Finance Analysis

Personal Finance Analysis

Run a deep financial and behavioral analysis using the latest master CSV data and Obsidian note.

Phase 1: Ensure Data is Current

1. Run /personal-finance-update first to sync all tables.
   • If /personal-finance-update fails: Report the error to the user, then suggest manual steps:
     1. Verify the master CSV directory exists (check config.json for the path)
     2. Confirm ~/Library/Mobile Documents/com~apple~CloudDocs is syncing
     3. Try running /personal-finance-update again
     4. If still failing, proceed with whatever data is already on disk and note the staleness in the report
2. Read ~/.claude/skills/finance-analysis/config.json to get paths for obsidian_note and memory_file.
3. Read the Obsidian note and memory file at the configured paths.

Phase 2: Load Raw Data

1. Read all master CSVs from the master_csv_dir path in config.json for years 2020 onward (the reliable range).
2. Compute any metrics not already in the Obsidian tables — monthly breakdowns, per-category trends, income source timelines, cash flow by month.

Phase 3: Financial Analysis

Present the following, using actual numbers from the data:

1. Net worth trajectory & cash flow — yearly and monthly trends (2020–present). Income vs outflow. Is the user saving, breaking even, or bleeding?
2. Spending efficiency — which categories deliver ROI (education, health, business investments) vs pure consumption? Quantify the split.
3. Burn rate — true monthly cost of living including cash withdrawals and transfers. Compare employed months vs unemployed months.
4. Risk exposure — income concentration (single source?), liquidity gaps, untracked blind spots (UOB joint account, cash spending, StanChart).
5. Runway projection — at current burn rate with no income change, how many months of runway remain? What if luxuries are cut to zero?
6. Category deep-dives — flag any category with unusual patterns, sudden changes, or outsized impact on the overall picture.

Phase 4: Psychological & Behavioral Analysis

Analyze the spending data as a behavioral signal:

1. Values revealed by spending — what do the numbers say about priorities and identity, beyond stated beliefs?
2. Stated vs actual — where do stated beliefs (minimalism, antifragility, wealth-first) conflict with actual spending behavior?
3. Emotional triggers — correlate spending spikes with life events (layoff, travel, seasons, wife-related). Are there patterns?
4. Impulse vs deliberate — what fraction of spending looks planned vs reactive? Use transaction frequency, amounts, and timing.
5. Relationship with money — fearful, strategic, impulsive, or evolving? How has the pattern shifted from 2020 to now?
6. Self-deception audit — where is spending rationalized as "investment" or "necessity" when data suggests comfort or status?
7. Highest-impact behavioral change — one concrete change that the data shows would have the biggest financial effect.

Phase 5: Report

Structure the output as:

1. Executive Summary — 3-5 bullet points, the most important findings.
2. Financial Analysis — numbered sections from Phase 3, with tables and numbers.
3. Behavioral Analysis — numbered sections from Phase 4, referencing specific transactions and patterns.
4. Recommendations — 3 actionable items ranked by financial impact.

Be brutally honest. Use actual numbers. Challenge the user's self-narrative where data contradicts it. No flattery, no hedging.