spending pattern analyzer
Analyzes 30-day spending patterns to identify financial leaks and calculate a weekly safe-to-spend number.
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Works with the AI tools you already use
See it in action
You say
I've been hit with two overdrafts lately. Here is my transaction history from last month. My floor is $1000, I have $1200 now, and rent of $800 is due in 5 days. Can I spend $50 on dinner tonight?
Your agent does
SAFE SPEND THIS WEEK: $0 VERDICT: Hard Stop — Rent is due in 5 days and your working funds ($200) are already $600 short of covering it without dipping below your $1000 floor. Adding a $50 dinner increases your floor breach to $650.
What you get
About this skill
The problem
Transaction logs show what happened, but they rarely explain why your balance is trending down. Most budget tools focus on historical categorization rather than predicting how much you can actually spend today without hitting your floor.
What it does
- Analyzes 30 days of transaction data from CSVs, pastes, or email receipts to calculate weekly burn rates.
- Identifies specific behavioral patterns like late-night impulse windows, subscription bleed, and small-leak spending.
- Calculates a concrete Safe-to-Spend number for the current week after accounting for upcoming obligations and reserve floors.
- Determines runway trajectory to predict exactly when current spending habits will breach your minimum cash reserve.
Why this beats prompting it yourself
Standard LLM prompts often hallucinate math or fail to maintain a consistent cash-reserve floor. This skill enforces a multi-step financial frame that separates working funds from fixed obligations, ensuring the safe-to-spend verdict is based on net liquidity rather than just total balance.
Use cases
- Audit a month of bank exports to find hidden subscription costs and recurring leaks.
- Get a "Can I afford this?" check before making a large unplanned purchase.
- Diagnose why an account keeps hitting low-balance alerts despite steady income.
- Establish a weekly spending limit that protects a long-term savings floor.
Known limitations
Accuracy depends on the completeness of the 30-day transaction history provided. Estimates used in place of hard data will be marked with tildes to indicate approximate values.
How to install
Drop the file into your AI Agent. Works with Claude, Cursor, ChatGPT, and 20+ more.
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