Anysite Competitor Analyzer

Competitor Analyzer

Systematic framework for gathering and analyzing competitive intelligence using Anysite MCP v2 tools.

Tool Interface (v2)

All data fetching uses the unified v2 meta-tools:

• execute(source, category, endpoint, params) - Fetch data. Returns first 10 items + cache_key. If next_offset is present, use get_page() to load more.
• get_page(cache_key, offset, limit) - Paginate through cached results from a previous execute(). Data cached 7 days.
• query_cache(cache_key, conditions, sort_by, aggregate, group_by) - Filter, sort, count, or aggregate already-fetched data without new API calls.
• export_data(cache_key, format) - Export full dataset as CSV, JSON, or JSONL. Returns a download URL.
• discover(source, category) - Inspect available endpoints and params before calling execute().

Error handling: If execute() returns an error with llm_hint, follow the hint to fix the call. Common issues: wrong URN format, alias not found (search first), fsd_company vs company: prefix mismatch.

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 v2 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
execute("webparser", "parse", "parse", {
    "url": "https://competitor.com",
    "only_main_content": true,
    "strip_all_tags": true
})

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

# About - company background
execute("webparser", "parse", "parse", {
    "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.freetierlimits
• Entry price → pricing.entry_price

Extract from about:

• Company description → company_overview.description
• Location → company_overview.headquarters
• Team size hints → companyoverview.employeecount

Phase 2: LinkedIn Intelligence (10-15 min)

Step 3: Find Company Profile

# Search for company
execute("linkedin", "search", "search_companies", {
    "keywords": "competitor name",
    "count": 5
})

# Get detailed profile using slug from search results
execute("linkedin", "company", "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 (use search_users with current_company filter)
execute("linkedin", "search", "search_users", {
    "current_company": "company-slug",
    "keywords": "engineer developer",
    "count": 50
})

# Find leadership
execute("linkedin", "search", "search_users", {
    "current_company": "company-slug",
    "title": "CEO founder",
    "count": 10
})

# Get employee stats breakdown (functions, seniority, growth trends)
# First get company URN from company profile, convert fsd_company to company: prefix
execute("linkedin", "company", "company_employee_stats", {
    "urn": {"type": "company", "value": "COMPANY_ID_FROM_URN"}
})

Use findings to assess:

• Team size → growthindicators.employeegrowth
• Eng:sales ratio → GTM strategy signal
• Recent hires → growth phase indicator

Tip: Use query_cache() on the employee search results to filter by title or sort by relevance without re-fetching:

query_cache(cache_key, conditions=[{"field": "headline", "operator": "contains", "value": "engineer"}])

Step 5: Content Strategy

# Analyze posting activity
# Use company: prefix URN from the company profile (convert fsd_company:{id} to company:{id})
execute("linkedin", "company", "company_posts", {
    "urn": {"type": "company", "value": "COMPANY_ID_FROM_URN"},
    "count": 20
})

Analyze posts for:

• Frequency → contentstrategy.blogfrequency
• Themes → contentstrategy.keytopics
• Engagement → onlinepresence.linkedin.engagementquality
• Tone → contentstrategy.toneof_voice

Tip: Use query_cache() to aggregate engagement metrics across fetched posts:

query_cache(cache_key, aggregate=[{"field": "comment_count", "function": "avg"}])

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

Step 6: Twitter Deep Dive

A. Company Account Analysis

# Get profile stats
execute("twitter", "user", "get", {
    "username": "competitor_handle"
})

# Recent activity (analyze more posts)
execute("twitter", "user_tweets", "get", {
    "username": "competitor_handle",
    "count": 100
})
# If next_offset returned, use get_page() to load more:
# get_page(cache_key, offset=next_offset, limit=50)

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)

Tip: Use query_cache() to find top-performing tweets:

query_cache(cache_key, sort_by=[{"field": "favorite_count", "order": "desc"}])

B. Founder/Executive Twitter Presence

# Find and analyze founder accounts
execute("twitter", "user", "get", {
    "username": "founder_handle"
})

execute("twitter", "user_tweets", "get", {
    "username": "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
execute("twitter", "search", "search_posts", {
    "query": "competitor_name OR @handle OR #competitor_hashtag",
    "count": 200
})

# Problem/complaint mentions
execute("twitter", "search", "search_posts", {
    "query": "competitor_name (problem OR issue OR bug OR slow OR expensive)",
    "count": 100
})

# Positive sentiment
execute("twitter", "search", "search_posts", {
    "query": "competitor_name (love OR great OR amazing OR best OR solved)",
    "count": 100
})

# Competitive mentions
execute("twitter", "search", "search_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)

Tip: Use query_cache() to filter cached mentions by sentiment keywords without re-fetching:

query_cache(cache_key, conditions=[{"field": "text", "operator": "contains", "value": "love"}])

D. Customer Voice Analysis

# Find actual users
execute("twitter", "search", "search_posts", {
    "query": "using competitor_name OR tried competitor_name",
    "count": 100
})

# Power users
execute("twitter", "search", "search_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
execute("reddit", "search", "search_posts", {
    "query": "competitor_name",
    "count": 100
})

# Industry-specific searches (combine with subreddit keywords)
relevant_topics = [
    "SaaS", "startups", "Entrepreneur",  # Business
    "webdev", "programming", "devops",    # Tech
    "nocode", "automation",               # No-code
    "datascience", "analytics"            # Data
]

for topic in relevant_topics:
    execute("reddit", "search", "search_posts", {
        "query": f"competitor_name {topic}",
        "count": 50
    })

B. Competitive Discussions

# Direct comparisons
execute("reddit", "search", "search_posts", {
    "query": "competitor_name vs",
    "count": 100
})

# Alternative searches
execute("reddit", "search", "search_posts", {
    "query": "alternative to competitor_name",
    "count": 100
})

execute("reddit", "search", "search_posts", {
    "query": "better than competitor_name",
    "count": 50
})

# Problem space
execute("reddit", "search", "search_posts", {
    "query": "[problem they solve] tools OR solutions",
    "count": 100
})

C. Deep Thread Analysis

For high-engagement threads, get comments:

# Get specific post details
execute("reddit", "posts", "posts", {
    "post_url": "reddit.com/r/subreddit/comments/..."
})

# Get all comments
execute("reddit", "posts", "posts_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)

Tip: Use query_cache() on fetched comments to sort by score and find the most upvoted opinions:

query_cache(cache_key, sort_by=[{"field": "score", "order": "desc"}])

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
execute("linkedin", "search", "search_users", {
    "company_keywords": "competitor-name",
    "title": "founder OR CEO OR CTO OR CPO",
    "count": 10
})

# Get detailed profiles
execute("linkedin", "user", "user", {
    "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 (use fsd_profile URN from user profile)
execute("linkedin", "user", "user_posts", {
    "urn": {"type": "fsd_profile", "value": "USER_URN_VALUE"},
    "count": 50
})

# Check comments on others' posts
execute("linkedin", "user", "user_comments", {
    "urn": {"type": "fsd_profile", "value": "USER_URN_VALUE"},
    "count": 30
})

# See what they're engaging with
execute("linkedin", "user", "user_reactions", {
    "urn": {"type": "fsd_profile", "value": "USER_URN_VALUE"},
    "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)

Tip: Use query_cache() on leadership posts to aggregate engagement:

query_cache(cache_key, aggregate=[
    {"field": "comment_count", "function": "avg"},
    {"field": "comment_count", "function": "sum"}
])

Step 10: Twitter Leadership Presence

# Founder Twitter activity
execute("twitter", "user", "get", {
    "username": "founder_handle"
})

execute("twitter", "user_tweets", "get", {
    "username": "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
execute("webparser", "parse", "parse", {
    "url": "https://competitor.com/docs",
    "only_main_content": true
})

# Check API reference
execute("webparser", "parse", "parse", {
    "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
execute("webparser", "parse", "parse", {
    "url": "https://github.com/competitor-org",
    "only_main_content": true
})

# Check main repository
execute("webparser", "parse", "parse", {
    "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

…

## Source & license

This open-source skill is cataloged on AgentStack and links to its original source — we do not rehost the code.

- **Author:** [anysiteio](https://github.com/anysiteio)
- **Source:** [anysiteio/agent-skills](https://github.com/anysiteio/agent-skills)
- **License:** MIT

Install and usage instructions live in the source repository linked above.