Spending Analysis

spending-analysis

Purpose

Analyzes bank transaction data (from checking_transactions): categorizes spend into a fixed taxonomy, flags anomalies against trailing history, and synthesizes natural language budgeting insights, savings rate metrics, and reserve months estimates.

Produces typed narrative fields for the orchestrator to merge into the final SpendingReport.

When this skill runs

• User asks a direct spending question ("how much did I spend on dining last month", "why did X category jump")
• Goals & Onboarding invokes it during interview to estimate savings rate and inform the liquidity-needs discussion
• Root planner invokes it ad hoc when a proposed action's rationale would benefit from spending context (rare; most paths don't need this)

Inputs

Category taxonomy

Transaction category strings from statements are normalized into a fixed internal taxonomy:

housing, utilities, groceries, dining, transportation, entertainment, subscriptions, healthcare, travel, shopping, income, transfers, fees, other

Unmapped raw categories fall into other.

Deterministic calculations (orchestrator-precomputed)

The orchestrator deterministically computes math and aggregates before dispatching to the Managed Agent:

1. Anomaly detection rule:

   current_month_spend > (trailing_3_month_average * 1.4)
     AND
   current_month_spend > (trailing_3_month_average + 100)
   

2. Savings rate: savings_rate = (total_income - total_outflow) / total_income over trailing 3 months
3. Reserve months: reserve_months = HoldingsSnapshot.cash_usd / average_monthly_expenses

Output

Return only the natural-language synthesis fields:

• narrative_summary — 2–5 sentences. Directly answer the user's query_intent using the preloaded numeric facts. If anomalies were flagged by the preloader, name them and give a plausible cause. If reserve_months is under 3 or savings_rate is under 0 (spending more than earning), call it out. Do not invent numbers; every figure you cite must appear in input.preloaded. If input.preloaded contains zero income/outflow and empty categories, state clearly that no transaction history is available.
• anomaly_commentary — optional list, at most one entry per anomaly in input.preloaded.anomalies, in the same order. Each string replaces the preloader's canned "surged from average" description with a more specific explanation (e.g. "one-time medical bill", "annual insurance premium"). Leave empty if you have no better explanation — the preloader's description is a valid default.

Do NOT restate total_income_usd, total_outflow_usd, savings_rate, reserve_months, category_breakdown, or the numeric fields of any anomaly. Those come from input.preloaded and are authoritative. The orchestrator merges your two fields onto the preloaded facts and persists the resulting SpendingReport.

Tools / permissions required

• Managed Agent sandbox: conversational reasoning over preloaded context
• Orchestrator (outside sandbox):
  • BigQuery: read checking_transactions (aggregate/SELECT only, user-scoped via Native SDK and BigQuery Remote MCP Server https://bigquery.googleapis.com/mcp)
  • Firestore: read holdings (cash balance only, for reserve estimate via Firestore Remote MCP Server)
• No direct database execution tools inside the Managed Agent sandbox.

Registry metadata

• Registered as: projects/{project}/locations/{location}/skills/private-spending-analysis
• Skill revision: 0.2.0 (draft — not yet registered)
• Approval scope: read:spending,read:holdings