# Daily Oracle (Automated)

This skill is designed to run as a background process (Cron Job). It wakes up, analyzes the user's digital footprint, generates a prediction, pushes the notification, and then terminates.

⚠️ PRIVACY NOTICE: This skill processes highly sensitive personal data. All analysis must be performed within the local LLM context. No data is to be sent to third-party logging services.

## Execution Routine

### 1. Initialization & Anti-Ban Jitter

Context: Since this task is triggered by a precise Cron timer (e.g., exactly 08:00:00), immediate execution creates a detectable bot pattern.
Action: Generate a random integer between 120 and 900 (seconds).
Wait: Sleep for that duration before making any network requests.

```bash

sleep $(( RANDOM % 780 + 120 ))

```

### 2. Silent Data Gathering

#### A. Calendar Context

Action: Read the local calendar for today's agenda (00:00 to 23:59).
Tool Usage: Use calendar-access or CLI equivalents (e.g., icalBuddy on macOS).

- _Prompt_: "Summarize my calendar events for today from 00:00 to 23:59. Focus on constraints (meetings) and opportunities (free blocks)."

#### B. Social Context (Headless)

Action: Open a headless browser session using _existing_ cookies.
Target: Scan "Close Friends" or specific mutuals who appear in the user's recent calendar/messages.
Safety: Limit scrolling to max 5 posts/stories. Abort immediately if login is requested to prevent session flagging.
Data Extraction: Screenshot or scrape text from the first viewport of specific curated URLs (e.g., instagram.com/direct/inbox/).

#### C. Communications (iMessage/Local DB)

Action: Read the last 50 messages from the local chat.db.
Query:

```sql

SELECT

text,

datetime(date/1000000000 + 978307200, 'unixepoch', 'localtime') as date_sent

FROM message

WHERE date_sent > datetime('now', '-24 hours')

ORDER BY date DESC

LIMIT 50;

```

Filtering: Look for intent keywords: "tomorrow", "gym", "coffee", "meet", "lunch", "tonight".

#### D. System Signals

Action: Check ~/Downloads for recent files.

```bash

find ~/Downloads -type f -mtime -1 -print

```

Inference: Determine active deliverables or recent interests based on file types (e.g., PDFs vs. Images).

### 3. The Oracle's Inference

Synthesize: Combine the hard data (Calendar: "Gym at 5pm") with soft data (Social: "Amy posted about coffee").
Predict: Formulate a single, high-confidence sentence in the future tense.
Tone: Insightful but grounded. Example: _"You will meet Amy at the gym today, and she will likely suggest getting coffee after workout because she posted about craving caffeine"_

### 4. Push Notification (Critical)

Context: The user is likely not looking at the terminal or chat window.
Action: Use the system's primary notification tool.

- _macOS_: osascript -e 'display notification "Your prediction..." with title "Daily Oracle"'

- _Linux_: notify-send "Daily Oracle" "Your prediction..."

Format:

> 🔮 Daily Oracle: [Your Prediction Here]

## Constraints & Safety

1. One-Shot Execution: This process must run from start to finish without pausing for user input.

2. Failure Mode: If data is insufficient to make a specific prediction, fall back to a generic personalized wellness tip based on the weather. Do NOT fail silently.

3. Data Hygiene: Clearly wipe the temporary context/memory of the gathered data once the prediction is sent.

4. Silence on Sources: The output message must strictly contain the _prediction_ and subtle reason for prediction. Do not list the data sources in the notification.

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