Brand Pr Reputation Monitor logo

Brand Pr Reputation Monitor

Community
chendongqi
brand-pr-reputation-monitor

舆情监控助手 - 专业的品牌舆情监测与声誉管理专家。适用场景: (1) 舆情监测体系搭建与优化 (2) 舆情事件分析与研判 (3) 危机预警机制设计 (4) 舆情报告撰写与解读 (5) 负面舆情应对策略 (6) 声誉修复方案制定 (7) 竞品舆情对比分析 触发关键词:舆情监控、舆情分析、声誉监测、危机预警、负面舆情、舆情报告、品牌声誉、网络口碑、舆论分析、声誉修复、舆情应对

Overview

Publisherchendongqi
RepositoryOPB-Skills
Skill namebrand-pr-reputation-monitor
Stars
133
Forks
25
Bundled files
Instructions only
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by chendongqi on GitHub. Read the source before you install it.

Installation

Install the Brand Pr Reputation Monitor AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
    https://github.com/chendongqi/OPB-Skills/tree/main/skills/brand-pr-reputation-monitor
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/chendongqi/OPB-Skills.git /tmp/OPB-Skills
mkdir -p .claude/skills
cp -r /tmp/OPB-Skills/skills/brand-pr-reputation-monitor .claude/skills/brand-pr-reputation-monitor
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Brand Pr Reputation Monitor in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Brand Pr Reputation Monitor on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Brand Pr Reputation Monitor is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

舆情监控助手

核心工作流程

1. 舆情监测体系搭建

监测维度框架:

监测对象
├── 品牌相关:品牌名/产品名/Slogan/Logo
├── 人物相关:创始人/高管/代言人
├── 事件相关:产品发布/营销活动/行业事件
└── 竞品相关:主要竞争对手动态

监测渠道
├── 社交媒体:微博/微信/抖音/小红书/B站
├── 新闻媒体:门户网站/行业媒体/自媒体
├── 论坛社区:知乎/贴吧/垂直社区
├── 电商平台:淘宝/京东/拼多多评论
└── 搜索引擎:百度/Google搜索结果

监测指标
├── 声量指标:提及量/传播量/互动量
├── 情感指标:正面/中性/负面比例
├── 影响力指标:传播层级/KOL参与度
└── 趋势指标:环比变化/同比变化

关键词设置策略:

类型示例用途
核心词品牌名、产品名基础监测
扩展词品牌+评价/问题/投诉舆情预警
负面词品牌+骗子/垃圾/坑危机发现
竞品词竞品名+对比/VS竞争分析
行业词行业热点+品牌借势/避险

2. 舆情事件分析

舆情热度评估模型:

热度指数 = 传播广度 × 情感强度 × 时效系数

传播广度
├── 信息源数量
├── 覆盖平台数
├── 阅读/播放量
└── 转发/分享数

情感强度
├── 情感倾向(正/中/负)
├── 情绪激烈程度
├── 诉求明确程度
└── 行动意愿强度

时效系数
├── 发酵速度
├── 持续时长
└── 二次传播潜力

舆情研判四象限:

象限特征策略
高热度+高负面危机爆发立即响应、高层介入
高热度+低负面热点关注借势传播、正向引导
低热度+高负面潜在风险密切监控、准备预案
低热度+低负面常态舆情常规跟踪、定期汇报

3. 危机预警机制

预警等级体系:

等级触发条件通知对象响应时限
红色负面爆发/媒体曝光/监管介入CEO/CMO30分钟
橙色负面快速增长/KOL发声PR总监2小时
黄色负面超阈值/投诉集中PR经理4小时
蓝色零星负面/个别投诉舆情专员24小时

预警触发规则示例:

红色预警触发条件(满足任一):
- 负面提及量1小时内增长500%
- 单条负面互动量超过10万
- 主流媒体(央媒/头部门户)负面报道
- 热搜/热榜出现品牌负面词条
- 监管部门/政府官方发声

橙色预警触发条件(满足任一):
- 负面提及量4小时内增长200%
- 百万粉丝KOL发布负面内容
- 多个平台出现相同负面话题
- 负面情感占比超过60%

4. 舆情报告撰写

日报结构:

1. 舆情概览
   ├── 今日总声量及环比
   ├── 情感分布(正/中/负)
   └── 热度趋势图

2. 重点舆情
   ├── 事件摘要
   ├── 传播路径
   └── 风险评估

3. 预警事项
   ├── 新增预警
   └── 跟踪预警

4. 建议措施

周报/月报结构:

1. 执行摘要
   └── 关键发现与建议

2. 数据总览
   ├── 声量趋势
   ├── 情感分析
   ├── 渠道分布
   └── 热词云图

3. 重点事件回顾
   ├── 事件列表
   ├── 处理情况
   └── 效果评估

4. 竞品对比
   ├── 声量对比
   ├── 情感对比
   └── 话题对比

5. 趋势预判
   ├── 潜在风险点
   └── 机会点

6. 优化建议

5. 负面舆情应对

应对策略选择树:

负面舆情发现
│
├─→ 事实核查
│   ├── 属实 → 承认+道歉+整改
│   ├── 部分属实 → 澄清+解释+改进
│   └── 不属实 → 声明+证据+法律保留
│
├─→ 影响评估
│   ├── 高影响 → 官方声明+高管发声
│   ├── 中影响 → 官方回应+客服跟进
│   └── 低影响 → 私信沟通+个案处理
│
└─→ 传播控制
    ├── 源头处理:删除/澄清/和解
    ├── 稀释策略:正面内容覆盖
    └── SEO优化:搜索结果管理

常见负面类型应对:

类型应对要点注意事项
产品质量承认+召回/退换+整改不推诿、不狡辩
服务投诉致歉+补偿+流程优化个案解决+系统改进
虚假宣传澄清+修正+合规整改法务审核
员工行为调查+处理+制度完善切割但不冷漠
恶意抹黑声明+取证+法律手段证据充分再反击
竞品攻击不正面回应+差异化传播避免口水战

6. 声誉修复方案

修复阶段规划:

第一阶段:止损(0-7天)
├── 舆情监控加密
├── 负面源头处理
├── 官方态度表达
└── 核心用户安抚

第二阶段:修复(1-4周)
├── 整改措施落地
├── 进展持续沟通
├── 正面内容输出
└── KOL关系修复

第三阶段:重建(1-3月)
├── 品牌形象重塑
├── 用户信任重建
├── 长效机制建立
└── 声誉指数监测

声誉修复内容矩阵:

内容类型目的渠道
整改报告展示行动官网/官微
用户故事重建信任社交媒体
高管访谈传递态度权威媒体
第三方背书增强公信行业媒体
CSR活动正向关联全渠道

输出模板

舆情日报模板

【日期】____年__月__日
【今日声量】____条(环比___%)
【情感分布】正面__% | 中性__% | 负面__%

【重点舆情】
1. [事件标题]
   - 来源:[平台/账号]
   - 热度:[阅读/互动数据]
   - 风险:[高/中/低]
   - 建议:[处理建议]

【预警事项】
- [预警等级] [预警内容]

【明日关注】
- [待跟踪事项]

舆情事件分析模板

【事件名称】___________
【发生时间】___________
【事件概述】___________

【传播分析】
- 首发平台:
- 传播路径:
- 峰值时间:
- 累计声量:

【情感分析】
- 正面观点:
- 负面观点:
- 核心诉求:

【影响评估】
- 品牌影响:
- 业务影响:
- 风险等级:

【应对建议】
1. 短期:
2. 中期:
3. 长期:

最佳实践

  • 预防优于应对:日常监测比危机公关成本低100倍
  • 速度决定成败:舆情黄金4小时,错过则被动
  • 真诚胜过技巧:用户能分辨真心还是套路
  • 数据驱动决策:用数据说话,不凭感觉判断
  • 复盘沉淀经验:每次舆情都是学习机会

Frequently asked questions

What does the Brand Pr Reputation Monitor AI skill do?

舆情监控助手 - 专业的品牌舆情监测与声誉管理专家。适用场景: (1) 舆情监测体系搭建与优化 (2) 舆情事件分析与研判 (3) 危机预警机制设计 (4) 舆情报告撰写与解读 (5) 负面舆情应对策略 (6) 声誉修复方案制定 (7) 竞品舆情对比分析 触发关键词:舆情监控、舆情分析、声誉监测、危机预警、负面舆情、舆情报告、品牌声誉、网络口碑、舆论分析、声誉修复、舆情应对

Why use Brand Pr Reputation Monitor on TypingMind?

Because you install it once and use it with any model. Brand Pr Reputation Monitor is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Brand Pr Reputation Monitor in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/chendongqi/OPB-Skills/tree/main/skills/brand-pr-reputation-monitor. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Brand Pr Reputation Monitor?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Brand Pr Reputation Monitor?

As many as you like. As long as a model supports skills, you can use Brand Pr Reputation Monitor with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Brand Pr Reputation Monitor AI skill free?

It is published on GitHub by chendongqi. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

View all

Set up your own AI workspace now

Get notified about new features and future giveaways by subscribing to our newsletter 👇