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客戶計費運營

操作客戶計費工作流程,例如訂閱、退款、流失處理、計費入口恢復和方案分析,使用連結的計費工具(如 Stripe)。當使用者需要協助客戶、檢查訂閱狀態或管理影響收入的計費操作時使用。

客戶計費運營 是什麼?

這項技能旨在為開發者和運營人員提供一個標準化流程,以安全且高效地處理真實的客戶計費問題。它不僅適用於 Stripe 等連結計費工具,也涵蓋了從客戶身份識別、問題分類、最小破壞性操作到最終的運維交接的完整工作流。該技能強調區分重複購買、團隊購買、產品故障等不同情境,並優先使用自助服務入口而非自訂代碼,從而降低風險並提升客戶滿意度。通過遵循預定義的輸出格式,用戶可以確保每次互動都留下清晰的審計軌跡,並識別出產品或網站層面的缺口。

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

客戶計費運營 是做什麼的?

將此技能用於真實的客戶操作,而非通用的支付 API 設計。

目標是幫助操作者回答:這位客戶是誰、發生了什麼事、最安全的修復方法是什麼、以及我們應該發送什麼後續訊息。

何時使用

  • 客戶表示計費出問題、想要退款、或無法取消
  • 調查重複訂閱、意外收費、續約失敗或流失風險
  • 審查方案組合、活躍訂閱、年度與月度轉換、或團隊座位混淆
  • 建立或驗證計費入口流程
  • 審核涉及訂閱、發票、退款或付款方式的支援投訴

偏好的工具表面

  • 優先使用連結的計費工具,例如 Stripe
  • 僅將電子郵件、GitHub 或問題追蹤器用作輔助證據
  • 當平台已提供所需控制項時,優先使用託管的計費/客戶入口,而非自定義的帳戶管理程式碼

護欄

  • 切勿在回應中暴露密鑰、完整的信用卡詳細資訊或不必要的客戶個人識別資訊
  • 不要盲目退款;首先對問題進行分類
  • 區分以下情況:
    • 意外的重複購買
    • 有意的多座位或團隊購買
    • 產品故障 / 未實現價值
    • 結帳失敗或不完整
    • 因缺少自助服務控制項而取消
  • 對於年度方案、團隊方案和按比例計算的狀態,在採取行動前確認合約形式

工作流程

1. 清晰識別客戶

從可用的最強識別碼開始:

  • 客戶電子郵件
  • Stripe 客戶 ID
  • 訂閱 ID
  • 發票 ID
  • GitHub 使用者名稱或支援電子郵件(如果已知可對應到計費)

回傳簡潔的身份摘要:

  • 客戶
  • 活躍訂閱
  • 已取消訂閱
  • 發票
  • 明顯異常,例如重複的活躍訂閱

2. 對問題進行分類

在採取行動之前,先將案例歸入一個類別:

案例典型行動
重複的個人訂閱取消多餘的,考慮退款
真實的多座位/團隊意圖保留座位,澄清計費模式
付款失敗 / 結帳不完整透過入口恢復或更新付款方式
缺少自助服務控制項提供入口、取消路徑或發票存取權限
產品故障或信任破裂退款、道歉、記錄產品問題

3. 首先採取最安全且可逆的行動

優先順序:

  1. 恢復自助服務管理
  2. 修復重複或損壞的計費狀態
  3. 僅退款受影響的費用或重複項目
  4. 記錄原因
  5. 發送簡短的客戶後續訊息

如果修復需要產品工作,請分開處理:

  • 當前客戶補救措施
  • 待辦事項中的產品錯誤 / 工作流程缺口

4. 檢查操作者端的產品缺口

如果客戶的痛點來自於缺少操作者界面,請明確指出。常見範例:

  • 沒有計費入口
  • 沒有使用量/速率限制能見度
  • 沒有方案/座位說明
  • 沒有取消流程
  • 沒有重複訂閱防護

將這些視為 ECC 或網站後續項目,而不僅僅是支援事件。

5. 產生操作者交接文件

以以下內容結束:

  • 客戶狀態摘要
  • 已採取的行動
  • 收入影響
  • 要發送的後續訊息文字
  • 要建立的產品或待辦事項

輸出格式

使用此結構:

客戶
- 姓名 / 電子郵件
- 相關帳戶識別碼

計費狀態
- 活躍訂閱
- 發票或續約狀態
- 異常

決策
- 問題分類
- 為什麼這個行動是正確的

已採取的行動
- 退款 / 取消 / 入口 / 無動作

後續
- 簡短的客戶訊息

產品缺口
- 應在產品或網站中修復的內容

良好建議範例

  • 「正確的修復方法是計費入口,而不是自定義儀表板」
  • 「這看起來像是重複的個人結帳,而不是真實的團隊座位購買」
  • 「退還一筆重複費用,保留其餘活躍訂閱,然後在需要時稍後將客戶轉換為組織計費」

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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