AgentSkillsCN

anysite-brand-reputation

利用 anysite MCP 服务器，监测 Twitter/X、Reddit、Instagram、YouTube 与 LinkedIn 上的品牌声誉与情绪倾向。追踪品牌提及次数，分析客户情绪，监控社交对话，识别声誉问题，并衡量品牌健康度。支持社交媒体监听、情绪分析、提及追踪以及危机预警。当用户需要监测品牌提及、追踪客户情绪、识别声誉风险、分析品牌认知，或衡量各平台上的社交媒体影响力与品牌健康度时，可调用此技能。

SKILL.md
--- frontmatter
name: anysite-brand-reputation
description: Monitor brand reputation and sentiment across Twitter/X, Reddit, Instagram, YouTube, and LinkedIn using anysite MCP server. Track brand mentions, analyze customer sentiment, monitor social conversations, identify reputation issues, and measure brand health. Supports social media listening, sentiment analysis, mention tracking, and crisis detection. Use when users need to monitor brand mentions, track customer sentiment, identify reputation risks, analyze brand perception, or measure social media presence and brand health across platforms.

anysite Brand Reputation Monitoring

Monitor and protect your brand reputation across social media platforms. Track mentions, analyze sentiment, and identify issues before they escalate.

Overview

  • •Track brand mentions across social platforms
  • •Analyze sentiment (positive, negative, neutral)
  • •Monitor conversations about your brand
  • •Identify reputation risks and crisis signals
  • •Measure brand health over time

Coverage: 65% - Pivoted from review platforms to social media monitoring; strong for Twitter, Reddit, Instagram, YouTube, LinkedIn

Supported Platforms

  • •✅ Twitter/X: Real-time mentions, sentiment, viral content
  • •✅ Reddit: Community discussions, detailed feedback, sentiment
  • •✅ Instagram: Visual brand mentions, hashtag tracking, influencer posts
  • •✅ YouTube: Video mentions, comment sentiment, brand coverage
  • •✅ LinkedIn: Professional mentions, company updates, B2B sentiment

Quick Start

Step 1: Set Up Monitoring

Define:

  • •Brand keywords (company name, product names, misspellings)
  • •Platforms to monitor (Twitter, Reddit, Instagram, etc.)
  • •Monitoring frequency (real-time, daily, weekly)
  • •Alert thresholds (negative sentiment, volume spikes)

Step 2: Search for Mentions

Platform searches:

code
Twitter: search_twitter_posts(query="brand name", count=100)
Reddit: search_reddit_posts(query="brand name", count=100)
Instagram: search_instagram_posts(query="#brandname", count=100)
LinkedIn: search_linkedin_posts(keywords="brand name", count=50)

Step 3: Analyze Sentiment

For each mention:

  • •Classify: Positive, negative, neutral
  • •Categorize: Complaint, praise, question, general
  • •Prioritize: Urgency, reach, influence

Step 4: Take Action

Based on findings:

  • •Respond to negative mentions
  • •Amplify positive feedback
  • •Address product issues
  • •Engage with community

Common Workflows

Workflow 1: Daily Brand Monitoring

Scenario: Monitor brand mentions across all platforms daily

Steps:

  1. •Search All Platforms
code
# Twitter (real-time pulse)
search_twitter_posts(query="brand name OR @brandhandle", count=100)
Filter: Last 24 hours

# Reddit (detailed discussions)
search_reddit_posts(query="brand name", count=50)
Filter: Last 24 hours

# Instagram (visual mentions)
search_instagram_posts(query="#brandname OR brand name", count=50)

# LinkedIn (professional mentions)
search_linkedin_posts(keywords="brand name", count=20)

# YouTube (video coverage)
search_youtube_videos(query="brand name review OR brand name unboxing", count=20)
  1. •Classify Mentions
code
For each mention:

Sentiment:
- Positive: Praise, recommendation, satisfaction
- Negative: Complaint, criticism, problem
- Neutral: Question, general mention, factual

Category:
- Product feedback
- Customer service issue
- Feature request
- General discussion
- Competitor comparison
  1. •Prioritize Issues
code
High Priority:
- Negative + High reach (viral potential)
- Multiple complaints about same issue
- Influencer negative mention
- Legal/safety concerns

Medium Priority:
- Individual complaints
- Feature requests
- Questions
- General feedback

Low Priority:
- Positive mentions
- Neutral discussions
- General brand awareness
  1. •Generate Daily Report
code
Summary:
- Total mentions (by platform)
- Sentiment breakdown (% positive/negative/neutral)
- Top issues identified
- Viral/trending mentions
- Recommended actions

Expected Output:

  • •Daily mention count: 50-200
  • •Sentiment distribution
  • •Top 5 issues to address
  • •Action items for team

Workflow 2: Crisis Detection and Management

Scenario: Identify and track potential PR crises

Steps:

  1. •Monitor for Anomalies
code
Track baseline:
- Average mentions per day
- Average sentiment score
- Typical engagement levels

Alert triggers:
- Mentions >2x baseline
- Negative sentiment >50%
- Viral negative content (high engagement)
  1. •Deep Dive on Spikes
code
When alert triggered:

search_twitter_posts(query="brand name", count=500)
→ Identify what's driving spike

search_reddit_posts(query="brand name", count=200)
→ Check community discussions

For viral posts:
  get_twitter_post(post_id) or get_reddit_post(url)
  → Analyze reach and engagement
  → Read comments for context
  1. •Assess Crisis Severity
code
Severity factors:
- Volume (how many mentions)
- Velocity (how fast growing)
- Reach (influencer involvement, media coverage)
- Sentiment (how negative)
- Validity (legitimate issue vs. misunderstanding)
  1. •Track Crisis Evolution
code
Hourly monitoring:
- Mention volume trend
- Sentiment shifts
- Platform spread
- Media pickup
- Official response impact
  1. •Measure Resolution
code
Track until:
- Volume returns to baseline
- Sentiment improves
- No new negative mentions for 24-48h

Expected Output:

  • •Crisis timeline
  • •Mention volume graph
  • •Sentiment tracking
  • •Key influencers/posts
  • •Response effectiveness

Workflow 3: Competitive Reputation Benchmarking

Scenario: Compare brand sentiment vs. competitors

Steps:

  1. •Define Competitors
code
List 3-5 main competitors
  1. •Gather Mentions for All
code
For brand + each competitor:
  search_twitter_posts(query=brand, count=200)
  search_reddit_posts(query=brand, count=100)
  search_linkedin_posts(keywords=brand, count=50)
  1. •Calculate Brand Health Scores
code
For each brand:

Mention Volume: Total mentions
Sentiment Score: (Positive - Negative) / Total
Engagement Rate: Avg engagement per mention
Share of Voice: Your mentions / Total category mentions
  1. •Analyze Strengths/Weaknesses
code
Compare:
- What are competitors praised for?
- What are competitors criticized for?
- Where do we excel?
- Where do we fall short?
  1. •Identify Opportunities
code
Look for:
- Unmet customer needs (complaints about competitors)
- Messaging gaps (what they're not saying)
- Product differentiation opportunities
- Customer service advantages

Expected Output:

  • •Competitive sentiment matrix
  • •Brand health scores comparison
  • •Strength/weakness analysis
  • •Strategic opportunities

MCP Tools Reference

Twitter/X

  • •search_twitter_posts(query, count) - Find brand mentions
  • •get_twitter_user(user) - Check brand profile stats
  • •get_twitter_user_posts(user, count) - Monitor brand account

Reddit

  • •search_reddit_posts(query, subreddit, count) - Find discussions
  • •get_reddit_post(url) - Get post details and sentiment
  • •get_reddit_post_comments(url) - Deep dive on discussions

Instagram

  • •search_instagram_posts(query, count) - Find visual mentions
  • •get_instagram_post(post_id) - Analyze mention engagement
  • •get_instagram_post_comments(post, count) - Read feedback

YouTube

  • •search_youtube_videos(query, count) - Find video mentions
  • •get_youtube_video(video) - Get video details
  • •get_youtube_video_comments(video, count) - Analyze sentiment

LinkedIn

  • •search_linkedin_posts(keywords, count) - Professional mentions
  • •get_linkedin_company_posts(urn, count) - Monitor own posts

Sentiment Analysis Framework

Manual Sentiment Classification:

Positive Indicators:

  • •"Love", "great", "amazing", "best"
  • •Recommendations ("highly recommend")
  • •Repeat purchase ("buying again")
  • •Comparisons ("better than X")

Negative Indicators:

  • •"Disappointed", "worst", "terrible", "awful"
  • •Problems ("doesn't work", "broken")
  • •Comparisons ("X is better")
  • •Demands ("need refund", "fix this")

Neutral Indicators:

  • •Questions without sentiment
  • •Factual statements
  • •General mentions
  • •Informational content

Sentiment Score:

code
Score = (Positive mentions - Negative mentions) / Total mentions × 100

+50 to +100: Excellent
+20 to +49: Good
-19 to +19: Neutral/Mixed
-20 to -49: Poor
-50 to -100: Critical

Monitoring Metrics

Volume Metrics:

  • •Total mentions per day/week/month
  • •Mentions by platform
  • •Trend over time

Sentiment Metrics:

  • •Sentiment distribution (% positive/negative/neutral)
  • •Sentiment score (net promoter style)
  • •Sentiment trend over time

Engagement Metrics:

  • •Average likes/shares per mention
  • •Viral mentions (>1000 engagements)
  • •Influencer mentions

Issue Tracking:

  • •Top complaints (by frequency)
  • •Product/service issues mentioned
  • •Feature requests
  • •Customer service mentions

Output Formats

Chat Summary:

  • •Daily mention highlights
  • •Sentiment overview
  • •Top issues identified
  • •Recommended actions

CSV Export:

  • •Mention list with sentiment
  • •Platform, date, reach
  • •Issue categorization

JSON Export:

  • •Complete mention data
  • •Time-series sentiment
  • •Engagement metrics

Reference Documentation

  • •MONITORING_GUIDE.md - Best practices for brand monitoring, crisis response protocols, and sentiment analysis techniques

Ready to monitor your brand? Ask Claude to help you track mentions, analyze sentiment, or identify reputation risks across social platforms!