Build a daily decision journal bot

Here’s a plan to build a Daily Decision Journal Bot—a tool to help users log decisions, reflect on outcomes, and improve judgment over time. Below is a complete guide covering the bot’s core features, data model, logic, and a sample implementation using Python (with a basic command-line interface). This can be extended to web or app interfaces.

🎯 Purpose of the Bot

• Encourage daily logging of significant decisions.

• Prompt structured reflection.

• Help track patterns and improve decision quality.

🧠 Core Features

1. Daily Prompt:

   • “What is a key decision you made today?”

   • “What were your expectations?”

   • “What information did you have at the time?”

   • “What was the result (if known)?”

   • “What will you do differently next time?”

2. Decision Tracker:

   • Store and timestamp entries.

   • Ability to search past decisions by date, keyword, or category.

3. Reflection Reminder:

   • Daily or weekly notifications to review prior decisions.

4. Outcome Review:

   • Ask users to revisit decisions after a set time (e.g., 30 days) to update the outcome.

5. Insights Summary (Optional):

   • Weekly or monthly summary of decisions and reflection prompts.

   • Trends in decision-making: overconfidence, lack of info, biases, etc.

🗃️ Data Model (JSON Example)

json
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{
  "date": "2025-05-18",
  "decision": "Offered a discount to retain a key client",
  "context": "Client threatened to leave; sales are down",
  "expected_outcome": "They accept the discount and renew",
  "actual_outcome": "Pending",
  "lessons_learned": "",
  "review_date": "2025-06-18"
}

🛠️ Bot Implementation (Python CLI version)

python
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import json
import os
from datetime import datetime, timedelta

DATA_FILE = "decision_journal.json"

def load_entries():
    if os.path.exists(DATA_FILE):
        with open(DATA_FILE, "r") as f:
            return json.load(f)
    return []

def save_entries(entries):
    with open(DATA_FILE, "w") as f:
        json.dump(entries, f, indent=2)

def prompt_today_entry():
    today = datetime.now().strftime("%Y-%m-%d")
    print(f"n📘 Decision Journal for {today}")
    decision = input("➡️ What decision did you make today? ")
    context = input("🧠 What was the context or background? ")
    expected = input("🔮 What was your expected outcome? ")
    review_date = (datetime.now() + timedelta(days=30)).strftime("%Y-%m-%d")

    entry = {
        "date": today,
        "decision": decision,
        "context": context,
        "expected_outcome": expected,
        "actual_outcome": "",
        "lessons_learned": "",
        "review_date": review_date
    }

    entries = load_entries()
    entries.append(entry)
    save_entries(entries)
    print("n✅ Entry saved! You’ll be reminded to review it in 30 days.")

def review_past_decisions():
    today = datetime.now().strftime("%Y-%m-%d")
    entries = load_entries()
    due_reviews = [e for e in entries if e["review_date"] <= today and e["actual_outcome"] == ""]

    if not due_reviews:
        print("n📭 No decisions to review today.")
        return

    print("n🔁 Time to review past decisions:n")
    for i, entry in enumerate(due_reviews, 1):
        print(f"n{i}. Decision from {entry['date']}")
        print(f"➡️ {entry['decision']}")
        print(f"🔮 Expected: {entry['expected_outcome']}")
        actual = input("✅ What was the actual outcome? ")
        lesson = input("📌 What did you learn from this? ")
        entry["actual_outcome"] = actual
        entry["lessons_learned"] = lesson

    save_entries(entries)
    print("n📝 Reviews updated!")

def main():
    print("n=== Daily Decision Journal Bot ===")
    print("1. Log today’s decision")
    print("2. Review past decisions")
    choice = input("Choose an option (1/2): ")

    if choice == "1":
        prompt_today_entry()
    elif choice == "2":
        review_past_decisions()
    else:
        print("❌ Invalid choice. Try again.")

if __name__ == "__main__":
    main()

💡 Future Extensions

• Web App (Flask/Django + JS frontend)

• Mobile App (React Native / Flutter)

• Voice Assistant Integration (Alexa, Google Assistant)

• Data Analytics Dashboard (for bias/pattern recognition)

• Export/Backup Options (CSV, PDF, cloud sync)

Let me know if you’d like a web version, mobile version, or integration with a specific platform (like Notion, Google Sheets, or an AI assistant).