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    Ouderdomsbalans Analyse

    4

    Automated Dutch accounts receivable aging analysis (ouderdomsbalans) with Python-driven bucket categorization.

    $7

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    ouderdomsbalans-analyse

    ouderdomsbalans-analyse

    Example session with this skill installed

    Maak een ouderdomsbalans van facturen.csv met peildatum 2026-06-10 en controleer de top 3 risico's.

    • Read your context and instructions
    • Compiled the ouderdomsbalans-analyse

    Analyse voltooid op peildatum 2026-06-10.
    Totaal openstaand: €142.500 (>90d: 31%).
    Top 3 Risico's:

    1. Klant Janssen: €5.445 (>90d, factuur 2025-201)
    2. BVBA De Smet: €3.200 (61-90d)
    3. Firma Pieters: €1.150 (>90d)
      Check 400-rekening aansluiting; adviseer bespreking waardevermindering voor Janssen.

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

    About this skill

    Automated Accounts Receivable Aging Analysis

    This skill automates the creation of an "Ouderdomsbalans" (Aging Report), a critical task for accountants and financial controllers. It processes raw invoice exports to categorize outstanding balances into aging buckets, providing a clear overview of credit risk and liquidity.

    What it does

    The skill utilizes a Python-based processing engine to transform a CSV export containing customer names, invoice numbers, due dates, and amounts. It handles partial payments, credit notes, and net balance calculations per customer.

    • Bucket Distribution: Categorizes receivables into Not Due, 1-30, 31-60, 61-90, and >90 days.
    • Deep-Dive Reporting: Generates a specific detail block for items over 90 days, facilitating immediate action on bad debt.
    • Reconciliation Flagging: Compares results against General Ledger (GL) balances to identify export discrepancies.
    • Strategic Insights: Calculates the ">90-day percentage" and identifies top-risk customers to guide provision discussions.

    Why use this skill?

    Manual aging reports in Excel are prone to formula errors and struggle with credit note netting. This skill ensures a consistent, programmatic approach that is repeatable for every period-end closing. It transitions the accountant from data entry to data analysis, focusing on which debts are truly doubtful.

    Built by Nex AI. More skills and info at nex-ai.be and slopsome.com.

    Find more skills like this on the Claude Skills Marketplace, the Codex Skills Marketplace and the Gemini Skills Marketplace, or browse Claude Skills for Finance & Accounting.

    • Cannot detect customer insolvency or disputes automatically.
    • No multi-currency support (single currency per run).
    • Requires strictly formatted CSV input for accuracy.

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    How to use

    1. 1

      Connect your agent to the Agensi MCP

      Copy the prompt and paste it into Claude, ChatGPT, Cursor or any agent that supports MCP. It sets up the connection for you. Manual setup

    2. 2

      Sign in to Agensi

      Your agent shows a sign-in link. Sign in and click Allow.

    3. 3

      Ask for what you need

      Your agent loads this skill and uses it on your task. It always asks before buying a skill.

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    Permissions

    Terminal / Shell
    Read Files
    Write Files

    Terminal/Shell: the agent runs the bundled Python script (argparse CLI). Read/Write Files: the script reads user-supplied local input files (CSV/JSON) and writes the result to a user-specified path. No network access, no environment variables, no secrets.

    Listed5 months ago
    Updated3 months ago

    What's inside

    Frequently Asked Questions

    It automates the categorization of unpaid invoices into specific time buckets (like 30, 60, or 90+ days) and identifies high-risk customers, eliminating the manual formula errors often found in Excel.

    The skill requires a standard CSV export containing customer details, invoice numbers, due dates, and amounts; it is designed to work with any Dutch or international accounting software that can generate these raw data files.

    Yes, the skill is programmed to automatically net out credit notes and partial payments per customer, ensuring your aging report reflects the true outstanding balance.

    You receive the Python-driven processing logic, integration instructions for your AI agent, and a template for the required CSV structure to ensure immediate functionality.

    Once you have your AI agent active, you simply upload the skill configuration and point it to your invoice export file; the Python engine handles the heavy lifting without needing complex manual setup.

    The skill generates a summary report of total debt, a structured aging table, and a specialized 'deep-dive' list for items over 90 days to help you prioritize collections right away.

    New in Finance & Accounting