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    spending checkup

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    Turn bank CSV exports into automated spending reports, budget plans, and subscription audits via local Python scripts.

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    spending checkup

    spending checkup

    Example session with this skill installed

    I have three months of bank exports in CSV. Can you run a spending checkup, find my subscriptions, and help me save $200 a month?

    • Read your context and instructions
    • Compiled the spending checkup
    • Generated the document

    I have processed your exports from January through March. The analysis identified 84% of your transactions, with only a few small coffee shop entries remaining unassigned.

    Spending checkup · Jan 2024 - Mar 2024 · ✅

    Typical monthIncomeSpendingLeft
    Median$5,200$4,150$1,050

    Where it goes:

    • Housing & Utilities: $2,100
    • Groceries: $650
    • Dining Out: $480
    • Transport: $320
    • Shopping: $250

    Recurring: 12 payments, $1,840 per year

    • Price rise: Your "Cloud Storage" plan increased from $9.99 to $12.99 in February.
    • Renewals: Annual Amazon Prime renewal ($139) is expected in April.

    3 ideas to save:

    • Dining Out: Reduce by 25% to save $120/month ($1,440/year).

    Subscription Audit: Cancel the unused "Fitness App" ($15/month) and "Magazine Sub" ($10/month) found in your recurring list.

    Insurance: Your monthly premiums are stable, but shopping for a new car insurance provider could address the remaining $55 needed for your $200 goal.

    Next steps

    1. Open spending-output/spending.html to see the visual trend charts.
    2. Review the Recurring tab in spending.xlsx to confirm which subscriptions are still active.
    3. Update rules.json with the merchant names I flagged to refine next month's report.

    spending.html

    PDF · document

    Generated

    Example file from a real run - the skill writes it into your workspace.

    Connects securely to your tools. The creator never sees your data.

    About this skill

    Managing personal finances is often a choice between manual spreadsheet entry or giving third-party apps full access to your bank accounts. This skill provides a private, local alternative for turning bank exports into actionable financial intelligence.

    What it does

    • Category mapping converts raw bank and credit card CSV exports into categorized monthly spending reports.
    • Budget generation creates a realistic monthly budget based on median spending patterns rather than simple averages.
    • Subscription auditing detects recurring payments, identifies price hikes over time, and flags upcoming yearly renewals.
    • Savings analysis identifies specific areas for cost reduction to meet a defined monthly savings goal.
    • Dashboard export generates both an interactive HTML dashboard and a detailed Excel workbook with organized sheets.

    How it works

    1. Import exports using the inspect command to validate CSV headers and date formats from your statements.
    2. Configure rules to ignore internal transfers between accounts and map specific merchants to categories.
    3. Execute build to process the transactions through the Python script and generate the financial model.
    4. Refine data by answering brief questions about unrecognized merchants to sharpen the accuracy of the report.

    Frameworks & tools

    This skill utilizes a Python 3.8+ script using only the standard library for maximum portability and privacy. It outputs standard .html and .xlsx files compatible with any browser and Excel or Google Sheets.

    Why this beats prompting it yourself

    Raw LLM prompts often hallucinate math or misinterpret transaction dates across large files. This skill uses a deterministic Python engine to ensure 100% mathematical accuracy while the AI handles the complex semantic task of merchant categorization.

    Use cases

    • Auditing the last 12 months of spending to find forgotten subscriptions.
    • Building a realistic household budget based on historical median data.
    • Identifying price creep in utility bills or insurance premiums over time.
    • Analyzing irregular large expenses that disrupt monthly cash flow.

    Known limitations

    Requires Python 3.8 or higher installed on the local machine. User must manually download CSV statements from their financial institutions.

    How to use

    Skills on Agensi run inside your AI agent. Copy the prompt, paste it into your agent, and it connects to Agensi and installs this skill for you. If a payment is needed, your agent shows it to you first.

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