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member-complaint-agent会员投诉 Agent

Agent Skill

member-complaint-agent 用于补充效率相关能力,适合在 OpenClaw 中需要让 Agent 承接效率相关任务时使用。可结合来源仓库、安装命令和原始 README 继续核验具体用法。安装前建议确认权限范围、维护状态,以及是否会触发联网、命令执行或文件读写。

总安装

4,823

周安装

203

GitHub Stars

公开资料未说明

下载量

1,689
OpenClaw

安装说明

本站只整理中文说明和来源信息,不托管安装包,也不代用户安装。

GitHub

来源数

2

许可证

MIT-0

最后核验

2026-05-01

来源状态

来源可访问

安装方式

通过对话安装

复制提示词发给支持本地命令或 Skills 的 AI 助手,先确认命令和权限,再让它执行。

请帮我安装这个 Agent Skill:member-complaint-agent(会员投诉 Agent)
来源仓库:https://github.com/qwqcode/member-complaint-agent
安装命令:
openclaw skills install member-complaint-agent
安装前请先检查当前环境是否支持对应 CLI,并向我确认将要执行的命令、安装目录、联网范围和文件读写权限;确认后再执行。

命令行安装

复制命令到本机终端执行。该命令会通过 OpenClaw 从第三方来源获取 Skill;本站只展示命令,不托管安装包,也不自动执行。

ClawHubOpenClaw
openclaw skills install member-complaint-agent

简介

member-complaint-agent 专注于会员投诉与客户服务的流程化处理。

  • 适用于处理客诉、退款、续费异常等客户服务场景的线性化操作模型。
  • 可自动生成客服草稿并根据风险等级进行分类处理建议。
  • 通过 clawhub 安装后可直接集成至 OpenClaw 进行任务分派与跟踪。
  • 使用前请核实其对接的实际业务系统及可能产生的外部通信行为。

SKILL.md

name
member-complaint-agent
description
Handle member/customer complaint workflows with a Linear-centered operating model. Use when the task involves 会员客诉、客户投诉、退款诉求、续费失败、自动续费争议、会员权益异常、客服草稿、风险分级、Linear issue 分析回写、日报/早报汇总, especially when complaint issues arrive from Linear and require structured analysis comments plus customer-reply drafts.

Member Complaint Agent

Overview

Turn complaint issues into a structured case analysis and a usable reply draft.

Default operating model for this skill:

  • treat Linear as the only action surface
  • treat Feishu or other chat channels as read-only display unless the user explicitly asks otherwise
  • parse deterministic metadata with rules first
  • use AI for intent, risk, tone, and drafting

Do not promise compensation, refunds, exceptions, or timelines unless the user provides the exact policy.

Linear-Centered Workflow

1. Intake from Linear issue

When the source is a Linear issue, extract these fields first if present:

  • issue id / issue url
  • raw customer message
  • member identifier
  • membership tier
  • metadata tags like platform, vendor, app version, device model, os version, product line
  • links to logs, profile page, or feedback page

Keep a clean separation between:

  • raw facts from the issue
  • deterministic parses from metadata
  • AI judgments

2. Deterministic metadata parse

Parse these with rules, not AI, whenever they are available in metadata:

  • platform: iOS / Android / other
  • vendor: Apple / OPPO / HONOR / Xiaomi / Vivo / Huawei / unknown
  • app version
  • device model
  • OS version
  • product line or package

Examples:

  • [PLUS会员][iOS][5.8.1(136138)][iPhone 12 Pro Max][26.2][plus]
  • [android][5.3.10 (1571, honor)][HONOR ANY-AN00][13 (33)][plus]

If metadata is ambiguous, say it is ambiguous instead of guessing.

3. Complaint analysis

Use AI for these judgments:

  • primary intent
  • subtype
  • emotion intensity
  • risk level
  • whether SOP should be referenced
  • whether escalation is needed
  • what missing information would improve handling

Use this v1 taxonomy unless the user provides a more specific business taxonomy:

  • refund-request
  • auto-renew-dispute
  • renewal-failure
  • membership-rights-issue
  • product-bug-or-function-failure
  • service-attitude-complaint
  • expectation-mismatch
  • other

Typical mappings:

  • 还是想退了 -> refund-request
  • 我的账号不能续费了 -> renewal-failure

4. SOP routing

When a complaint is channel-dependent, route by parsed platform/vendor before drafting:

  • iOS / Apple related purchase or refund issues -> Apple/iOS SOP
  • Android + vendor-specific billing/renewal issue -> vendor SOP when available
  • no SOP available -> say SOP not loaded and avoid inventing steps

Treat SOPs as authoritative only when the user has actually provided them.

5. Write back two Linear comments

Default output is two comments, not one.

Comment A: AI analysis comment

Use this structure:

【AI客诉分析】
- 客诉类型:
- 子意图:
- 情绪强度:低 / 中 / 高
- 风险等级:低 / 中 / 高 / 升级
- 渠道识别:
- 会员信息:
- 是否命中SOP:是 / 否 / 待确认
- 是否建议升级:是 / 否
- 判断依据:
  1.
  2.
  3.
- 待补充信息:
  1.
  2.

Comment B: customer reply draft

Use this structure:

【对客回复草稿】
您好,

...

【客服发送前检查】
- 需补充变量:
- 禁止承诺项:
- 建议时效:

Keep the customer draft short, calm, and directly usable by support staff.

6. Daily digest mode

When asked for a daily report / morning brief from complaint issues, summarize:

  • total issue count
  • intent distribution
  • platform/vendor distribution
  • unresolved issues older than 12 hours
  • high-risk issues
  • top recurring causes
  • ratio of refund / rights-related complaints if available

Do not fake metrics if the underlying issue list is incomplete.

Output Rules

Separate fact from judgment

Always label the difference between:

  • confirmed facts from issue content
  • inferred classification
  • recommended action

Prefer minimum-safe drafting

If the case touches refunds, legal risk, privacy, or public escalation:

  • acknowledge the issue
  • summarize what is known
  • recommend next step
  • avoid final commitments unless backed by policy

Guardrails

  • Do not invent refund policy or channel rules.
  • Do not say a refund will succeed unless a provided SOP explicitly supports that wording.
  • Do not turn ambiguous renewal problems into payment-fraud accusations.
  • Do not present metadata guesses as facts.
  • If the case mentions regulators, chargebacks, privacy, legal threats, or viral exposure, recommend human escalation.
  • If a required SOP is missing, say what is missing.

References

Read references/complaint-playbook.md for severity, tone, taxonomy notes, SOP-routing guidance, and reusable comment patterns.

适合场景

01

OpenClaw 用户查找和安装 Skill 时

02

用户想查找某类 Agent Skill 时

03

需要根据任务场景推荐可安装能力包时

04

需要对比不同来源的安装命令和来源信息时

能力概览

能力 1

按任务关键词查找相关 Skills

能力 2

展示可复制的安装命令

能力 3

保留来源站点、仓库和原始说明,方便继续核验

能力 4

补充不同宿主或平台的使用分布数据

能力 5

展示第三方安全扫描或审计结果

安装后应在对应宿主中按原始 README 的触发条件使用;具体调用方式请以来源页面和 README 为准。

平台分布

OpenClaw

90.64%
按下载量换算1,531

安全审计

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通过

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权限和风险

只读

该 Skill 主要提供规则、说明或参考内容,本身偏只读;真正读写文件、联网或执行命令仍取决于宿主 Agent 的任务。

安装前确认

本站仅展示第三方公开信息,不托管安装包,不提供自动安装或运行环境。安装前应自行审查源码、依赖和命令行为。当前只有一个来源,正式发布前建议补源仓库或其他目录站核验。

来源信息

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