Receivable Aging Analyzer

Provides structured receivable aging analysis, best-practice frameworks, and actionable recommendations for prioritizing collections without executing transa...

Overview

Analyzes accounts receivable aging and suggests prioritization strategies for collections. This is a descriptive skill that provides templates, frameworks, and heuristic analysis without executing real code, accessing external APIs, or performing actual financial transactions.

Trigger

Use this skill when the user wants to:

• get structured guidance on receivable aging analyzer
• apply best-practice frameworks to their financial situation
• generate templates and checklists for financial planning

Example prompts

• "Help me create a personal budget"
• "Analyze my cash flow forecast"
• "Check my expense categorization"
• "Assess my financial health"
• "Review my receivables aging"
• "Check invoice compliance"
• "Develop a pricing strategy"
• "Find cost reduction opportunities"
• "Optimize my tax deductions"
• "Interpret my financial reports"

Workflow

1. User provides context and goals.
2. Skill applies built-in templates and frameworks.
3. Skill generates structured output with recommendations.
4. User receives actionable insights and next steps.

Inputs

• User context (financial situation, goals, constraints)
• Optional data inputs (amounts, categories, timeframes)
• Analysis preferences

Outputs

• Structured analysis report
• Templates and frameworks
• Actionable recommendations
• Next steps checklist

Usage Scenarios

1. User input: "Analyze our AR aging: 30% is over 60 days. How do we prioritize collections?" → Expected output: Tiered collection strategy — 0-30 days (automated reminders), 31-60 (personal outreach + payment plans), 61-90 (escalation + late fees), 90+ (collections/legal review) — with expected recovery rates by tier and weekly action plan for top 10 delinquent accounts.
2. User input: "Our DSO has climbed from 35 to 52 days. Diagnose the root cause and fix it." → Expected output: DSO decomposition analysis — identifies that 60% of increase is from 3 large accounts with extended terms, 25% from invoice dispute delays, 15% from slow small accounts — with corrective actions per root cause.
3. User input: "Design a proactive credit control system to prevent receivable aging issues." → Expected output: Credit control framework — customer credit assessment checklist, credit-limit policy, payment-term tiering (Net 15/30/45 based on risk), early-payment discount structure, automated dunning schedule, and monthly AR health dashboard template.

Scenario 2: 小老板追款讨债指南

User input: "我自己开了个小公司，客户拖账拖了半年，现在欠我40多万。每次打电话都说下周付，下周永远不来。怎么系统性地处理这笔应收款？" Expected output: 小微企业应收账款催收体系——第一步：账龄分级（30天以内电话提醒、60天微信正式催、90天发律师函、120天起诉准备）；第二步：催收话术剧本（第一通电话温和确认是否收到发票、第二通问付款障碍、第三通提供分期方案、第四通最后通牒）；第三步：证据链整理（合同+送货单+微信聊天记录录屏+发票+催收记录，装订成册）；第四步：催收外包方案（在催收平台如资产360挂单，催回按比例收费）。关键：所有沟通留痕，对微信记录定期截图保存。

Safety

• No real financial transactions or API calls
• No access to real bank accounts or financial systems
• Recommendations are informational only
• Users should consult financial professionals for actual decisions

Acceptance Criteria

• Must return structured markdown/JSON output
• Must include actionable recommendations
• Must clearly state the descriptive/non-executable nature
• Must provide templates or frameworks for user adaptation