anysite-brand-reputation

What this skill does

# 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

## v2 Tool Interface

All data fetching uses the anysite MCP v2 universal meta-tools:

- **`execute(source, category, endpoint, params)`** — fetch data from any source. Returns first page + `cache_key`.
- **`get_page(cache_key, offset, limit)`** — paginate through results when `next_offset` is returned.
- **`query_cache(cache_key, conditions, sort_by, aggregate, group_by)`** — filter, sort, or aggregate cached data without new API calls.
- **`export_data(cache_key, format)`** — export full dataset as CSV, JSON, or JSONL for reports.

Always call `discover(source, category)` first if unsure about endpoint names or params.

### Error Handling

v2 responses may include `llm_hint` fields with guidance on how to fix errors (e.g., wrong URN format, missing params). Always check `llm_hint` in error responses before retrying.

## 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:
```
Twitter:    execute("twitter", "search", "search_posts", {"query": "brand name", "count": 100})
Reddit:     execute("reddit", "search", "search_posts", {"query": "brand name", "count": 100})
Instagram:  execute("instagram", "search", "search_posts", {"query": "#brandname", "count": 100})
LinkedIn:   execute("linkedin", "post", "search_posts", {"keywords": "brand name", "count": 50})
```

Each call returns a `cache_key` — use it for pagination, filtering, and export.

**Step 3: Analyze Sentiment**

For each mention:
- Classify: Positive, negative, neutral
- Categorize: Complaint, praise, question, general
- Prioritize: Urgency, reach, influence

Use `query_cache(cache_key, conditions=[...], sort_by=...)` to filter high-engagement or negative mentions without re-fetching.

**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**
```
# Twitter (real-time pulse)
execute("twitter", "search", "search_posts", {"query": "brand name OR @brandhandle", "count": 100})
→ Returns cache_key_twitter; filter last 24h with from_date param (timestamp)

# Reddit (detailed discussions)
execute("reddit", "search", "search_posts", {"query": "brand name", "count": 50, "time_filter": "day"})
→ Returns cache_key_reddit

# Instagram (visual mentions)
execute("instagram", "search", "search_posts", {"query": "#brandname OR brand name", "count": 50})
→ Returns cache_key_instagram

# LinkedIn (professional mentions)
execute("linkedin", "post", "search_posts", {"keywords": "brand name", "count": 20})
→ Returns cache_key_linkedin

# YouTube (video coverage)
execute("youtube", "search", "search_videos", {"query": "brand name review OR brand name unboxing", "count": 20})
→ Returns cache_key_youtube
```

If any result includes `next_offset`, fetch more with:
```
get_page(cache_key, offset=next_offset, limit=50)
```

2. **Classify Mentions**

Use `query_cache` to sort and filter cached results:
```
# Find high-engagement mentions across platforms
query_cache(cache_key_twitter, sort_by=[{"field": "favorite_count", "order": "desc"}])
query_cache(cache_key_reddit, sort_by=[{"field": "vote_count", "order": "desc"}])

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
```

3. **Prioritize Issues**
```
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
```

4. **Generate Daily Report**

Export data for reporting:
```
export_data(cache_key_twitter, "csv")
export_data(cache_key_reddit, "csv")
→ Returns download URLs for each dataset

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**
```
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)
```

2. **Deep Dive on Spikes**
```
When alert triggered:

execute("twitter", "search", "search_posts", {"query": "brand name", "count": 500})
→ Identify what's driving spike; use get_page() to load all results

execute("reddit", "search", "search_posts", {"query": "brand name", "count": 200})
→ Check community discussions

For viral posts:
  # Get specific Reddit post details and comments
  execute("reddit", "posts", "posts", {"post_url": "<reddit_post_url>"})
  execute("reddit", "posts", "posts_comments", {"post_url": "<reddit_post_url>"})
  → Analyze reach and engagement, read comments for context

  # For viral tweets, scrape the tweet URL directly
  execute("webparser", "parse", "parse", {"url": "<tweet_url>"})
  → Get tweet details and engagement metrics
```

3. **Assess Crisis Severity**
```
Severity factors:
- Volume (how many mentions)
- Velocity (how fast growing)
- Reach (influencer involvement, media coverage)
- Sentiment (how negative)
- Validity (legitimate issue vs. misunderstanding)

Use query_cache to analyze cached data:
query_cache(cache_key, aggregate=[{"function": "count"}])
→ Total mention count without re-fetching
```

4. **Track Crisis Evolution**
```
Hourly monitoring:
- Mention volume trend
- Sentiment shifts
- Platform spread
- Media pickup
- Official response impact
```

5. **Measure Resolution**
```
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**
```
List 3-5 main competitors
```

2. **Gather Mentions for All**
```
For brand + each competitor:
  execute("twitter", "search", "search_posts", {"query": "<brand>", "count": 200})
  execute("reddit", "search", "search_posts", {"query": "<brand>", "count": 100})
  execute("linkedin", "post", "search_