY-MM-DD)
    20→            **kwargs: Catch-all for hallucinated arguments.
    21→        """
    22→        try:
    23→            # Handle aliases/hallucinations from LLM
    24→            if not target_date:
    25→                target_date = kwargs.get('start_date') or kwargs.get('date') or kwargs.get('day')
    26→
    27→            if not target_date:
    28→                return "Error: target_date (or start_date) is required."
    29→
    30→            d = date.fromisoformat(target_date)
    31→            query = HealthDataQuery()
    32→            data = query.get_daily_summary(d)
    33→
    34→            # Auto-sync if data is missing for recent days (today/yesterday)
    35→            # This mimics behavior of ./h.sh which usually syncs before showing
    36→            if not data.get("metrics") and (date.today() - d).days <= 1:
    37→                logger.info(f"Data missing for {target_date}, attempting auto-sync...")
    38→                try:
    39→                    from health.services.data_sync import HealthDataSync
    40→                    syncer = HealthDataSync()
    41→                    syncer.authenticate()
    42→                    syncer.sync_all_metrics(d, d, force=True)
    43→                    # Re-query after sync
    44→                    data = query.get_daily_summary(d)
    45→                    logger.info("Auto-sync completed, re-queried data.")
    46→                except Exception as sync_err:
    47→                    logger.error(f"Auto-sync failed: {sync_err}")
    48→                    # Continue with empty data, user will see "No data"
    49→
    50→
    51→            # Format summarized output to save context window
    52→            lines = [f"📊 **Health Report for {target_date}**"]
    53→
    54→            # Metrics
    55→            if data.get("metrics"):
    56→                lines.append("\n**Metrics:**")
    57→                for key, val in data["metrics"].items():
    58→                    # Flatten simple dicts for readability
    59→                    if isinstance(val, dict):
    60→                        # Extract key high-level stats if possible, otherwise stringify
    61→                        summary_str = ", ".join([f"{k}={v}" for k, v in val.items() if isinstance(v, (int, float, str))])
    62→                        if len(summary_str) > 200:
    63→                            summary_str = summary_str[:200] + "..."
    64→                        lines.append(f"- {key}: {summary_str}")
    65→                    else:
    66→                        lines.append(f"- {key}: {val}")
    67→
    68→            # Activities
    69→            if data.get("activities"):
    70→                lines.append("\n**Activities:**")
    71→                for act in data["activities"]:
    72→                    lines.append(f"- {act.get('activity_type', 'Activity')}: {act.get('duration_seconds', 0)//60} mins, {act.get('calories', 0)} kcal, Avg HR {act.get('average_heart_rate', 'N/A')}")
    73→
    74→            # Manual Logs
    75→            manual = ManualLogStorage().load_log(d)
    76→            if manual.diet_entries:
    77→                lines.append(f"\n**Diet:** {len(manual.diet_entries)} entries")
    78→            if manual.alcohol_entries:
    79→                lines.append(f"**Alcohol:** {len(manual.alcohol_entries)} entries")
    80→            if manual.fasting_mode:
    81→                lines.append(f"**Fasting:** {manual.fasting_mode}")
    82→            if manual.feeling_entries:
    83→                lines.append(f"**Feelings:** {len(manual.feeling_entries)} entries")
    84→
    85→            return "\n".join(lines) if len(lines) > 1 else f"No data found for {target_date}."
    86→
    87→        except Exception as e:
    88→            logger.error(f"Error getting detailed stats: {e}")
    89→            return f"Error retrieving data: {str(e)}"
    90→
    91→    @staticmethod
    92→    def get_metric_history(metric_type: str = None, start_date: str = None, end_date: str = None, **kwargs) -> str:
    93→        """
    94→        Get historical data for a specific metric (e.g., steps, sleep_score, heart_rate, hrv, weight).
    95→
    96→        For time ranges > 7 days, automatically uses aggregated analysis for better insights.
    97→        """
    98→        try:
    99→            # Handle alias: target_date -> start/end
   100→            if 'target_date' in kwargs and not start_date:

<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 datetime import date, datetime
     2→from typing import Optional
     3→from health.services.manual_log_storage import ManualLogStorage
     4→from health.models.manual_log import (
     5→    DietEntry,
     6→    AlcoholEntry,
     7→    SupplementEntry,
     8→    BodyFeelingEntry
     9→)
    10→from health.utils.logging_config import setup_logger
    11→
    12→logger = setup_logger(__name__)
    13→
    14→class HealthWriter:
    15→    @staticmethod
    16→    def log_diet(
    17→        target_date: str,
    18→        description: str,
    19→        time: Optional[str] = None,
    20→        meal_type: str = "snack"
    21→    ) -> str:
    22→        """Log a diet entry."""
    23→        try:
    24→            date_obj = date.fromisoformat(target_date)
    25→            time_str = time or datetime.now().strftime("%H:%M")
    26→
    27→            entry = DietEntry(
    28→                time=time_str,
    29→                description=description,
    30→                meal_type=meal_type
    31→            )
    32→
    33→            storage = ManualLogStorage()
    34→            storage.add_diet_entry(date_obj, entry)
    35→            return f"✅ Logged diet for {target_date}: {description}"
    36→        except Exception as e:
    37→            logger.error(f"Failed to log diet: {e}")
    38→            return f"❌ Error logging diet: {str(e)}"
    39→
    40→    @staticmethod
    41→    def log_alcohol(
    42→        target_date: str,
    43→        alcohol_type: str,
    44→        amount: str,
    45→        time: Optional[str] = None
    46→    ) -> str:
    47→        """Log alcohol consumption."""
    48→        try:
    49→            date_obj = date.fromisoformat(target_date)
    50→            time_str = time or datetime.now().strftime("%H:%M")
    51→
    52→            entry = AlcoholEntry(
    53→                time=time_str,
    54→                alcohol_type=alcohol_type,
    55→                amount=amount
    56→            )
    57→
    58→            storage = ManualLogStorage()
    59→            storage.add_alcohol_entry(date_obj, entry)
    60→            return f"✅ Logged alcohol for {target_date}: {amount} {alcohol_type}"
    61→        except Exception as e:
    62→            logger.error(f"Failed to log alcohol: {e}")
    63→            return f"❌ Error logging alcohol: {str(e)}"
    64→
    65→    @staticmethod
    66→    def log_supplement(
    67→        target_date: str,
    68→        name: str,
    69→        dosage: str,
    70→        time: Optional[str] = None
    71→    ) -> str:
    72→        """Log supplement."""
    73→        try:
    74→            date_obj = date.fromisoformat(target_date)
    75→            time_str = time or datetime.now().strftime("%H:%M")
    76→
    77→            entry = SupplementEntry(
    78→                time=time_str,
    79→                supplement_name=name,
    80→                dosage=dosage
    81→            )
    82→
    83→            storage = ManualLogStorage()
    84→            storage.add_supplement_entry(date_obj, entry)
    85→            return f"✅ Logged supplement for {target_date}: {name} ({dosage})"
    86→        except Exception as e:
    87→            logger.error(f"Failed to log supplement: {e}")
    88→            return f"❌ Error logging supplement: {str(e)}"
    89→
    90→    @staticmethod
    91→    def log_feeling(
    92→        target_date: str,
    93→        feeling_type: str,
    94→        severity: int,
    95→        description: str,
    96→        time: Optional[str] = None
    97→    ) -> str:
    98→        """Log body feeling/symptom."""
    99→        try:
   100→            date_obj = date.fromisoformat(target_date)
   101→            time_str = time or datetime.now().strftime("%H:%M")
   102→
   103→            entry = BodyFeelingEntry(
   104→                time=time_str,
   105→                feeling_type=feeling_type,
   106→                severity=severity,
   107→                description=description
   108→            )
   109→
   110→            storage = ManualLogStorage()
   111→            storage.add_feeling_entry(date_obj, entry)
   112→            return f"✅ Logged feeling for {target_date}: {feeling_type} (Level {severity})"
   113→        except Exception as e:
   114→            logger.error(f"Failed to log feeling: {e}")
   115→            return f"❌ Error logging feeling: {str(e)}"
   116→
   117→    @staticmethod
   118→    def log_fasting(
   119→        target_date: str,
   120→        mode: str,
   121→        notes: Optional[str] = None
   122→    ) -> str:
   123→        """Log fasting mode."""
   124→        try:
   125→            date_obj = date.fromisoformat(target_date)
   126→            storage = ManualLogStorage()
   127→            # Note: The underlying storage method set_fasting_mode only takes date and mode
   128→            # If notes were important, we'd need to update the model.
   129→            # For now we'll just log the mode.
   130→            storage.set_fasting_mode(date_obj, mode)
   131→            return f"✅ Set fasting mode for {target_date}: {mode}"
   132→        except Exception as e:
   133→            logger.error(f"Failed to set fasting mode: {e}")
   134→            return f"❌ Error setting fasting mode: {str(e)}"
   135→

<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>


/root/projects/butler/experiments/dispatcher_clean.py
/root/projects/butler/health/analytics/engine.py
/root/projects/butler/health/analytics/weekly_report.py
/root/projects/butler/health/cli/manual.py
/root/projects/butler/health/cli/query.py
/root/projects/butler/health/models/activity.py
/root/projects/butler/health/models/daily_metrics.py
/root/projects/butler/health/services/data_sync.py
/root/projects/butler/health/services/garmin_client.py
/root/projects/butler/health/services/manual_log_storage.py


Let me check project structure files and the main entry points more:


ls -la /root/projects/butler/*.py /root/projects/butler/*.md 2>/dev/null | head -20

List root-level files


find /root/projects/butler -maxdepth 1 -type f \( -name "*.py" -o -name "*.md" -o -name "*.sh" -o -name ".env*" \) 2>/dev/null

Find config and entry point files at root


ls -la /root/projects/butler/scripts/ | head -15

List scripts directory


-rw-r--r-- 1 root root  24803 Mar  2 10:58 /root/projects/butler/BOT_FEATURES_REFERENCE.md
-rw-r--r-- 1  501 games  2812 Feb  2 10:56 /root/projects/butler/BOT_FIX_SUMMARY.md
-rw-r--r-- 1  501 games  5114 Jan 22 11:30 /root/projects/butler/Butler-Bot使用指南.md
-rw-r--r-- 1  501 games  1236 Jan 19 13:35 /root/projects/butler/CLAUDE.md
-rw-r--r-- 1 root root   2950 Mar  2 10:57 /root/projects/butler/CONFIGURATION_SUMMARY.md
-rw-r--r-- 1  501 games  4596 Jan 19 14:04 /root/projects/butler/decision.md
-rw-r--r-- 1  501 games  6743 Jan 22 14:49 /root/projects/butler/distilled_insights.md
-rw-r--r-- 1  501 games  4188 Jan 19 13:46 /root/projects/butler/health.md
-rw-r--r-- 1  501 games   481 Jan 22 14:46 /root/projects/butler/list_samples.py
-rw-r--r-- 1  501 games   473 Feb  2 10:25 /root/projects/butler/m1.py
-rw-r--r-- 1  501 games   473 Feb  2 10:25 /root/projects/butler/m2.py
-rw-r--r-- 1  501 games  5361 Jan 20 14:49 /root/projects/butler/mannul-data-input.md
-rw-r--r-- 1 root root   3044 Mar  2 14:32 /root/projects/butler/methodology.md
-rw-r--r-- 1 root root  12859 Mar  2 14:29 /root/projects/butler/POWELL_REVISE.md
-rw-r--r-- 1  501 games  1926 Jan 20 18:32 /root/projects/butler/quick_test.py
-rw-r--r-- 1 root root  32992 Mar  2 10:57 /root/projects/butler/README.md
-rw-r--r-- 1  501 games  3830 Jan 22 13:26 /root/projects/butler/REPLY-SAMPLE.md
-rw-r--r-- 1  501 games  5700 Jan 20 18:06 /root/projects/butler/SLACK_BOT_OPTIMIZATION.md
-rw-r--r-- 1  501 games  2450 Jan 20 18:04 /root/projects/butler/smoke_test.py
-rw-r--r-- 1 root root   3077 Mar  2 14:27 /root/projects/butler/writing_style.md


total 256
drwxr-xr-x  3  501 games  4096 Mar  2 15:45 .
drwxr-xr-x 20 root root   4096 Mar  2 17:44 ..
-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


/root/projects/butler/BOT_FEATURES_REFERENCE.md
/root/projects/butler/BOT_FIX_SUMMARY.md
/root/projects/butler/CLAUDE.md
/root/projects/butler/CONFIGURATION_SUMMARY.md
/root/projects/butler/decision.md
/root/projects/butler/distilled_insights.md
/root/projects/butler/health.md
/root/projects/butler/h.sh
/root/projects/butler/list_samples.py
/root/projects/butler/m1.py
/root/projects/butler/m2.py
/root/projects/butler/mannul-data-input.md
/root/projects/butler/methodology.md
/root/projects/butler/monitor-current-claude-endpoint.sh
/root/projects/butler/POWELL_REVISE.md
/root/projects/butler/quick_test.py
/root/projects/butler/README.md
/root/projects/butler/REPLY-SAMPLE.md
/root/projects/butler/SLACK_BOT_OPTIMIZATION.md
/root/projects/butler/smoke_test.py
/root/projects/butler/writing_style.md
/root/projects/butler/zhihu-style.md
/root/projects/butler/.env.backup
/root/projects/butler/.env.backup.20260120_183006
/root/projects/butler/.env.example
/root/projects/butler/.env.gemini.example
/root/projects/butler/.env.updated
/root/projects/butler/Butler-Bot使用指南.md
/root/projects/butler/.env


Now let me look at the structure of the tools and the config:


Get line counts for tool files


grep -r "MODE_" /root/projects/butler/slack_bot --include="*.py" | grep -v "^Binary" | head -20

Search for MODE constants


     1→"""
     2→Tool groups for dynamic tool selection.
     3→
     4→When using proxies with tool limitations, we can selectively load only relevant tools.
     5→"""
     6→
     7→from typing import List, Dict, Any
     8→from .registry import TOOLS_SCHEMA, TOOL_FUNCTIONS
     9→
    10→
    11→# === PRESET: Light Mode (6 core tools for proxy compatibility) ===
    12→LIGHT_TOOLS_NAMES = [
    13→    "get_daily_detailed_stats",
    14→    "get_metric_history",
    15→    "log_diet",
    16→    "log_supplement",
    17→    "sync_garmin",
    18→    "search_web",  # Added for web search functionality
    19→]
    20→
    21→LIGHT_TOOLS = [t for t in TOOLS_SCHEMA if t["function"]["name"] in LIGHT_TOOLS_NAMES]
    22→LIGHT_FUNCTIONS = {k: v for k, v in TOOL_FUNCTIONS.items() if k in LIGHT_TOOLS_NAMES}
    23→
    24→
    25→# === PRESET: Standard Mode (10 tools) ===
    26→STANDARD_TOOLS_NAMES = LIGHT_TOOLS_NAMES + [
    27→    "get_activity_history",
    28→    "get_manual_history",
    29→    "log_alcohol",
    30→    "log_feeling",
    31→    "get_health_insights",
    32→]
    33→
    34→STANDARD_TOOLS = [t for t in TOOLS_SCHEMA if t["function"]["name"] in STANDARD_TOOLS_NAMES]
    35→STANDARD_FUNCTIONS = {k: v for k, v in TOOL_FUNCTIONS.items() if k in STANDARD_TOOLS_NAMES}
    36→
    37→
    38→# === PRESET: Full Mode (15 tools) ===
    39→FULL_TOOLS_NAMES = STANDARD_TOOLS_NAMES + [
    40→    "analyze_driver",
    41→    "search_web",
    42→    "sync_obsidian",
    43→    "log_fasting",
    44→    "get_aggregated_analysis",
    45→]
    46→
    47→FULL_TOOLS = [t for t in TOOLS_SCHEMA if t["function"]["name"] in FULL_TOOLS_NAMES]
    48→FULL_FUNCTIONS = {k: v for k, v in TOOL_FUNCTIONS.items() if k in FULL_TOOLS_NAMES}
    49→
    50→
    51→def get_tool_preset(mode: str = "light") -> tuple[List[Dict[str, Any]], Dict]:
    52→    """
    53→    Get tool preset by mode.
    54→
    55→    Args:
    56→        mode: 'light' (5 tools), 'standard' (10 tools), 'full' (15 tools), 'all' (21 tools)
    57→
    58→    Returns:
    59→        (tools_schema, tool_functions)
    60→    """
    61→    if mode == "light":
    62→        return LIGHT_TOOLS, LIGHT_FUNCTIONS
    63→    elif mode == "standard":
    64→        return STANDARD_TOOLS, STANDARD_FUNCTIONS
    65→    elif mode == "full":
    66→        return FULL_TOOLS, FULL_FUNCTIONS
    67→    elif mode == "all":
    68→        return TOOLS_SCHEMA, TOOL_FUNCTIONS
    69→    else:
    70→        raise ValueError(f"Unknown mode: {mode}. Use 'light', 'standard', 'full', or 'all'")
    71→

<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>


   74 /root/projects/butler/slack_bot/tools/analytics.py
   78 /root/projects/butler/slack_bot/tools/file_ops.py
   44 /root/projects/butler/slack_bot/tools/garmin.py
   70 /root/projects/butler/slack_bot/tools/groups.py
 1013 /root/projects/butler/slack_bot/tools/health_read.py
  134 /root/projects/butler/slack_bot/tools/health_write.py
   22 /root/projects/butler/slack_bot/tools/obsidian.py
   91 /root/projects/butler/slack_bot/tools/process_ops.py
  412 /root/projects/butler/slack_bot/tools/registry.py
   33 /root/projects/butler/slack_bot/tools/shell_tool.py
   82 /root/projects/butler/slack_bot/tools/system_ops.py
   44 /root/projects/butler/slack_bot/tools/web.py
 2097 total


/root/projects/butler/slack_bot/obsidian/cli.py:    MODE_NONE = "none"
/root/projects/butler/slack_bot/obsidian/cli.py:    MODE_WRITE = "write"
/root/projects/butler/slack_bot/obsidian/cli.py:    MODE_REPLY = "reply"
/root/projects/butler/slack_bot/obsidian/cli.py:    MODE_DECIDE = "decide"
/root/projects/butler/slack_bot/obsidian/cli.py:    MODE_SEARCH = "search"
/root/projects/butler/slack_bot/obsidian/cli.py:            self.MODE_WRITE: WritingAssistant(self.indexer),
/root/projects/butler/slack_bot/obsidian/cli.py:            self.MODE_REPLY: ReplyGenerator(self.indexer),
/root/projects/butler/slack_bot/obsidian/cli.py:            self.MODE_DECIDE: DecisionSupport(self.indexer),
/root/projects/butler/slack_bot/obsidian/cli.py:            self.MODE_SEARCH: SearchAnalyzer(self.indexer)
/root/projects/butler/slack_bot/obsidian/cli.py:        self.current_mode = self.MODE_NONE
/root/projects/butler/slack_bot/obsidian/cli.py:        if self.current_mode == self.MODE_NONE:
/root/projects/butler/slack_bot/obsidian/cli.py:        if arg in [self.MODE_WRITE, self.MODE_REPLY, self.MODE_DECIDE, self.MODE_SEARCH]:
/root/projects/butler/slack_bot/obsidian/cli.py:            print(f"Invalid mode. Available modes: {self.MODE_WRITE}, {self.MODE_REPLY}, {self.MODE_DECIDE}, {self.MODE_SEARCH}")
/root/projects/butler/slack_bot/obsidian/cli.py:            self.current_mode = self.MODE_NONE
/root/projects/butler/slack_bot/obsidian/cli.py:        if self.current_mode == self.MODE_NONE:
/root/projects/butler/slack_bot/obsidian/cli.py:        if self.current_mode == self.MODE_WRITE:
/root/projects/butler/slack_bot/obsidian/cli.py:        elif self.current_mode == self.MODE_REPLY:
/root/projects/butler/slack_bot/obsidian/cli.py:        elif self.current_mode == self.MODE_DECIDE:
/root/projects/butler/slack_bot/obsidian/cli.py:        elif self.current_mode == self.MODE_SEARCH:
/root/projects/butler/slack_bot/obsidian/cli.py:        if self.current_mode == self.MODE_NONE:


Let me get more info on the context storage and health data models:


find /root/projects/butler/health -type f -name "*.py" | xargs wc -l | tail -1

Count total lines in health module


     1→import json
     2→import time
     3→from pathlib import Path
     4→from typing import List, Dict, Any, Optional
     5→from health.utils.logging_config import setup_logger
     6→from health import config
     7→
     8→logger = setup_logger(__name__)
     9→
    10→CONTEXT_DIR = config.DATA_DIR / "slack_context"
    11→CONTEXT_DIR.mkdir(parents=True, exist_ok=True)
    12→
    13→class ContextStorage:
    14→    """Manages persistent conversation history for Slack channels."""
    15→
    16→    # Maximum messages to keep (simple sliding window)
    17→    MAX_MESSAGES = 40
    18→
    19→    def __init__(self, channel_id: str):
    20→        self.channel_id = channel_id
    21→        self.file_path = CONTEXT_DIR / f"{channel_id}.json"
    22→
    23→    def _load(self) -> List[Dict[str, Any]]:
    24→        if not self.file_path.exists():
    25→            return []
    26→        try:
    27→            with open(self.file_path, "r", encoding="utf-8") as f:
    28→                return json.load(f)
    29→        except Exception as e:
    30→            logger.error(f"Failed to load context for {self.channel_id}: {e}")
    31→            return []
    32→
    33→    def _save(self, context: List[Dict[str, Any]]) -> None:
    34→        try:
    35→            with open(self.file_path, "w", encoding="utf-8") as f:
    36→                json.dump(context, f, ensure_ascii=False, indent=2)
    37→        except Exception as e:
    38→            logger.error(f"Failed to save context for {self.channel_id}: {e}")
    39→
    40→    def add_message(self, role: str, content: str, model: Optional[str] = None) -> None:
    41→        """
    42→        Add a message to the context.
    43→
    44→        Args:
    45→            role: 'user' or 'assistant'
    46→            content: Message content
    47→            model: Model identifier (only for assistant messages, currently always 'gemini')
    48→        """
    49→        context = self._load()
    50→        msg = {
    51→            "role": role,
    52→            "content": content,
    53→            "timestamp": time.time()
    54→        }
    55→        if model:
    56→            msg["model"] = model
    57→
    58→        context.append(msg)
    59→
    60→        # Apply sliding window compression
    61→        if len(context) > self.MAX_MESSAGES:
    62→            context = context[-self.MAX_MESSAGES:]
    63→
    64→        self._save(context)
    65→
    66→    def get_context(self) -> List[Dict[str, Any]]:
    67→        """
    68→        Get conversation context in standard format.
    69→
    70→        Returns messages as [{"role": "user", "content": "..."}, ...]
    71→        """
    72→        full_context = self._load()
    73→
    74→        # Convert to standard format (remove timestamp and model metadata)
    75→        filtered = []
    76→        for msg in full_context:
    77→            filtered.append({
    78→                "role": msg["role"],
    79→                "content": msg["content"]
    80→            })
    81→
    82→        return filtered
    83→
    84→    def clear(self) -> None:
    85→        """Clear all context for this channel."""
    86→        if self.file_path.exists():
    87→            self.file_path.unlink()
    88→        logger.info(f"Cleared context for {self.channel_id}")
    89→

<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 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(

<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>


5579 total


Perfect! Now let me create a comprehensive summary document. Let me gather a few more key pieces:


head -100 /root/projects/butler/slack_bot/tools/health_read.py | tail -50

Get more of health_read to see analyzer patterns


     1→# 🤖 Butler - 智能个人助理系统
     2→
     3→> 一个企业级质量的模块化个人助理系统，专注于数据驱动的健康追踪、生物黑客实验和桌面自动化
     4→
     5→[![Python](https://img.shields.io/badge/Python-3.12-blue.svg)](https://www.python.org/)
     6→[![Code Quality](https://img.shields.io/badge/Code%20Quality-Enterprise-green.svg)](docs/REVIEW_AND_ROADMAP.md)
     7→[![Architecture](https://img.shields.io/badge/Architecture-⭐⭐⭐⭐⭐-yellow.svg)](docs/ARCHITECTURE.md)
     8→[![License](https://img.shields.io/badge/License-Private-red.svg)](LICENSE)
     9→
    10→---
    11→
    12→## 📋 目录
    13→
    14→- [项目概览](#-项目概览)
    15→- [核心特性](#-核心特性)
    16→- [设计原则](#-设计原则)
    17→- [架构设计](#-架构设计)
    18→- [技术栈](#-技术栈)
    19→- [快速开始](#-快速开始)
    20→- [模块详解](#-模块详解)
    21→- [使用指南](#-使用指南)
    22→- [开发规范](#-开发规范)
    23→- [性能指标](#-性能指标)
    24→- [未来规划](#-未来规划)
    25→- [贡献指南](#-贡献指南)
    26→
    27→---
    28→
    29→## 🎯 项目概览
    30→
    31→Butler 是一个为生物黑客和数据驱动决策者设计的**个人 AI 助理系统**。它将健康数据管理、智能分析和桌面自动化无缝集成，通过 Slack Bot 提供自然语言交互界面，让你可以像与私人助理对话一样管理个人数据。
    32→
    33→### 为什么选择 Butler?
    34→
    35→- 🏥 **全面的健康数据管理**: 自动同步 Garmin 设备数据，涵盖 14+ 种健康指标
    36→- 🤖 **智能 AI 助理**: 基于 Gemini 2.0 的 Slack Bot，支持自然语言查询和复杂工具调用
    37→- 📊 **高级数据分析**: Pandas 驱动的相关性分析、趋势预测和驱动因素识别
    38→- 🔒 **隐私优先**: 所有数据本地存储，无云依赖，完全掌控你的数据
    39→- 🛠 **高度可扩展**: 模块化架构，轻松添加新功能和数据源
    40→- 💻 **桌面自动化**: 持久化 Shell 会话，执行系统任务和脚本
    41→
    42→### 项目统计
    43→
    44→```
    45→核心代码:      ~10,600 行 Python
    46→模块数量:      3 个主要模块 (Health, Obsidian, Shell)
    47→工具数量:      14 个 AI 可调用工具
    48→健康指标:      14+ 种 (睡眠、心率、HRV、压力等)
    49→数据存储:      104 MB (SQLite + JSON)
    50→代码质量:      ⭐⭐⭐⭐⭐ (5/5)
    51→总体评分:      ⭐⭐⭐⭐☆ (4.1/5)
    52→```
    53→
    54→---
    55→
    56→## ✨ 核心特性
    57→
    58→### 1. 🏥 健康数据管理
    59→
    60→#### Garmin 自动同步
    61→- **增量同步**: 智能检测最后同步日期，只同步新数据
    62→- **批处理**: 历史数据回填，避免 API 限流
    63→- **14+ 健康指标**:
    64→  - 睡眠 (总睡眠、深睡、REM、睡眠评分)
    65→  - 心率 (静息心率、最大/最小/平均心率、心率区间)
    66→  - HRV (心率变异性、基线、状态)
    67→  - 压力 (平均/最大压力、时间分布)
    68→  - Body Battery (充电/消耗、最高/最低值)
    69→  - 步数、距离、卡路里、楼层
    70→  - 血氧饱和度 (SpO2)
    71→  - 呼吸率、水合作用
    72→  - 运动活动 (类型、时长、距离、心率、配速)
    73→
    74→#### 手动记录系统
    75→- **饮食日志**: 餐食类型、时间、描述、卡路里
    76→- **补剂追踪**: 名称、剂量、时间
    77→- **酒精记录**: 类型、数量、时间
    78→- **感受日志**: 症状类型、严重程度、描述
    79→- **禁食模式**: OMAD、PSMF 等饮食方案追踪
    80→
    81→#### 高级分析引擎
    82→```python
    83→# 相关性分析: 酒精对睡眠的影响
    84→analyze_driver(
    85→    driver_metric="alcohol_units",
    86→    target_metric="sleep_score",
    87→    method="correlation"
    88→)
    89→
    90→# 趋势分析: HRV 和静息心率趋势
    91→get_health_insights(
    92→    analysis_type="fitness_trends",
    93→    lookback_days=90
    94→)
    95→
    96→# 生活方式影响: 禁食对 Body Battery 的影响
    97→get_health_insights(
    98→    analysis_type="lifestyle_impact",
    99→    lookback_days=60
   100→)

<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>


            # Format summarized output to save context window
            lines = [f"📊 **Health Report for {target_date}**"]

            # Metrics
            if data.get("metrics"):
                lines.append("\n**Metrics:**")
                for key, val in data["metrics"].items():
                    # Flatten simple dicts for readability
                    if isinstance(val, dict):
                        # Extract key high-level stats if possible, otherwise stringify
                        summary_str = ", ".join([f"{k}={v}" for k, v in val.items() if isinstance(v, (int, float, str))])
                        if len(summary_str) > 200:
                            summary_str = summary_str[:200] + "..."
                        lines.append(f"- {key}: {summary_str}")
                    else:
                        lines.append(f"- {key}: {val}")

            # Activities
            if data.get("activities"):
                lines.append("\n**Activities:**")
                for act in data["activities"]:
                    lines.append(f"- {act.get('activity_type', 'Activity')}: {act.get('duration_seconds', 0)//60} mins, {act.get('calories', 0)} kcal, Avg HR {act.get('average_heart_rate', 'N/A')}")

            # Manual Logs
            manual = ManualLogStorage().load_log(d)
            if manual.diet_entries:
                lines.append(f"\n**Diet:** {len(manual.diet_entries)} entries")
            if manual.alcohol_entries:
                lines.append(f"**Alcohol:** {len(manual.alcohol_entries)} entries")
            if manual.fasting_mode:
                lines.append(f"**Fasting:** {manual.fasting_mode}")
            if manual.feeling_entries:
                lines.append(f"**Feelings:** {len(manual.feeling_entries)} entries")

            return "\n".join(lines) if len(lines) > 1 else f"No data found for {target_date}."

        except Exception as e:
            logger.error(f"Error getting detailed stats: {e}")
            return f"Error retrieving data: {str(e)}"

    @staticmethod
    def get_metric_history(metric_type: str = None, start_date: str = None, end_date: str = None, **kwargs) -> str:
        """
        Get historical data for a specific metric (e.g., steps, sleep_score, heart_rate, hrv, weight).

        For time ranges > 7 days, automatically uses aggregated analysis for better insights.
        """
        try:
            # Handle alias: target_date -> start/end
            if 'target_date' in kwargs and not start_date:


Perfect! Now I have a comprehensive understanding of the codebase. Let me compile my findings into a detailed report.

## Butler Codebase Exploration Report

I've completed a thorough exploration of the Butler project. Here's a comprehensive overview:

---

### **1. OVERALL DIRECTORY STRUCTURE**

```
/root/projects/butler/
├── health/                      # Core health data management (5,579 lines Python)
│   ├── analytics/               # Data analysis & weekly reports
│   ├── cli/                     # Command-line interfaces
│   ├── db/                      # SQLite database schema & repository
│   ├── models/                  # Pydantic models for health data
│   ├── services/                # Garmin API client, data sync, storage
│   └── utils/                   # Logging, date utilities, env loader
├── slack_bot/                   # Slack bot implementations
│   ├── main.py                  # Health Bot entry point
│   ├── dispatcher.py            # Message routing & LLM dispatch (~590 lines)
│   ├── context/                 # Conversation history storage
│   ├── llm/                     # LLM implementations (Gemini via OpenAI-compatible API)
│   ├── obsidian/                # Obsidian knowledge assistant
│   │   ├── dispatcher.py        # Mode routing (write/reply/decide/search/deai/zhihu)
│   │   ├── indexer.py           # Vault scanner & RAG search (~188 lines)
│   │   ├── generators.py        # Mode-specific generators
│   │   └── cli.py               # CLI interface
│   ├── shell/                   # Shell command execution (disabled by default)
│   ├── tools/                   # LLM-callable tools (~2,097 lines)
│   │   ├── registry.py          # Tool schema definitions (~412 lines)
│   │   ├── groups.py            # Tool presets (light/standard/full/all)
│   │   ├── health_read.py       # Health data query tools (~1,013 lines)
│   │   ├── health_write.py      # Health data logging tools
│   │   ├── analytics.py         # Statistical analysis tools
│   │   ├── web.py               # Web search tool
│   │   └── others...            # File ops, process, system ops
│   └── utils/                   # Slack formatting utilities
├── scripts/                     # Utility scripts & bot management
├── tests/                       # Unit & integration tests
├── data/                        # Health data storage (SQLite + JSON)
├── docs/                        # Documentation & milestones
└── Configuration files          # .env, .env.backup, .env.example, etc.
```

---

### **2. OBSIDIAN STRUCTURE & INTEGRATION**

The Obsidian system is located in `/root/projects/butler/slack_bot/obsidian/` and supports a **knowledge assistant** with 6 modes:

#### **Mode Implementations** (in `dispatcher.py` & `generators.py`):

| Mode | Purpose | Key Features |
|------|---------|--------------|
| **MODE_WRITE** | Article writing | RAG search, writing samples, style adherence |
| **MODE_REPLY** | Professional replies | Few-shot learning, EQ-aware responses |
| **MODE_DECIDE** | Decision support | Devil's advocate, framework-based analysis |
| **MODE_SEARCH** | Vault search | Query expansion, ranking by relevance |
| **MODE_DEAI** | AI removal/revision | De-AI content for authenticity |
| **MODE_ZHIHU** | Zhihu article format | Platform-specific writing style |

#### **Key Components**:
- **ObsidianIndexer** (`indexer.py`): Scans vault for markdown files, extracts samples tagged with `#writing_sample` or `#reply_sample`
- **BaseGenerator**: Abstract parent for all modes
- **Generators**: WritingAssistant, ReplyGenerator, DecisionSupport, SearchAnalyzer, DeAIReviser, ZhihuGenerator
- **RAG Strategy**: Keyword-based search with cubic weighting for multi-token queries
- **File Sources**: `writing_style.md`, `methodology.md`, `decision.md`, `REPLY-SAMPLE.md`, `zhihu-style.md`

#### **Obsidian File Structure**:
```
/root/projects/butler/slack_bot/obsidian/
├── dispatcher.py        # Routes messages to generators by mode
├── indexer.py           # Scans Obsidian vault (uses #tags for classification)
├── generators.py        # ~250 lines defining all 6 mode implementations
└── cli.py               # CLI version of Obsidian bot
```

**Data paths**:
- Context: `/root/projects/butler/data/obsidian_context/`
- State tracking: `{channel_id}_state.json` (stores current mode)

---

### **3. HEALTH BOT MODE IMPLEMENTATIONS**

The Health Bot uses **tool-based modes** rather than stateful modes. The main dispatcher routes based on **tool presets**:

#### **Tool Presets** (in `tools/groups.py`):

| Mode | Tools | Use Case |
|------|-------|----------|
| **light** | 6 tools | Basic health queries, compatibility with restrictive proxies |
| **standard** | 10 tools | Full health analysis + manual logging |
| **full** | 15 tools | Including Obsidian sync, fasting logs, aggregated analysis |
| **all** | 21+ tools | Complete feature set (all available tools) |

#### **Key Tools Structure** (in `tools/registry.py` & individual tool files):

**Read Tools** (`health_read.py` - 1,013 lines):
- `get_daily_detailed_stats(target_date)` - Comprehensive daily summary
- `get_metric_history(metric_type, start_date, end_date)` - Time-range trends
- `get_activity_history(start_date, end_date)` - Workout/activity logs
- `get_aggregated_analysis(start_date, end_date, metrics)` - Advanced analytics with rolling averages
- `get_manual_history(start_date, end_date, category)` - Diet/alcohol/supplement logs
- `get_health_insights(analysis_type, lookback_days)` - Specialized insights
- `analyze_driver(driver_metric, target_metric, method)` - Correlation analysis

**Write Tools** (`health_write.py` - 134 lines):
- `log_diet(target_date, description, meal_type, time)`
- `log_alcohol(target_date, alcohol_type, amount, time)`
- `log_supplement(target_date, name, dosage, time)`
- `log_feeling(target_date, feeling_type, severity, description, time)`
- `log_fasting(target_date, mode, notes)`

**System Tools**:
- `sync_garmin(target_date)` - Sync health data from Garmin
- `search_web(query)` - Real-time web search
- `get_health_insights(analysis_type, lookback_days)` - Statistical analysis
- `analyze_driver(driver_metric, target_metric, method)` - Causation detection

---

### **4. EXISTING MODE PATTERNS**

The codebase implements **two distinct mode systems**:

#### **Pattern 1: Obsidian Modes (Stateful)**
```python
# In obsidian/dispatcher.py
class ObsidianDispatcher:
    MODE_WRITE = "write"
    MODE_REPLY = "reply"
    MODE_DECIDE = "decide"
    MODE_SEARCH = "search"
    MODE_DEAI = "deai"
    MODE_ZHIHU = "zhihu"

    def dispatch(self, text, channel_id, user_id, response_ts):
        # Load state from storage
        state = storage.load_state()
        current_mode = state.get("mode", "none")

        # Route to appropriate generator
        generator = self.generators[current_mode]
        response, updated_history = generator.chat(text, history)
```

**Characteristics**:
- Persistent state stored per channel in JSON
- Mode switching via `mode <name>` command
- History cleared when switching modes
- First turn vs. follow-up logic (rebuilds prompt on first turn only)

#### **Pattern 2: Health Bot Tool Modes (Preset-based)**
```python
# In dispatcher.py
class MessageDispatcher:
    def __init__(self, tool_mode: str = "light"):
        # Get preset tools
        self.tools, self.tool_functions = get_tool_preset(tool_mode)
```

**Characteristics**:
- No persistent state (stateless)
- Tool set determined at initialization
- **Safety Override** mechanism: Keywords in message text can force specific tool calls (lines 117-257 in dispatcher.py)
- Two-pass approach: LLM call → tool execution → LLM analysis

---

### **5. HOW SLACK BOT IS STRUCTURED**

#### **Entry Point** (`slack_bot/main.py`):
```python
app = App(token=bot_token)
dispatcher = MessageDispatcher(tool_mode="light")  # or specified via .env

@app.event("message")
def handle_message(message, say):
    dispatcher.dispatch(text, channel, user_id, response_ts, request_id, files)
```

#### **Message Flow**:
1. **Slack Event** → `handle_message()` in main.py
2. **Deduplication** → Check `client_msg_id` or `ts` against processed messages
3. **Dispatch** → Call `dispatcher.dispatch()`
   - Load context from `ContextStorage`
   - Check for `/clear`, `/reset`, `/stats` commands
   - Handle image downloads (if files attached)
4. **LLM Call** → `GeminiLLM.generate_response()`
   - System instruction with date/time
   - Conversation history
   - Tool schemas
5. **Safety Override** → If no tool calls from LLM, match keywords and force appropriate tools
6. **Tool Execution** → Call registered tool functions, truncate results
7. **Second Round** → LLM analyzes tool results
8. **Response Formatting** → Slack markdown conversion, chunking (3800 char max), multi-message posting

#### **Context Storage** (`slack_bot/context/storage.py`):
```python
class ContextStorage:
    MAX_MESSAGES = 40  # Sliding window

    def __init__(self, channel_id: str):
        self.file_path = CONTEXT_DIR / f"{channel_id}.json"

    def add_message(role, content, model=None)
    def get_context() -> List[Dict]  # Standard format
    def clear()
```

---

### **6. LLM CLIENT SETUP**

#### **Architecture** (`slack_bot/llm/`):

**Base Class** (`base.py`):
```python
class BaseLLM(ABC):
    @abstractmethod
    def generate_response(message, context, tools=None, images=None) -> Tuple[str, List[Dict]]
```

**Gemini Implementation** (`gemini.py` - 519 lines):

**Dual-Backend Support**:
1. **OpenAI-Compatible API** (via OpenRouter or local proxy)
   - Uses `OpenAI` client from `openai` package
   - Points to `GEMINI_BASE_URL` (e.g., `https://openrouter.ai/api`)
   - Special headers for OpenRouter: `HTTP-Referer`, `X-Title`, `X-Google-API-Key`

2. **Direct Google API** (fallback)
   - Uses `google.generativeai` package
   - For cases without proxy

**Configuration Flow**:
```python
# In gemini.py __init__
api_key = os.environ.get("GEMINI_API_KEY")
base_url = os.environ.get("GEMINI_BASE_URL")

if base_url:
    # Use OpenAI-compatible client (proxy)
    client = OpenAI(api_key=api_key, base_url=f"{base_url}/v1")
else:
    # Use direct Google API
    genai.configure(api_key=api_key)
    genai_model = genai.GenerativeModel(model_name, system_instruction=...)
```

**Response Generation** (`generate_response()`):
- **Message Building**: System instruction + context + current message
- **Image Handling**: Convert to base64 for `image_url` in content
- **Tool Calling**: Extract from `message.tool_calls` (OpenAI format) or `response.candidates[0].content.parts` (Google format)
- **Retry Logic**: Exponential backoff for transient errors
- **Fallback**: If empty response with tools, retry without tools

**System Instructions**:
```python
# In get_system_instruction() - injected with current date
BUTLER_SYSTEM_INSTRUCTION_BASE = """You are Butler, a personal health assistant...
CRITICAL ANTI-HALLUCINATION RULES:
1. TOOL-FIRST POLICY: Call tools FIRST for current/specific health data
2. NO DATA INVENTION: If no data found, say so (don't guess)
3. VISION LIMITATIONS: If image unclear, say so (don't guess ingredients)
4. ACTION COMMANDS: If user says "sync", "fetch", you MUST call the tool
"""
```

---

### **7. VECTOR DB & EMBEDDING CODE**

**No Vector DB Found** ✗

The codebase uses **simple keyword-based RAG** (Retrieval-Augmented Generation):

#### **ObsidianIndexer Search** (`slack_bot/obsidian/indexer.py`):
```python
def search(query, limit=5):
    # Tokenize query (handles English/Chinese)
    tokens = re.findall(r'[a-zA-Z0-9_]+|[\u4e00-\u9fff]', query_lower)

    # Score files: count token occurrences with cubic weighting
    # Longer tokens (e.g., "XLSmart") score higher than single chars
    score = count * (len(token) ** 3)

    # Boost by unique token matches
    score *= (unique_matches ** 2)

    # Return top K files
```

**Why No Vector DB?**
- Simple search is sufficient for personal vault (~few thousand documents)
- Keyword-based avoids API dependency (embedding models)
- Prioritizes privacy & simplicity
- Future consideration: could add embedding via offline models (e.g., `all-MiniLM-L6-v2`)

---

### **8. KEY CODE EXCERPTS**

#### **Dispatcher Safety Override** (dispatcher.py, lines 117-257):
```python
# SAFETY OVERRIDE: If LLM didn't call tools, detect intent from keywords
if not tool_calls:
    lower_msg = message_text.lower()

    # 1. Sync Garmin
    if any(k in lower_msg for k in ["sync", "update", "fetch", "同步"]) and \
       any(k in lower_msg for k in ["garmin", "佳明", "data"]):
        tool_calls.append({"name": "sync_garmin", "args": {"target_date": "..."}})

    # 2. Query health data
    elif any(k in lower_msg for k in ["睡眠", "sleep", "步数", "steps"]):
        # Determine query type (single day vs trend)
        # Call appropriate tool (get_daily_detailed_stats or get_metric_history)

    # 3. Web search
    elif any(k in lower_msg for k in ["搜索", "search", "最新"]):
        tool_calls.append({"name": "search_web", "args": {"query": message_text}})
```

#### **Obsidian Generator Pattern** (generators.py):
```python
class WritingAssistant(BaseGenerator):
    def chat(self, user_input, history):
        is_first_turn = (len(history) == 0)

        if is_first_turn:
            # Build rich prompt with RAG + samples
            style_samples = self.indexer.get_writing_samples(count=3)
            rag_notes = self.indexer.search(topic, limit=5)

            prompt = f"""Ghostwriter for user. Topic: {topic}

            === YOUR IDENTITY & METHODOLOGY ===
            {self._read_file("methodology.md")}

            === YOUR WRITING SAMPLES ===
            {"\n\n".join(style_samples)}

            === RELEVANT NOTES ===
            {"\n\n".join(rag_notes)}

            Write the article in Chinese...
            """
            actual_user_message = prompt
        else:
            # Follow-up: just the instruction
            actual_user_message = user_input

        response, _ = self.llm.generate_response(prompt, history)
        new_history = history + [
            {"role": "user", "content": actual_user_message},
            {"role": "assistant", "content": response}
        ]
        return response, new_history
```

#### **Health Data Pydantic Models** (models/daily_metrics.py):
```python
class StepsData(BaseModel):
    date: date_type
    total_steps: Optional[int]
    total_distance_meters: Optional[float]
    calories_burned: Optional[int]
    step_goal: Optional[int]
    raw_data: Optional[Dict[str, Any]]

class SleepData(BaseModel):
    date: date_type
    total_sleep_seconds: Optional[int]
    deep_sleep_seconds: Optional[int]
    sleep_score: Optional[int]
    # ... 10+ other fields
    raw_data: Optional[Dict[str, Any]]

# Similar models for: HeartRateData, StressData, BodyBatteryData, etc.
```

---

### **9. SYSTEM INSTRUCTION TEMPLATES**

#### **Health Bot** (in `slack_bot/llm/gemini.py`):
```
You are Butler, a personal health assistant for tracking health data.

CRITICAL ANTI-HALLUCINATION RULES:
1. TOOL-FIRST POLICY: Call tools FIRST for current/specific data
2. NO DATA INVENTION: If no data, say "I cannot find data"
3. VISION LIMITATIONS: If image unclear, say so
4. EXPLICIT UNCERTAINTY: Call tool to check, don't guess

Guidelines:
- Be concise and data-driven
- Support both English and Chinese
```

#### **Obsidian Bot** (in `slack_bot/obsidian/generators.py`):
```
You are Butler (Obsidian Edition), an intelligent knowledge assistant.

Your Role:
- Writing: Mimic user's unique writing style
- Communication: Draft high-EQ, logically rigorous replies
- Decision: Act as Devil's Advocate using decision frameworks

Guidelines:
- Style Alignment: Strictly adhere to tone in provided samples
- Data Source: Rely on RAG + loaded markdown files
- Identity: Pragmatic, rational, value technological optimism
```

---

### **10. PROJECT STATISTICS**

| Metric | Value |
|--------|-------|
| **Total Python Lines** | ~10,600 |
| **Main Modules** | 3 (health, slack_bot, scripts) |
| **Health-Related Lines** | ~5,579 |
| **Slack Bot Lines** | ~2,097 (tools) + ~590 (dispatcher) |
| **LLM Implementations** | 2 (Gemini via OpenAI API + direct Google API) |
| **Tool Definitions** | 14+ callable tools |
| **Obsidian Modes** | 6 (write, reply, decide, search, deai, zhihu) |
| **Health Metrics Tracked** | 14+ |
| **Database** | SQLite + JSON (104 MB) |
| **Test Coverage** | Unit + Integration tests in `/tests` |

---

### **11. IMPORTANT FILE PATHS (ABSOLUTE)**

**Core Entry Points**:
- `/root/projects/butler/slack_bot/main.py` - Health Bot entry
- `/root/projects/butler/slack_bot/dispatcher.py` - Message routing (~590 lines)
- `/root/projects/butler/slack_bot/llm/gemini.py` - LLM implementation (~519 lines)

**Obsidian System**:
- `/root/projects/butler/slack_bot/obsidian/dispatcher.py` - Mode routing
- `/root/projects/butler/slack_bot/obsidian/generators.py` - Generator implementations
- `/root/projects/butler/slack_bot/obsidian/indexer.py` - RAG indexer (~188 lines)

**Tools**:
- `/root/projects/butler/slack_bot/tools/registry.py` - Tool definitions (~412 lines)
- `/root/projects/butler/slack_bot/tools/health_read.py` - Read tools (~1,013 lines)
- `/root/projects/butler/slack_bot/tools/health_write.py` - Write tools
- `/root/projects/butler/slack_bot/tools/groups.py` - Tool presets

**Models & Configuration**:
- `/root/projects/butler/health/models/daily_metrics.py` - Pydantic models
- `/root/projects/butler/health/services/garmin_client.py` - Garmin API client
- `/root/projects/butler/slack_bot/context/storage.py` - Context management

**Supporting Files**:
- `/root/projects/butler/.env` - Environment config (contains secrets)
- `/root/projects/butler/writing_style.md` - Obsidian bot style guide
- `/root/projects/butler/methodology.md` - Decision framework
- `/root/projects/butler/health.md` - Health profile loaded by LLM

---

This comprehensive exploration should give you a solid understanding of the codebase architecture, how modes are implemented in both systems, and how the LLM integration works. The codebase is well-structured with clear separation of concerns and uses Pydantic for type safety throughout.