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財務與帳單營運

基於證據的收入、定價、退款、團隊計費與帳單模型真相工作流程。當用戶需要銷售快照、定價比較、重複收費診斷或程式碼支援的帳單事實,而非一般付款建議時使用。

財務與帳單營運 是什麼?

此技能為開發者與AI代理提供一個嚴謹的工作流程,用於處理與金錢相關的真實業務問題,包括收入、定價、退款和團隊計費。它不僅處理單一客戶的補救措施,更專注於揭露產品定價、收入狀態和團隊計費功能的「營運真相」。此技能會直接整合ECC生態系中的其他技能,例如檢索即時帳單資料、進行競爭對手定價研究、以及檢查程式碼庫以驗證產品行為與網站或銷售文案的宣稱是否一致。其核心工作流程要求從最新的帳單證據開始,並嚴格區分客戶意外事件與產品本身的程式碼實作真相。最終輸出格式包含銷售快照、客戶影響、產品真相、決策建議和產品缺口分析,確保如「按席位計費」等說法必須有實際的授權程式碼路徑支援,避免基於市場行銷語言做出不準確的推論。此技能支援Claude Code、Cursor、Codex等代理平台,並內建檢查清單以確保答案基於即時數據或明確的快照時間戳。

相容平台~Claude Code~Codex CLI~Cursor
npx skills add https://github.com/affaan-m/everything-claude-code/tree/main/skills/finance-billing-ops

在你喜歡的 AI 中提問

開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

財務與帳單營運 是做什麼的?

當用戶想要了解金錢、定價、退款、團隊席位邏輯,或者產品是否真的如網站和銷售文案所述時,請使用此技能。

此技能的範圍比 customer-billing-ops 更廣泛。那個技能是針對客戶補救措施。此技能是為了營運真相:收入狀態、定價決策、團隊計費,以及程式碼支援的帳單行為。

技能組合

在相關時將這些 ECC 原生技能納入工作流程:

  • customer-billing-ops 用於客戶特定的補救措施和後續追蹤
  • research-ops 當競爭對手定價或當前市場證據很重要時
  • market-research 當答案應以定價建議作結時
  • github-ops 當帳單真相取決於兄弟儲存庫中的程式碼、待辦事項或版本狀態時
  • verification-loop 當答案取決於驗證結帳、席位處理或權益行為時

使用時機

  • 用戶詢問 Stripe 銷售、退款、MRR 或近期客戶活動
  • 用戶詢問團隊計費、按席位計費或配額疊加在程式碼中是否真實存在
  • 用戶想要競爭對手定價比較或定價模型基準
  • 問題混合了收入事實與產品實作真相

護欄

  • 區分即時數據與已儲存快照
  • 分離:
    • 收入事實
    • 客戶影響
    • 程式碼支援的產品真相
    • 建議
  • 除非實際的權限路徑確實強制執行,否則不要說「按席位」
  • 不要假設重複訂閱代表重複價值

工作流程

1. 從最新的帳單證據開始

優先使用即時帳單數據。如果數據不是即時的,請明確標註快照時間戳。

標準化畫面:

  • 已完成銷售
  • 活躍訂閱
  • 失敗或不完整的結帳
  • 退款
  • 爭議
  • 重複訂閱

2. 將客戶意外事件與產品真相分離

如果問題是客戶特定的,首先進行分類:

  • 重複結帳
  • 真實團隊意圖
  • 中斷的自助服務控制
  • 未達到的產品價值
  • 付款失敗或設定不完整

然後將其與更廣泛的產品問題分離:

  • 團隊計費真的存在嗎?
  • 席位真的有在計算嗎?
  • 結帳數量會改變權益嗎?
  • 網站是否誇大了當前行為?

3. 檢查程式碼支援的帳單行為

如果答案取決於實作真相,請檢查程式碼路徑:

  • 結帳
  • 定價頁面
  • 權益計算
  • 席位或配額處理
  • 安裝 vs. 用戶使用邏輯
  • 帳單入口或自助服務管理支援

4. 以決策和產品缺口作結

報告:

  • 銷售快照
  • 問題診斷
  • 產品真相
  • 建議的營運行動
  • 產品或待辦事項缺口

輸出格式

快照
- 時間戳
- 收入 / 訂閱 / 異常

客戶影響
- 受影響的對象
- 發生什麼事

產品真相
- 程式碼實際做了什麼
- 網站或銷售文案宣稱了什麼

決策
- 退款 / 保留 / 轉換 / 無動作

產品缺口
- 需要建構或修復的確切後續項目

常見陷阱

  • 不要將失敗的嘗試與淨收入混為一談
  • 不要僅從行銷用語推斷團隊計費
  • 當有當前證據可用時,不要僅憑記憶比較競爭對手定價
  • 不要未經問題分類就直接從診斷跳到退款

驗證

  • 答案包含即時數據陳述或快照時間戳
  • 產品真相的主張有程式碼支援
  • 客戶影響與更廣泛的定價/產品結論已乾淨分離

Individual skills in this repo

This repo contains 20 individual skills — each has its own dedicated page.

accessibility

Design, implement, and audit inclusive digital products using WCAG 2.2 Level AA. Use when building or auditing UI that must meet WCAG 2.2 Level AA, or when reviewing a change for keyboard, contrast, or screen-reader support.

affaan-m/claude-api

Anthropic Claude API patterns for Python and TypeScript. Covers Messages API, streaming, tool use, vision, extended thinking, batches, prompt caching, and Claude Agent SDK. Use when building applications with the Claude API or Anthropic SDKs.

affaan-m/everything-claude-code

End-to-end marketing campaign planning and execution. Covers audience research, positioning, campaign angle definition, landing page copy, email sequences, social posts, ad copy, short-form video scripts, and content calendars. Use as the orchestration layer for multi-channel product launches. Use when planning or executing a multi-channel product launch, or producing landing page, email, social, or ad copy.

affaan-m/everything-claude-code

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/everything-claude-code-conventions

Development conventions and patterns for everything-claude-code. JavaScript project with conventional commits.

affaan-m/frontend-design

Create distinctive, production-grade frontend interfaces with high design quality. Use when the user asks to build web components, pages, or applications and the visual direction matters as much as the code quality.

affaan-m/gget

gget CLI and Python workflow for quick genomic database queries, sequence lookup, BLAST-style searches, enrichment checks, and reproducible bioinformatics evidence logs.

affaan-m/literature-review

Systematic literature-review workflow for academic, biomedical, technical, and scientific topics, including search planning, source screening, synthesis, citation checks, and evidence logging.

affaan-m/motion-ui

Production-ready UI motion system for React/Next.js. Use when implementing animations, transitions, or motion patterns.

affaan-m/project-guidelines-example

Example project-specific skill template based on a real production application.

affaan-m/pubmed-database

Direct PubMed and NCBI E-utilities search workflows for biomedical literature, MeSH queries, PMID lookup, citation retrieval, and API-backed literature monitoring.

affaan-m/scholar-evaluation

Structured scholarly-work evaluation for papers, proposals, literature reviews, methods sections, evidence quality, citation support, and research-writing feedback.

affaan-m/uspto-database

USPTO patent and trademark data workflow for official record lookup, PatentSearch queries, TSDR checks, assignment data, and reproducible IP research logs.

agent-architecture-audit

Full-stack diagnostic for agent and LLM applications. Audits the 12-layer agent stack for wrapper regression, memory pollution, tool discipline failures, hidden repair loops, and rendering corruption. Produces severity-ranked findings with code-first fixes. Essential for developers building agent applications, autonomous loops, or any LLM-powered feature. Use when an agent or LLM feature misbehaves and the failing layer is unknown, or before shipping an agent stack.

agent-eval

Head-to-head comparison of coding agents (Claude Code, Aider, Codex, etc.) on custom tasks with pass rate, cost, time, and consistency metrics. Use when choosing between coding agents, or when a change to an agent setup needs measured pass rate, cost, and time rather than an impression.

agent-harness-construction

Design and optimize AI agent action spaces, tool definitions, and observation formatting for higher completion rates. Use when defining or revising an agent

agentic-engineering

Operate as an agentic engineer using eval-first execution, decomposition, and cost-aware model routing. Use when planning or executing engineering work that agents will carry out end to end.

agentic-os

Build persistent multi-agent operating systems on Claude Code. Covers kernel architecture, specialist agents, slash commands, file-based memory, scheduled automation, and state management without external databases. Use when building a persistent multi-agent system on Claude Code with its own memory, commands, and scheduling.

agent-introspection-debugging

Structured self-debugging workflow for AI agent failures using capture, diagnosis, contained recovery, and introspection reports. Use when an agent run fails and you need a reproducible diagnosis instead of a retry.

agent-payment-x402

Add x402 payment execution to AI agents with per-task budgets, spending controls, and non-custodial wallets. Supports Base through agentwallet-sdk and X Layer through OKX Payments / OKX Agent Payments Protocol. Use when an agent must pay for something itself and needs per-task budgets, spending controls, and a non-custodial wallet.

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