Design: Phase 1 - The Productivity-Spending Correlation Audit
Overview
This audit aims to establish a "Source of Truth" by correlating financial spending patterns with professional productivity (GitHub activity). The goal is to identify the relationship between AI/Impulse spending and coding output to inform future optimization strategies.
1. Data Ingestion & Cleaning
1.1 Financial Data
- Sources:
download-transactions(2).csvand provided text for pending transactions. - Normalization: All transactions will be converted to CAD using the exchange rates provided in the CSV.
- Cleaning: Remove noise from merchant descriptions to facilitate pattern matching.
1.2 Productivity Data
- Source: GitHub CLI (
gh). - Metrics: Commits, push events, and pull request activity for the period Jan 2026 - June 2026.
2. Targeted Categorization
Transactions will be mapped into four primary segments:
- AI_SPEND: Subscriptions and API usage for AI services (OpenAI, Claude, OpenRouter, DeepSeek, etc.).
- WEED_IMPULSE_SPEND: Transactions identified as cannabis-related or high-frequency convenience spending (e.g., UberEats, fast food, bars).
- ESSENTIAL: Necessary living expenses (Rent, basic groceries, utilities, essential transport).
- NON_ESSENTIAL_OTHER: All other discretionary spending.
3. Correlation & Analysis
The analysis will compute:
- AI ROI:
Total AI Spend / Total GitHub Activity. - Behavioral Correlation: Statistical correlation between
Daily Spending (AI + Weed/Impulse)andDaily GitHub Activity. - The "Cost of Inactivity": Daily burn rate of subscriptions during periods of zero GitHub activity.
4. Visualizations
- The Correlation Plot: A dual-axis time-series graph (Spending vs. Commits).
- The Segmented Sunburst: A radial chart showing the distribution of spending across the four segments.
- The Leakage Heatmap: A calendar view of high-spending days.
5. Constraints & Privacy
- Privacy: No data will be transmitted outside the local environment.
- Integrity: All calculations must use the normalized CAD values.