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:

# 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:

python scripts/analyze_competitor.py "Competitor Name" "https://competitor.com" > /tmp/analysis.json

Step 2: Web Presence Reconnaissance

Scrape key pages to understand positioning:

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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

# 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:

# 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/..."
})

Analyze thread comments for:

Detailed user experiences
Technical discussions
Feature comparisons
Pricing discussions
Customer support experiences
Decision factors (why they chose/didn't choose)

D. Sentiment & Voice Analysis

Positive signals:

"I love [competitor]"
"Works perfectly for..."
"Best tool for..."
"Highly recommend"
"Switched to [competitor] and..."

Negative signals:

"Disappointed with..."
"Overpriced"
"Customer support is..."
"Buggy/unreliable"
"Looking for alternative"
"Switched away from..."

Neutral/informational:

"How does [competitor] work?"
"Anyone tried [competitor]?"
"Pricing question"
Feature clarifications

E. Community Size & Engagement

Calculate metrics:

Brand Awareness Score:
- Total unique mentions (last 30 days)
- Number of different subreddits mentioned in
- Average upvotes per mention
- Comment volume per mention

Community Health:
- Positive/Negative mention ratio
- Response rate to questions
- Problem resolution in comments
- Community helping each other

Step 7.5: Cross-Platform Insight Synthesis

Compare Twitter vs Reddit:

Twitter typically shows:

Official company narrative
Marketing messaging
Quick customer service interactions
Surface-level sentiment
Broader reach

Reddit typically reveals:

Unfiltered user opinions
Detailed technical discussions
Pricing sensitivity
Competitive comparisons
Real problems and workarounds

Look for disconnects:

Company claims strong product (Twitter) but users complain (Reddit)
High Twitter engagement but low Reddit mentions → Marketing-driven, not organic
Reddit loves it but low Twitter presence → Word-of-mouth, under-marketed
Consistent messaging → Authentic product-market fit

Phase 4: Leadership & Founders Intelligence (15-20 min)

Step 8: Identify Key Leaders

# Find founders and C-level
Anysite:search_linkedin_users({
    "company_keywords": "competitor-name",
    "title": "founder OR CEO OR CTO OR CPO",
    "count": 10
})

# Get detailed profiles
Anysite:get_linkedin_profile({
    "user": "founder-linkedin-username",
    "with_experience": true,
    "with_education": true,
    "with_skills": true
})

Extract for each leader:

Full career history → their experience and expertise
Previous companies → track record
Education background → academic credentials
Skills → technical depth
Languages → market reach
Recommendations → credibility signals

Step 9: Analyze Leadership Activity

# Get personal posts
Anysite:get_linkedin_user_posts({
    "urn": "user-urn-from-profile",
    "count": 50
})

# Check comments on others' posts
Anysite:get_linkedin_user_comments({
    "urn": "user-urn",
    "count": 30
})

# See what they're engaging with
Anysite:get_linkedin_user_reactions({
    "urn": "user-urn",
    "count": 50
})

Analyze for:

Posting frequency and themes
Technical depth in posts
Market perspective
Customer engagement
Thought leadership quality
Network quality (who engages with them)

Step 10: Twitter Leadership Presence

# Founder Twitter activity
Anysite:get_twitter_user({
    "user": "founder_handle"
})

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

Leadership indicators:

Personal brand strength
Technical credibility
Customer relationships
Industry influence
Communication style
Transparency level

Phase 5: Technical & Data Discovery (10-15 min)

Step 11: Documentation Quality

# Scrape docs homepage
Anysite:parse_webpage({
    "url": "https://competitor.com/docs",
    "only_main_content": true
})

# Check API reference
Anysite:parse_webpage({
    "url": "https://competitor.com/api",
    "only_main_content": true
})

Step 11: Documentation Quality

# Scrape docs homepage
Anysite:parse_webpage({
    "url": "https://competitor.com/docs",
    "only_main_content": true
})

# Check API reference
Anysite:parse_webpage({
    "url": "https://competitor.com/api",
    "only_main_content": true
})

Assess:

Documentation completeness
Code examples presence
Interactive explorer
SDK availability → Feed into technical_capabilities

Step 12: GitHub Presence (if applicable)

# Parse GitHub profile page
Anysite:parse_webpage({
    "url": "https://github.com/competitor-org",
    "only_main_content": true
})

# Check main repository
Anysite:parse_webpage({
    "url": "https://github.com/competitor-org/main-repo",
    "only_main_content": true
})

Extract:

Star count (developer interest)
Fork count (actual usage)
Commit frequency (development velocity)
Contributors count (community size)
Issue response time (support quality)
Open source components (ecosystem play)

Step 13: Alternative Data Sources

# Glassdoor reviews (if company page exists)
Anysite:parse_webpage({
    "url": "https://www.glassdoor.com/Reviews/competitor-name",
    "only_main_content": true
})

What to extract:

Overall rating (employee satisfaction)
CEO approval rating (leadership quality)
Salary ranges (compensation level)
Interview difficulty (hiring standards)
Work-life balance (culture signal)
Recent reviews (current state)

Step 14: Integration Ecosystem

# Get sitemap to find all pages
Anysite:get_sitemap({
    "url": "https://competitor.com/sitemap.xml",
    "count": 50
})

# Parse integrations page
Anysite:parse_webpage({
    "url": "https://competitor.com/integrations",
    "only_main_content": true
})

Step 14: Integration Ecosystem

# Get sitemap to find all pages
Anysite:get_sitemap({
    "url": "https://competitor.com/sitemap.xml",
    "count": 50
})

# Parse integrations page
Anysite:parse_webpage({
    "url": "https://competitor.com/integrations",
    "only_main_content": true
})

Extract:

Integration partners → technical_capabilities.integrations
Platform focus (Zapier, enterprise tools, etc.)
API-first vs GUI-first

Phase 6: Synthesis (15-20 min)

Step 15: Competitive Analysis

Compare findings against your own product:

Strengths (what they do well):

Identify 3-5 clear advantages they have
Based on features, pricing, market position, or execution

Weaknesses (where they struggle):

Identify 3-5 clear gaps or problems
Missing features, high prices, poor UX, etc.

Opportunities (what you can exploit):

Their weaknesses that you can capitalize on
Underserved segments they're missing
Messaging/positioning gaps

Threats (what you need to watch):

Their strengths that could hurt you
Recent funding or growth
Feature development velocity

Step 16: Strategic Insights

Synthesize everything into:

Key Takeaways (3-5 bullets):

Most important findings
Clear, actionable insights

Competitive Threats (2-3 bullets):

What they could do to hurt your position
Their strategic advantages

Opportunities to Exploit (3-5 bullets):

How to position against them
Their vulnerabilities to target
Market gaps they're missing

Watch Areas:

Things to monitor quarterly
Signals of strategic shifts

Step 17: Generate Final Report

Update the JSON template with all findings, then generate markdown:

import json
from scripts.analyze_competitor import save_analysis

# Load populated template
with open('/tmp/analysis.json', 'r') as f:
    data = json.load(f)

# Generate reports
json_path, md_path = save_analysis(data)
print(f"Reports saved:\n  JSON: {json_path}\n  Markdown: {md_path}")

Move final files to outputs:

cp /tmp/analysis.json /mnt/user-data/outputs/
cp /tmp/analysis.md /mnt/user-data/outputs/

Advanced Techniques

Multi-Competitor Analysis

For analyzing 3-5 competitors simultaneously:

Run analysis workflow for each competitor
Create comparison matrix in spreadsheet format
Focus on key differentiators:
- Pricing comparison table
- Feature matrix (rows=features, cols=competitors)
- Market position map (price vs capabilities)
- Social presence comparison

Ongoing Monitoring

For quarterly updates (not full re-analysis):

Quick check (30 min):

# 1. Re-scrape pricing
Anysite:parse_webpage({"url": "competitor.com/pricing"})

# 2. Check recent posts
Anysite:get_linkedin_company_posts({"urn": "...", "count": 10})

# 3. Employee growth
Anysite:get_linkedin_company_employees({"companies": ["..."], "count": 20})

# 4. Recent mentions
Anysite:search_twitter_posts({"query": "competitor", "count": 50})

Update only changed sections in JSON template.

Battle Card Creation

For sales team quick reference:

Focus on:

Quick facts (1-2 sentences)
Head-to-head feature comparison (table format)
Pricing comparison (clear numbers)
3 reasons we win
3 reasons we might lose
Talk tracks ("When they say X, we say Y")

Keep to 1-2 pages maximum.

Reference Files

When you need detailed guidance:

Data collection methodology: See data_collection.md
- Use when unsure which Anysite tools to use
- Use when planning data gathering strategy
- Contains detailed tool parameters and extraction techniques
Analysis frameworks: See analysis_frameworks.md
- Use when analyzing specific company types (SaaS vs Enterprise vs Consumer)
- Use when creating battle cards or competitive matrices
- Contains templates for different output formats

Common Patterns

Pattern 1: Rapid Assessment (30-45 min)

For quick competitive scan:

Homepage + pricing scrape
LinkedIn company profile
Recent social posts (20 total)
Fill core sections only (skip deep dives)
Generate brief summary (1 page)

Pattern 2: Deep Intelligence (2-3 hours)

For comprehensive analysis:

Full web presence (7-10 pages)
Complete LinkedIn intelligence
Social media deep dive (50-100 posts/mentions)
Community sentiment analysis
Technical documentation review
Full JSON template populated
Detailed markdown report

Pattern 3: Pricing Focus

For pricing-specific analysis:

Scrape all pricing pages
Calculate unit economics
Map tier structures
Compare to market
Identify pricing strategy
Generate pricing comparison table

Pattern 4: Leadership Focus

For founder/team intelligence:

Identify all founders and C-level
Deep dive into founder LinkedIn profiles
Analyze personal posting activity (50+ posts)
Track Twitter presence and influence
Map previous company experience
Assess thought leadership quality
Evaluate public credibility

Use when:

Considering partnerships
Evaluating acquisition targets
Assessing strategic threats
Understanding company DNA

Tips for Effective Analysis

Be Systematic:

Follow the phase order
Don't skip LinkedIn intelligence (best growth signals)
Always check pricing (most volatile data)

Think Strategically:

Not just "what" they do, but "why"
Look for patterns in their behavior
Consider their constraints (funding, team size)

Verify Claims:

Marketing copy ≠ reality
Cross-reference multiple sources
Note confidence levels (verified vs estimated)

Focus on Actionable Insights:

Don't just describe, analyze implications
What should YOUR company do based on findings?
What threats need immediate response?

Document Data Freshness:

Always note analysis date
Mark which data is recent vs stale
Plan update frequency based on importance

Output Quality Standards

Good competitive analysis includes:

✅ Clear positioning statement
✅ Quantified metrics (prices, follower counts, team size)
✅ Specific examples (actual quotes, feature lists)
✅ Strategic implications explained
✅ Data sources noted
✅ Confidence levels indicated

Avoid:

❌ Vague assessments ("they seem good at X")
❌ Unsupported claims ("probably losing money")
❌ Missing pricing details
❌ Outdated data without date stamps
❌ Pure feature lists without analysis

Troubleshooting

"Can't find LinkedIn company":

Try variations of company name
Search for CEO name, find company from profile
Check if they use different legal name

"Pricing page missing/unclear":

Check /plans, /buy, /subscribe URLs
Look for pricing calculator
Note "Contact Sales" as signal (enterprise focus)

"No social media presence":

Still document the absence (itself a signal)
Check founder personal accounts
Look for employee posting activity

"Too much data, overwhelmed":

Start with Phase 1 & 2 only (foundation + LinkedIn)
Generate partial report
Add Phase 3 & 4 if needed for depth

anysite-competitor-analyzer

Competitor Analyzer

When to Use This Skill

Quick Start

Analysis Workflow

Phase 1: Foundation (15-20 min)

Phase 2: LinkedIn Intelligence (10-15 min)

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

Phase 4: Leadership & Founders Intelligence (15-20 min)

Phase 5: Technical & Data Discovery (10-15 min)

Phase 6: Synthesis (15-20 min)

Advanced Techniques

Multi-Competitor Analysis

Ongoing Monitoring

Battle Card Creation

Reference Files

Common Patterns

Pattern 1: Rapid Assessment (30-45 min)

Pattern 2: Deep Intelligence (2-3 hours)

Pattern 3: Pricing Focus

Pattern 4: Leadership Focus

Tips for Effective Analysis

Output Quality Standards

Troubleshooting