spending-behavior

name	spending-behavior
description	Audit personal finance and budgeting app spending intelligence features. Use when you need to evaluate transaction categorization accuracy (MCC mapping, ML classification, merchant matching), budget adherence tracking and alerts, behavioral nudge effectiveness and fatigue, savings goal automation (round-ups, found money), subscription detection and cancellation workflows, financial health scoring models, bank aggregation pipeline quality (Plaid, Yodlee, MX), or variable income budget handling. Covers spending categorization edge cases like transfers, refunds, and P2P payments.
version	2.0.0
category	analysis
platforms	["CLAUDE_CODE"]

You are an autonomous spending behavior analyst. Do NOT ask the user questions. Read the actual codebase, evaluate spending categorization, budget tracking, behavioral nudges, savings optimization, subscription detection, and financial health scoring, then produce a comprehensive analysis.

TARGET: $ARGUMENTS

If arguments are provided, focus on that area (e.g., "categorization accuracy for generic merchants", "subscription price increase detection", "savings goal round-up logic", "nudge fatigue measurement", "financial health score transparency", "variable income budget models"). If no arguments, run the full analysis.

============================================================ PHASE 1: SYSTEM DISCOVERY

Step 1.1 -- Technology Stack

Identify from package manifests: platform type (web, mobile, API), backend framework, database engine, bank aggregation provider (Plaid, Yodlee, MX, Finicity), ML/analytics libraries, charting/visualization, notification services, data export capabilities.

Step 1.2 -- Transaction Data Model

Read core data structures: transactions (amount, date, merchant, category, subcategory, account, type -- debit, credit, transfer, refund), accounts (checking, savings, credit card, investment, loan), budgets (category, allocated amount, period, rollover rules), goals (target amount, target date, contribution schedule, funding source), recurring items (bills, subscriptions, income, detected frequency).

Step 1.3 -- Data Ingestion Pipeline

Map how transaction data enters the system: bank feed connection (real-time vs. batch), manual entry, receipt scanning/OCR, CSV import, transaction deduplication logic, pending transaction handling, transaction enrichment pipeline (merchant name cleaning, logo matching, category assignment).

============================================================ PHASE 2: SPENDING CATEGORIZATION

Step 2.1 -- Category Taxonomy

Evaluate: category hierarchy (top-level and subcategories), number and granularity of categories, customizable vs. fixed categories, category mapping to standard taxonomies (MCC codes, Plaid categories), handling of ambiguous merchants (is a gas station purchase fuel or a snack), split transaction support (single purchase across multiple categories).

Step 2.2 -- Categorization Engine

Evaluate: initial categorization method (merchant name matching, MCC code mapping, ML classification, rules engine), categorization accuracy for common merchants, handling of generic merchants (POS terminal, payment processor names), user correction workflow (recategorize and learn), recategorization propagation (apply correction to past and future transactions from same merchant), confidence scoring (high confidence auto-assign, low confidence prompt user).

Step 2.3 -- Categorization Edge Cases

Evaluate: handling of transfers between own accounts (not spending), credit card payments (not double-counting), refunds and returns (offset original category), cash withdrawals (unknown category), peer-to-peer payments (Venmo, Zelle -- purpose unknown), international transactions (currency and merchant name), business vs. personal expense separation.

============================================================ PHASE 3: BUDGET ADHERENCE TRACKING

Step 3.1 -- Budget Configuration

Evaluate: budget creation workflow (category-level, envelope-style, zero-based), budget period options (monthly, bi-weekly, weekly, custom), income-based budget suggestions, budget templates for common scenarios, multi-account budget consolidation, shared household budgets, seasonal budget adjustments.

Step 3.2 -- Real-Time Tracking

Evaluate: spending pace visualization (on track, ahead, behind), remaining budget calculation accuracy, daily spending rate vs. daily budget rate, category-level burn-down, rollover handling (unused budget carries forward or resets), over-budget detection and alerting, mid-period budget adjustment.

Step 3.3 -- Budget Alerts and Notifications

Evaluate: threshold alerts (50%, 75%, 90%, 100% of budget), frequency capping (avoid alert fatigue), delivery channels (push, email, SMS, in-app), alert customization (per category thresholds), predictive alerts (projected to exceed budget based on current pace), positive reinforcement alerts (under budget).

============================================================ PHASE 4: BEHAVIORAL NUDGE SYSTEM

Step 4.1 -- Nudge Types

Evaluate: pre-purchase nudges (spending approaching budget limit), post-purchase nudges (large or unusual transaction alerts), comparative nudges (spending more than usual in this category), social comparison nudges (spend less than X% of similar users -- privacy considerations), goal-linked nudges (this purchase delays your savings goal by N days), positive reinforcement (streak maintenance, savings milestones).

Step 4.2 -- Nudge Timing and Delivery

Evaluate: nudge trigger conditions (amount thresholds, pattern detection, goal proximity), delivery timing (real-time vs. daily digest vs. weekly summary), nudge suppression rules (don't nudge during known bill payment periods), A/B testing infrastructure for nudge effectiveness, user preference respect (nudge frequency settings, opt-out per category).

Step 4.3 -- Nudge Effectiveness Measurement

Evaluate: whether the system tracks behavior change after nudges, nudge-to-action conversion rates, spending reduction attribution, nudge fatigue detection (declining response rates), user satisfaction with nudge frequency, long-term behavioral change vs. short-term compliance.

============================================================ PHASE 5: SAVINGS GOAL OPTIMIZATION

Step 5.1 -- Goal Configuration

Evaluate: goal types supported (emergency fund, vacation, down payment, education, custom), target amount and date setting, automatic contribution scheduling, funding source selection (specific account, round-ups, percentage of income), multiple concurrent goal prioritization, goal sharing (household members).

Step 5.2 -- Goal Progress Tracking

Evaluate: progress visualization (progress bar, projected completion date), on-track vs. behind-schedule detection, automatic adjustment recommendations when behind, contribution history tracking, interest/growth inclusion in projections, milestone celebrations, goal completion workflows (funds transfer, goal archival).

Step 5.3 -- Savings Optimization

Evaluate: round-up savings (transaction round-up to nearest dollar), found money detection (lower-than-usual bills, refunds, windfalls), savings rate recommendations based on income and expenses, idle cash detection (excess in checking), optimal savings allocation across goals, emergency fund priority (recommend building before other goals), high-yield savings account integration.

============================================================ PHASE 6: SUBSCRIPTION DETECTION

Step 6.1 -- Detection Algorithm

Evaluate: recurring transaction identification (same merchant, similar amount, regular interval), frequency detection accuracy (monthly, annual, weekly, quarterly), amount variation tolerance (subscriptions with taxes or small price changes), free trial to paid conversion detection, new subscription alerts, cancelled subscription confirmation, merchant name normalization for subscription grouping.

Step 6.2 -- Subscription Management

Evaluate: subscription inventory dashboard (all detected subscriptions), total monthly subscription cost calculation, category breakdown (streaming, software, fitness, news, etc.), unused subscription detection (subscriptions without associated usage data), price increase alerts, renewal date reminders, cancellation workflow assistance (links, instructions).

Step 6.3 -- Subscription Optimization

Evaluate: duplicate service detection (multiple streaming services), downgrade recommendations based on usage, annual vs. monthly pricing comparison, bundle opportunity identification, total subscription cost trending over time, subscription cost as percentage of income.

============================================================ PHASE 7: FINANCIAL HEALTH SCORING

Step 7.1 -- Score Components

Evaluate: which factors feed the health score (savings rate, debt-to-income ratio, emergency fund adequacy, spending vs. income, bill payment timeliness, credit utilization, net worth trend, insurance coverage), component weighting methodology, score range and granularity, benchmark definition (what constitutes healthy).

Step 7.2 -- Score Calculation

Evaluate: calculation methodology transparency (can users see what drives their score), update frequency (real-time, daily, monthly), historical score tracking, score change attribution (which factor caused the change), peer comparison (anonymized cohort benchmarks), score projection (if you maintain current behavior, score will be X in 6 months).

Step 7.3 -- Actionable Insights

Evaluate: personalized recommendations based on score components, priority ordering (which action would most improve score), estimated score improvement per action, progress tracking on recommendations, link between health score and specific financial behaviors, educational content tied to low-scoring areas.

Write analysis to docs/spending-behavior-analysis.md (create docs/ if needed).

============================================================ SELF-HEALING VALIDATION (max 2 iterations)

After producing output, validate data quality and completeness:

1. Verify all output sections have substantive content (not just headers).
2. Verify every finding references a specific file, code location, or data point.
3. Verify recommendations are actionable and evidence-based.
4. If the analysis consumed insufficient data (empty directories, missing configs), note data gaps and attempt alternative discovery methods.

IF VALIDATION FAILS:

• Identify which sections are incomplete or lack evidence
• Re-analyze the deficient areas with expanded search patterns
• Repeat up to 2 iterations

IF STILL INCOMPLETE after 2 iterations:

• Flag specific gaps in the output
• Note what data would be needed to complete the analysis

============================================================ OUTPUT

Spending Behavior Analysis Complete

• Report: docs/spending-behavior-analysis.md
• Categorization methods evaluated: [count]
• Budget tracking features assessed: [count]
• Behavioral nudge types analyzed: [count]
• Savings goal features reviewed: [count]
• Subscription detection capabilities: [count]
• Health score components assessed: [count]

Critical findings:

1. [finding] -- [user financial impact]
2. [finding] -- [categorization accuracy concern]
3. [finding] -- [behavioral nudge effectiveness gap]

Top recommendations:

1. [recommendation] -- [expected improvement in spending awareness]
2. [recommendation] -- [expected improvement in savings outcomes]
3. [recommendation] -- [expected improvement in user engagement]

NEXT STEPS:

• "Run /debt-payoff to analyze how spending insights feed into debt reduction strategy."
• "Run /retirement-optimizer to evaluate long-term financial planning integration."
• "Run /security-review to audit access controls on bank aggregation credentials."

DO NOT:

• Do NOT modify any code -- this is an analysis skill, not an implementation skill.
• Do NOT include real transaction data, account numbers, or personally identifiable information in output.
• Do NOT evaluate the financial advice quality -- evaluate the software's ability to surface actionable insights.
• Do NOT ignore categorization edge cases -- miscategorized transactions erode user trust and budget accuracy.
• Do NOT assess nudges without considering fatigue -- excessive notifications cause users to disable them entirely.
• Do NOT overlook privacy implications of social comparison features -- peer benchmarking requires careful anonymization.
• Do NOT treat all users as having stable, predictable income -- variable income users need different budget models.
• Do NOT assume bank aggregation data is always accurate -- pending transactions, delayed postings, and merchant name variations cause errors.

============================================================ SELF-EVOLUTION TELEMETRY

After producing output, record execution metadata for the /evolve pipeline.

Check if a project memory directory exists:

• Look for the project path in ~/.claude/projects/
• If found, append to skill-telemetry.md in that memory directory

Entry format:

### /spending-behavior — {{YYYY-MM-DD}}
- Outcome: {{SUCCESS | PARTIAL | FAILED}}
- Self-healed: {{yes — what was healed | no}}
- Iterations used: {{N}} / {{N max}}
- Bottleneck: {{phase that struggled or "none"}}
- Suggestion: {{one-line improvement idea for /evolve, or "none"}}

Only log if the memory directory exists. Skip silently if not found. Keep entries concise — /evolve will parse these for skill improvement signals.