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财务计费运营

基于证据的收入、定价、退款、团队计费和计费模型真实工作流,适用于ECC。当用户需要销售快照、定价比较、重复收费诊断或基于代码的计费真相而非通用支付建议时使用。

财务计费运营 是什么?

该技能旨在为开发者或AI代理用户提供一个全面的计费运营工作流,专注于基于代码和数据的计费真相。它适用于需要了解收入状态、定价决策、团队计费逻辑或产品实际行为与营销宣传是否一致的场景。该技能支持ECC(企业计算核心)平台,并能整合多个子技能如客户计费、市场研究和GitHub操作,以获取竞争定价或代码实现证据。工作流从最新的计费证据开始,区分客户特定问题与产品真相,最终生成包含销售快照、诊断、产品差异和运营决策的输出。它内置了严格的防护措施,确保基于代码的真相声明与市场宣传分离,避免轻率结论。

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

财务计费运营 是做什么的?

当用户想要了解资金、定价、退款、团队席位逻辑,或者产品实际行为是否与网站和销售文案所述一致时,请使用此技能。

这比customer-billing-ops范围更广。那个技能用于客户补救。此技能用于运营真相:收入状态、定价决策、团队计费和基于代码的计费行为。

技能栈

在相关时,将这些ECC原生技能纳入工作流:

  • customer-billing-ops:用于客户特定的补救和后续跟进
  • research-ops:当竞争定价或当前市场证据重要时使用
  • market-research:当答案应以定价建议结尾时使用
  • github-ops:当计费真相取决于同级仓库中的代码、待办事项或发布状态时使用
  • verification-loop:当答案取决于证明结账、席位处理或权限行为时使用

使用时机

  • 用户询问Stripe销售、退款、MRR或近期客户活动
  • 用户询问团队计费、按席位计费或配额堆积在代码中是否真实存在
  • 用户想要竞争定价比较或定价模型基准
  • 问题混合了收入事实和产品实现真相

防护措施

  • 区分实时数据与保存的快照
  • 分离以下内容:
    • 收入事实
    • 客户影响
    • 基于代码的产品真相
    • 建议
  • 除非实际的权限路径强制执行,否则不要说“按席位”
  • 不要假设重复订阅意味着重复价值

工作流

1. 从最新的计费证据开始

优先使用实时计费数据。如果数据不是实时的,请明确说明快照时间戳。

标准化视图:

  • 已付销售
  • 活跃订阅
  • 失败或不完整的结账
  • 退款
  • 争议
  • 重复订阅

2. 将客户事件与产品真相分开

如果问题是客户特定的,首先分类:

  • 重复结账
  • 真实团队意图
  • 自助控制故障
  • 未满足的产品价值
  • 支付失败或设置不完整

然后将其与更广泛的产品问题分离:

  • 团队计费真的存在吗?
  • 席位是否实际被统计?
  • 结账数量是否改变权限?
  • 网站是否夸大了当前行为?

3. 检查基于代码的计费行为

如果答案取决于实现真相,请检查代码路径:

  • 结账
  • 定价页面
  • 权利计算
  • 席位或配额处理
  • 安装与用户使用逻辑
  • 计费门户或自助管理支持

4. 以决策和产品差距结束

报告:

  • 销售快照
  • 问题诊断
  • 产品真相
  • 推荐的运营操作
  • 产品或待办事项差距

输出格式

快照
- 时间戳
- 收入/订阅/异常

客户影响
- 谁受影响
- 发生了什么

产品真相
- 代码实际做什么
- 网站或销售文案声称什么

决策
- 退款/保留/转换/无操作

产品差距
- 需要构建或修复的确切后续项目

陷阱

  • 不要将失败的尝试与净收入混为一谈
  • 不要仅凭营销语言推断团队计费
  • 当有当前证据可用时,不要凭记忆比较竞争定价
  • 不要在没有对问题进行分类的情况下,从诊断直接跳到退款

验证

  • 答案包含实时数据声明或快照时间戳
  • 产品真相声明有代码支持
  • 客户影响与更广泛的定价/产品结论被清晰地分开

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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