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財務與帳單運營

以證據為基礎的營收、定價、退款、團隊帳單與帳單模型真相工作流程,適用於 ECC。當使用者需要銷售快照、定價比較、重複收費診斷或基於程式碼的帳單現實,而非一般付款建議時使用。

財務與帳單運營 是什麼?

此技能專為 AI 代理(如 Claude Code、Cursor 或 Codex)設計,用於處理 ECC 平台中關於金錢、定價、退款及團隊座位邏輯的真實查詢。它超越了單純的客戶帳單運營,聚焦於「運營者真相」:即營收狀態、定價決策、團隊帳單與程式碼驗證的帳單行為。技能支持多種平台(包括 ECC 原生技能如 research-ops、github-ops 和 verification-loop),並強調從最新即時數據出發,嚴格區分營收事實、客戶影響、產品真相與建議。工作流程涵蓋從獲取最新帳單證據、分類客戶事件與產品問題、檢查程式碼實現路徑,到最終輸出包含銷售快照、問題診斷、產品真相、決策與產品差距的結構化報告。此技能有助於避免常見陷阱,如將失敗記錄與淨營收混淆,或僅憑行銷語言推斷團隊帳單功能。

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

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開啟一個已預先載入此 Agent Skill 的新對話。

說明文件

財務與帳單運營 是做什麼的?

當使用者想要了解金錢、定價、退款、團隊座位邏輯,或產品實際行為是否與網站和銷售文案所述一致時,請使用此技能。

此技能範圍比 customer-billing-ops 更廣。後者專注於客戶補救,而本技能專注於運營者真相:營收狀態、定價決策、團隊帳單與程式碼驗證的帳單行為。

技能棧

在相關時將這些 ECC 原生技能引入工作流程:

  • customer-billing-ops:用於客戶特定的補救與後續追蹤
  • research-ops:當競爭對手定價或當前市場證據重要時
  • 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、待辦事項或發布狀態時
  • verification-loop:當答案取決於驗證結帳、座位處理或權利行為時

使用時機

  • 使用者詢問 Stripe 銷售額、退款、MRR 或近期客戶活動
  • 使用者詢問團隊帳單、按座位計費或配額疊加在程式碼中是否真實存在
  • 使用者想要競爭對手定價比較或定價模型基準
  • 問題同時涉及營收事實與產品實現真相

護欄

  • 區分即時數據與已儲存快照
  • 分離:
    • 營收事實
    • 客戶影響
    • 程式碼驗證的產品真相
    • 建議
  • 除非實際權利路徑強制執行,否則不要說「按座位」
  • 不要假設重複訂閱即代表重複價值

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。若數據非即時,需明確說明快照時間戳記。

標準化圖像:

  • 已付款銷售
  • 活躍訂閱
  • 失敗或不完整的結帳
  • 退款
  • 爭議
  • 重複訂閱

2. 分離客戶事件與產品真相

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
  • 自助服務控制失效
  • 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:

  • 團隊帳單是否真實存在?
  • 座位是否實際被計算?
  • 結帳數量是否改變權利?
  • 網站是否誇大當前行為?

3. 檢查程式碼驗證的帳單行為

如果答案取決於實現真相,請檢查程式碼路徑:

  • 結帳
  • 定價頁面
  • 權利計算
  • 座位或配額處理
  • 安裝 vs 使用者使用邏輯
  • 帳單入口或自助管理支援

4. 以決策和產品差距作結

報告:

  • 銷售快照
  • 問題診斷
  • 產品真相
  • 建議的運營者行動
  • 產品或待辦事項差距

輸出格式

快照
- 時間戳記
- 營收 / 訂閱 / 異常

客戶影響
- 受影響的人
- 發生的事件

產品真相
- 程式碼實際行為
- 網站或銷售文案的聲明

決策
- 退款 / 保留 / 轉換 / 不操作

產品差距
- 需建置或修復的準確後續項目

陷阱

  • 不要將失敗記錄與淨營收混淆
  • 不要僅從行銷語言推斷團隊帳單
  • 在有當前證據可用時,不要憑記憶比較競爭對手定價
  • 不要未經分類問題就直接從診斷跳到退款

驗證

  • 答案包含即時數據聲明或快照時間戳記
  • 產品真相的主張有程式碼驗證
  • 客戶影響與更廣泛的定價/產品結論已清晰分離

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/content-engine

Create platform-native content systems for X, LinkedIn, TikTok, YouTube, newsletters, and repurposed multi-platform campaigns. Use when the user wants social posts, threads, scripts, content calendars, or one source asset adapted cleanly across platforms.

affaan-m/ECC

Multi-platform content distribution across X, LinkedIn, Threads, and Bluesky. Adapts content per platform using content-engine patterns. Never posts identical content cross-platform. Use when the user wants to distribute content across social platforms.

affaan-m/fal-ai-media

Unified media generation via fal.ai MCP — image, video, and audio. Covers text-to-image (Nano Banana), text/image-to-video (Seedance, Kling, Veo 3), text-to-speech (CSM-1B), and video-to-audio (ThinkSound). Use when the user wants to generate images, videos, or audio with AI.

affaan-m/manim-video

日本語翻訳:このファイルは manim-video 用の日本語翻訳が必要です

affaan-m/remotion-video-creation

Remotion のベストプラクティス - React で動画を作成する。3D、アニメーション、音声、字幕、チャート、トランジションなどをカバーするドメイン固有の29のルール。

affaan-m/video-editing

AI-assisted video editing workflows for cutting, structuring, and augmenting real footage. Covers the full pipeline from raw capture through FFmpeg, Remotion, ElevenLabs, fal.ai, and final polish in Descript or CapCut. Use when the user wants to edit video, cut footage, create vlogs, or build video content.

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.

agent-self-evaluation

Use after completing any non-trivial task. The agent self-rates its output on 5 axes — accuracy, completeness, clarity, actionability, conciseness — with concrete evidence per criterion. Produces a structured 1-5 scorecard with specific improvement suggestions.

agent-sort

Build an evidence-backed ECC install plan for a specific repo by sorting skills, commands, rules, hooks, and extras into DAILY vs LIBRARY buckets using parallel repo-aware review passes. Use when ECC should be trimmed to what a project actually needs instead of loading the full bundle.

ai-first-engineering

Engineering operating model for teams where AI agents generate a large share of implementation output. Use when setting team process, review gates, or ownership rules for a codebase largely written by agents.

ai-regression-testing

Regression testing strategies for AI-assisted development. Sandbox-mode API testing without database dependencies, automated bug-check workflows, and patterns to catch AI blind spots where the same model writes and reviews code. Use when adding regression coverage to AI-assisted code, or when the same model both wrote and reviewed a change.

android-clean-architecture

Clean Architecture patterns for Android and Kotlin Multiplatform projects — module structure, dependency rules, UseCases, Repositories, and data layer patterns. Use when structuring modules, layers, or data flow in an Android or KMP project.

angular-developer

Generates Angular code and provides architectural guidance. Trigger when creating projects, components, or services, or for best practices on reactivity (signals, linkedSignal, resource), forms, dependency injection, routing, SSR, accessibility (ARIA), animations, styling (component styles, Tailwind CSS), testing, or CLI tooling.

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