菜单
When the user wants to match disparate financial data sources (e.g., bank statements vs. general ledger) at scale using AI. Also use when the user mentions "bank rec," "matching invoices," "reconciling payments," "fuzzy matching in finance," or "automated accounting."
用 Codex 或 Claude 帮你安装 复制这段 Prompt，粘贴到 Codex、Claude 或其他助手里，让它检查 Skill 页面并帮你完成安装。
基于 SOC 职业分类
| name | automated-reconciliation |
| description | When the user wants to match disparate financial data sources (e.g., bank statements vs. general ledger) at scale using AI. Also use when the user mentions "bank rec," "matching invoices," "reconciling payments," "fuzzy matching in finance," or "automated accounting." |
| metadata | {"version":"1.0.0"} |
You are an Accounting Systems Architect. Your goal is to eliminate manual data entry by using fuzzy matching and AI to reconcile thousands of transactions in seconds.
The Data Sources
The "Match" Definition
LLMs are not good at matching 50,000 rows.
For large datasets, this skill uses Python libraries like pandas and RecordLinkage. LLMs are used to resolve the "Ambiguous Matches" (the 5% the code can't solve).
The Results
The Exceptions
Journal Entry Suggestions
python3 scripts/calculate.py to self-test; import the functions for actual computations.When the user wants to pay off debt, compare the avalanche vs. snowball strategy, model a payoff timeline, calculate total interest paid, or find the fastest way out of debt. Also use when the user mentions "credit card debt," "student loans," "debt free," "minimum payments," "how long to pay off," or "which debt first."
When the user wants to calculate their net worth, track assets and liabilities over time, measure financial progress, analyse asset allocation, or set a net worth milestone. Also use when the user mentions "what am I worth," "assets vs debts," "balance sheet," "financial snapshot," or "am I building wealth."
When the user wants to create a personal budget, track income vs. expenses, apply the 50/30/20 rule, build a zero-based budget, improve their savings rate, or understand where their money is going. Also use when the user mentions "I'm overspending," "I can't save money," "monthly budget," "budget categories," or "living paycheck to paycheck."
When the user wants to plan for retirement, calculate how much to save, project a retirement nest egg, determine a safe withdrawal amount, or figure out whether they are on track. Also use when the user mentions "401k," "pension," "retirement age," "how much do I need to retire," "compound interest," "FIRE," or "retire early."
When the user wants to invest in a tax-efficient way, maximise contributions to tax-advantaged accounts, understand the tax drag on investment returns, compare traditional vs. Roth accounts, or determine the optimal account to hold specific asset classes. Also use when the user mentions "401k vs Roth," "ISA allowance," "tax on investments," "asset location," "tax-loss harvesting," or "after-tax returns."
When the user wants to use machine learning to detect fraud, errors, or unusual patterns in high-volume financial data. Also use when the user mentions "ML fraud detection," "unsupervised learning for audit," "isolation forest," "autoencoders for finance," "unusual transaction clusters," or "automated expense auditing."