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客户计费运营

操作客户计费工作流,例如订阅、退款、流失排查、计费门户恢复和套餐分析,通过连接的计费工具(如 Stripe)实现。当用户需要帮助客户、检查订阅状态或管理影响收入的计费操作时使用。

客户计费运营 是什么?

此技能专注于真实的客户计费运营,而非通用的支付 API 设计。其目标是帮助操作员回答:这个客户是谁,发生了什么,最安全的修复是什么,以及应发送什么后续消息。技能支持通过 Stripe 等连接的计费工具进行操作,并定义了清晰的使用场景,包括调研重复订阅、意外收费、续费失败或流失风险,以及创建或验证计费门户流程。它包含了严格的护栏,例如不暴露密钥或完整卡号,不盲目退款,并要求在操作前通过表格(如重复个人订阅与真实团队购买)对问题进行分类。工作流程分为五步:识别客户、分类问题、采取最安全的可逆操作、检查操作端产品缺口,以及生成包含客户状态摘要、操作记录、收入影响和后续文本的操作移交。输出采用标准化结构,包括客户、计费状态、决策、操作、后续和产品缺口部分,有助于操作员快速记录并升级问题。

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

在你喜欢的 AI 中提问

打开一个已预加载此 Agent Skill 的新对话。

文档

客户计费运营 是做什么的?

将此技能用于真实的客户操作,而非通用的支付 API 设计。

目标是帮助操作员回答:这个客户是谁,发生了什么,最安全的修复是什么,以及应发送什么后续消息。

何时使用

  • 客户反馈计费出错、希望退款或无法取消
  • 调研重复订阅、意外收费、续费失败或流失风险
  • 审查套餐组合、活跃订阅、年度与月度转换率或团队席位混淆
  • 创建或验证计费门户流程
  • 审计涉及订阅、发票、退款或支付方式的支持投诉

首选工具界面

  • 优先使用连接的计费工具,如 Stripe
  • 仅将电子邮件、GitHub 或问题跟踪器作为辅助证据
  • 当平台已提供所需控制时,优先使用托管的计费/客户门户,而非自定义账户管理代码

护栏

  • 绝不在响应中暴露密钥、完整卡号或不必要的客户 PII
  • 不要盲目退款;首先对问题进行分类
  • 区分以下情况:
    • 意外的重复购买
    • 有意的多席位或团队购买
    • 产品故障 / 价值未达到
    • 失败或不完整的结账
    • 因缺少自助控制而取消
  • 对于年度套餐、团队套餐和按比例分配的状态,在采取行动前验证合同形式

工作流程

1. 清晰地识别客户

从最强的可用标识符开始:

  • 客户电子邮件
  • Stripe 客户 ID
  • 订阅 ID
  • 发票 ID
  • GitHub 用户名或支持电子邮件(若已知可映射回计费)

返回简洁的身份摘要:

  • 客户
  • 活跃订阅
  • 已取消订阅
  • 发票
  • 明显异常,例如重复的活跃订阅

2. 对问题进行分类

在采取行动前将案例归入一个类别:

案例典型操作
重复的个人订阅取消多余订阅,考虑退款
真实的多席位/团队意图保留席位,澄清计费模式
付款失败 / 不完整的结账通过门户恢复或更新支付方式
缺少自助控制提供门户、取消路径或发票访问权限
产品故障或信任破裂退款、道歉、记录产品问题

3. 首先采取最安全的可逆操作

首选顺序:

  1. 恢复自助管理
  2. 修复重复或异常的计费状态
  3. 仅退款受影响的费用或重复项
  4. 记录原因
  5. 发送简短的客户后续消息

如果修复需要产品工作,请分离:

  • 当前客户补救措施
  • 将产品缺陷 / 工作流缺口放入待办事项

4. 检查操作端产品缺口

如果客户问题源于缺失的操作界面,请明确指出来。常见示例:

  • 没有计费门户
  • 没有使用量/速率限制可见性
  • 没有套餐/席位说明
  • 没有取消流程
  • 没有重复订阅防护

将这些视为 ECC 或网站后续项,而不仅仅是支持事件。

5. 生成操作移交

以以下内容结束:

  • 客户状态摘要
  • 采取的操作
  • 收入影响
  • 要发送的后续文本
  • 需要创建的产品或待办事项问题

输出格式

使用以下结构:

客户
- 姓名 / 电子邮件
- 相关账户标识符

计费状态
- 活跃订阅
- 发票或续费状态
- 异常

决策
- 问题分类
- 为什么此操作是正确的

采取的操作
- 退款 / 取消 / 门户 / 无操作

后续
- 简短的客户消息

产品缺口
- 应在产品或网站中修复的内容

良好建议示例

  • “正确的修复是计费门户,而不是自定义仪表板”
  • “这看起来是重复的个人结账,而不是真正的团队席位购买”
  • “退还一笔重复费用,保留剩余的活跃订阅,然后如果需要,稍后将客户转换为组织计费”

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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