competitor-analyzer

What this skill does

# Competitor Analyzer

Systematic framework for gathering and analyzing competitive intelligence using Anysite MCP tools.

## When to Use This Skill

Trigger this skill when users ask to:
- "Analyze [competitor name]"
- "Research our competitors"
- "Create a competitive analysis of [company]"
- "How does [competitor] position themselves?"
- "What are [competitor]'s strengths and weaknesses?"
- "Compare our product with [competitor]"
- "Who are our main competitors?"
- "Build a battle card for [competitor]"

## Quick Start

**For single competitor analysis:**
```bash
# 1. Generate analysis template
python scripts/analyze_competitor.py "Competitor Name" "https://competitor.com"

# 2. Use Anysite tools to gather data (see workflow below)

# 3. Fill in the JSON template with findings

# 4. Generate final report
python scripts/analyze_competitor.py "Competitor Name" "https://competitor.com" | \
    python -c "import sys,json; exec('from scripts.analyze_competitor import format_markdown_report; print(format_markdown_report(json.load(sys.stdin)))')" \
    > /mnt/user-data/outputs/competitor_report.md
```

## Analysis Workflow

### Phase 1: Foundation (15-20 min)

**Step 1: Initialize Analysis Structure**

Run the analysis script to create structured template:
```bash
python scripts/analyze_competitor.py "Competitor Name" "https://competitor.com" > /tmp/analysis.json
```

**Step 2: Web Presence Reconnaissance**

Scrape key pages to understand positioning:
```python
# Homepage - core messaging
Anysite:parse_webpage({
    "url": "https://competitor.com",
    "only_main_content": true,
    "strip_all_tags": true
})

# Pricing - cost structure
Anysite:parse_webpage({
    "url": "https://competitor.com/pricing",
    "only_main_content": true
})

# About - company background
Anysite:parse_webpage({
    "url": "https://competitor.com/about",
    "only_main_content": true,
    "extract_contacts": true
})
```

**Extract from homepage:**
- H1/H2 headlines → positioning_statement
- Feature bullets → core_features
- Customer logos → customer_logos
- Value prop → value_proposition

**Extract from pricing:**
- Tier names and prices → pricing.tiers
- Cost per unit → pricing.unit_economics
- Free tier details → pricing.free_tier_limits
- Entry price → pricing.entry_price

**Extract from about:**
- Company description → company_overview.description
- Location → company_overview.headquarters
- Team size hints → company_overview.employee_count

### Phase 2: LinkedIn Intelligence (10-15 min)

**Step 3: Find Company Profile**

```python
# Search for company
Anysite:search_linkedin_companies({
    "keywords": "competitor name",
    "count": 5
})

# Get detailed profile using slug from search results
Anysite:get_linkedin_company({
    "company": "company-slug-from-search"
})
```

**Extract:**
- `follower_count` → Online presence indicator
- `employee_count` → Company size
- `description` → Self-positioning
- `headquarters` → Location
- `specialties` → Keywords they emphasize

**Step 4: Analyze Team & Growth**

```python
# Check employee growth signals
Anysite:get_linkedin_company_employees({
    "companies": ["company-slug"],
    "keywords": "engineer developer",
    "count": 50
})

# Find leadership
Anysite:get_linkedin_company_employees({
    "companies": ["company-slug"],
    "keywords": "CEO founder",
    "count": 10
})
```

**Use findings to assess:**
- Team size → growth_indicators.employee_growth
- Eng:sales ratio → GTM strategy signal
- Recent hires → growth phase indicator

**Step 5: Content Strategy**

```python
# Analyze posting activity
Anysite:get_linkedin_company_posts({
    "urn": "company-urn-from-profile",
    "count": 20
})
```

**Analyze posts for:**
- Frequency → content_strategy.blog_frequency
- Themes → content_strategy.key_topics
- Engagement → online_presence.linkedin.engagement_quality
- Tone → content_strategy.tone_of_voice

### Phase 3: Deep Social & Community Research (20-30 min)

**Step 6: Twitter Deep Dive**

**A. Company Account Analysis**
```python
# Get profile stats
Anysite:get_twitter_user({
    "user": "competitor_handle"
})

# Recent activity (analyze more posts)
Anysite:get_twitter_user_posts({
    "user": "competitor_handle",
    "count": 100
})
```

**Extract from company account:**
- Followers → reach indicator
- Tweet frequency → activity level
- Content mix (product updates, thought leadership, customer engagement)
- Response time to mentions
- Tone of voice
- Most engaging tweets (viral content patterns)

**B. Founder/Executive Twitter Presence**
```python
# Find and analyze founder accounts
Anysite:get_twitter_user({
    "user": "founder_handle"
})

Anysite:get_twitter_user_posts({
    "user": "founder_handle",
    "count": 100
})
```

**Leadership Twitter signals:**
- Personal brand strength
- Technical credibility (what they share)
- Customer engagement quality
- Industry thought leadership
- Follower quality (who follows them)
- Retweet patterns (what they amplify)

**C. Brand Mentions & Sentiment**
```python
# Comprehensive mention search
Anysite:search_twitter_posts({
    "query": "competitor_name OR @handle OR #competitor_hashtag",
    "count": 200
})

# Problem/complaint mentions
Anysite:search_twitter_posts({
    "query": "competitor_name (problem OR issue OR bug OR slow OR expensive)",
    "count": 100
})

# Positive sentiment
Anysite:search_twitter_posts({
    "query": "competitor_name (love OR great OR amazing OR best OR solved)",
    "count": 100
})

# Competitive mentions
Anysite:search_twitter_posts({
    "query": "competitor_name vs OR competitor_name alternative OR switching from competitor_name",
    "count": 100
})
```

**Sentiment scoring:**
```
For each mention batch, calculate:
- Positive mentions: praise, recommendations, success stories
- Negative mentions: complaints, frustrations, churn signals
- Neutral mentions: questions, feature discussions
- Competitive mentions: comparisons with alternatives

Sentiment Score = (Positive - Negative) / Total
Range: -1.0 (very negative) to +1.0 (very positive)
```

**D. Customer Voice Analysis**
```python
# Find actual users
Anysite:search_twitter_posts({
    "query": "using competitor_name OR tried competitor_name",
    "count": 100
})

# Power users
Anysite:search_twitter_posts({
    "query": "@handle thanks OR @handle helped OR @handle support",
    "count": 50
})
```

**Extract:**
- Real use cases (what customers actually do)
- Pain points (what they struggle with)
- Success stories (what works well)
- Feature requests (what they want)
- Support quality (how fast company responds)

**Step 7: Reddit Deep Community Intelligence**

**A. Brand Presence Mapping**
```python
# General mentions across Reddit
Anysite:search_reddit_posts({
    "query": "competitor_name",
    "count": 100
})

# Industry-specific subreddits
relevant_subs = [
    "SaaS", "startups", "Entrepreneur",  # Business
    "webdev", "programming", "devops",    # Tech
    "nocode", "automation",               # No-code
    "datascience", "analytics"            # Data
]

for sub in relevant_subs:
    Anysite:search_reddit_posts({
        "query": "competitor_name",
        "subreddit": sub,
        "count": 50
    })
```

**B. Competitive Discussions**
```python
# Direct comparisons
Anysite:search_reddit_posts({
    "query": "competitor_name vs",
    "count": 100
})

# Alternative searches
Anysite:search_reddit_posts({
    "query": "alternative to competitor_name",
    "count": 100
})

Anysite:search_reddit_posts({
    "query": "better than competitor_name",
    "count": 50
})

# Problem space
Anysite:search_reddit_posts({
    "query": "[problem they solve] tools OR solutions",
    "count": 100
})
```

**C. Deep Thread Analysis**

For high-engagement threads, get comments:
```python
# Get specific post details
Anysite:get_reddit_post({
    "post_url": "reddit.com/r/subreddit/comments/..."
})

# Get all comments
Anysite:get_reddit_post_comments({
    "post_url": "reddit.com/r/subreddit/comments/..."
})
```

**