Competitor Signals

Find leads by monitoring competitor product activity. Instead of looking for your prospects directly, watch your competitors' audience — every person engaging with a competitor launch is self-identifying as in-market for your category.

When to Use

• User wants to find people engaging with competitor products
• User mentions Product Hunt launches, competitor press coverage, or competitor case studies
• User wants to find people switching from or evaluating competitor products
• User asks "who is using competitor]" or "who is looking at alternatives to competitor]"
• User wants to monitor competitor activity for lead generation
• User has a clear list of competitors and wants to mine their audience

Prerequisites

• Python 3.9+ with requests and optionally python-dotenv
• Product Hunt developer token (free, optional — get at api.producthunt.com/v2/oauth/applications)
• Apify API token in .env (fallback for PH if API names are redacted, optional)
• Working directory: the project root containing this skill

Phase 1: Collect Context

Step 1: Gather Competitor Information

Ask the user:

> "To find leads from competitor activity, I need: > 1. Who are your competitors? (product names and company names) > 2. Do you know their Product Hunt slugs? (the URL path on producthunt.com/posts/SLUG) > 3. Any specific competitor launches or announcements you've seen recently? > 4. Are there competitors or signals you specifically want to track? (e.g., a competitor just raised funding, launched a new feature, or got press coverage)"

Step 2: Discover Competitors (if user needs help)

If the user doesn't have a complete competitor list, help them discover competitors:

2a. Product Hunt search:

• Search producthunt.com for the user's product category
• Note: PH doesn't have a great search API — use web search: "site:producthunt.com product category]"

2b. G2/Capterra category pages:

• Search: "product category] G2" or "product category] Capterra"
• These pages list all competitors in a category with rankings

2c. "Alternatives to" sites:

• Search: "known competitor] alternatives"
• Sites like alternativeto.net, slant.co, stackshare.io list competitors

2d. Ask the user: > "Based on my research, here are competitors I've found in your space: list]. Are there any I'm missing? Any you'd like to exclude (e.g., not really competitors, too different in market segment)?"

Step 3: Find Product Hunt Slugs

For each competitor, find their PH launches:

• Search: "site:producthunt.com competitor name]"
• Or browse: producthunt.com/products/[competitor-name]
• Note the slug from the URL: producthunt.com/posts/SLUG
• A competitor may have multiple launches (initial launch + feature launches)

Step 4: Identify Competitor Web Pages to Scrape

For each competitor, identify pages the agent should scrape:

Case studies page: [competitor].com/customers or [competitor].com/case-studies

• Extract: company names, logos, quotes, person names, titles
• These are PROVEN BUYERS in the category

Testimonials page: Often on the homepage or a dedicated page

• Extract: person name, title, company, quote
• These are current users who publicly endorsed the competitor

Blog: [competitor].com/blog

• Guest posts by customers are case studies in disguise
• "How Company X] uses Competitor]" = case study

Present all discovered pages to the user for review.

Phase 2: Agent-Driven Scraping

Step 5: Scrape Competitor Websites

Before running the tool, the agent should manually scrape competitor case studies and testimonials. This is agent-driven because every competitor website has a different format.

For each competitor's case study page:

1. Navigate to the page using web fetch or Chrome DevTools
2. Extract all customer company names and any associated person names/quotes
3. Note the case study URL for reference

For each competitor's testimonials page:

1. Extract: person name, title, company, quote text
2. These are high-value signals — these people actively chose to endorse the competitor

Save all scraped data to ${CLAUDE_SKILL_DIR}/../.tmp/competitor_manual_signals.json:

json
[
    {
        "person_name": "Sarah Chen",
        "company": "TechCorp",
        "signal_type": "case_study_company",
        "signal_label": "Competitor Case Study",
        "competitor": "Twilio",
        "context": "How TechCorp scaled video calls to 100K users with Twilio",
        "url": "https://twilio.com/case-studies/techcorp",
        "profile_url": "",
        "date": "",
        "source": "Manual",
        "engagement": 0
    }
]

Step 6: Check Tech Press

Search for recent articles about competitors:

• "competitor] TechCrunch"
• "competitor] The New Stack"
• "competitor] InfoQ"
• "competitor] DevOps.com"
• "competitor] launch announcement"
• "competitor] raises funding"

For articles found:

• Note the article URL and key companies/people mentioned
• If the article has comments, check for people expressing opinions
• Add notable findings to the manual signals JSON

Phase 3: Execute Tool

Step 7: Save Config

bash
cat > ${CLAUDE_SKILL_DIR}/../.tmp/competitor_signals_config.json << 'CONFIGEOF'
{
    "competitors": ["Twilio", "Agora", "Vonage", "Daily.co"],
    "product_hunt_slugs": ["twilio-video", "agora-2", "daily-co"],
    "days": 90,
    "manual_signals_file": "${CLAUDE_SKILL_DIR}/../.tmp/competitor_manual_signals.json",
    "skip": []
}
CONFIGEOF

Step 8: Run the Tool

bash
python3 ${CLAUDE_SKILL_DIR}/scripts/competitor_signals.py \
    --config ${CLAUDE_SKILL_DIR}/../.tmp/competitor_signals_config.json \
    --output ${CLAUDE_SKILL_DIR}/../.tmp/competitor_signals.csv

The tool will:

1. Try Product Hunt API first (if PRODUCTHUNT_TOKEN is set)
2. Fall back to Apify PH scraper if API names are redacted
3. Search HN for all competitor names (stories + comments, last 90 days)
4. Load manual signals (case studies, testimonials, press)
5. Detect "switching signals" (highest priority — people saying they're moving to/from a competitor)
6. Deduplicate and score
7. Export CSV with switching signals highlighted

Phase 4: Analyze & Recommend

Step 10: Analyze Results

10a. Switching Signals (HIGHEST PRIORITY)

• These are people who publicly said they're switching from or evaluating alternatives to a competitor
• List every switching signal with full context
• These leads should be contacted IMMEDIATELY — they're in active evaluation
• Outreach angle: "I noticed you mentioned looking for alternatives to competitor] — here's how we compare"

10b. Case Study Companies

• These are PROVEN BUYERS in the category
• They've already committed budget to the problem space
• The decision-maker already said yes once — they'll consider alternatives if you offer something better
• Recommend enriching these companies via SixtyFour to find the current decision-maker

10c. Testimonial Authors

• Current users of the competitor who are vocal about it
• They may be satisfied (hard sell) OR they may have moved on since the testimonial
• Good for understanding what the competitor does well (competitive intel)
• If the testimonial mentions specific pain points or limitations, that's an opening

10d. Product Hunt Activity

• Commenters asking questions = evaluating the category
• Commenters with negative feedback = potentially dissatisfied
• Upvoters = interested in the space (weaker signal, higher volume)

10e. HN Discussion

• Commenters engaging with competitor stories = following the space
• People sharing experiences (positive or negative) = active users or evaluators

10f. Competitor-Level Analysis

• Which competitor generates the most signals? (largest audience = most opportunity)
• Which competitor has the most negative signals? (weakest competitor = easiest to displace)
• Are there any surprises? (unknown competitor getting a lot of attention?)

Step 11: Recommend Next Steps

1. Switching signals (immediate outreach):

• Enrich these people via SixtyFour NOW
• They're in active evaluation — speed matters
• Personalize based on what they said ("You mentioned specific pain]...")

1. Case study companies (account-based approach):

• These companies have budget for this category
• Use SixtyFour /enrich-company to understand them
• Find the decision-maker (not the person in the case study, who may have left)
• Outreach angle: "Companies like yours in industry] are switching to us because..."

1. PH commenters asking questions:

• They're early in evaluation
• Can reply directly on Product Hunt (public, non-intrusive)
• Or enrich and reach out privately

1. Cross-reference with other signals:

• If a company appears in competitor case studies AND in job signals (hiring for the role) -> they're invested but possibly scaling beyond the competitor
• If a person appears in competitor PH comments AND in community signals -> they're deeply researching the space

Step 12: Ask for Go-Ahead

> "Would you like me to: > 1. Enrich the switching signal leads immediately (highest priority) > 2. Enrich the case study companies and find decision-makers > 3. Cross-reference with data from other signal skills > 4. Scrape additional competitor pages for more signals > 5. Export for manual review first"

Signal Scoring

| Signal Type | Score | Priority | |---|---|---| | Switching From/To Competitor | 9 | IMMEDIATE — active evaluation | | Competitor Case Study Company | 9 | HIGH — proven buyer | | Competitor Testimonial Author | 8 | HIGH — current/past user | | PH Launch Commenter | 8 | HIGH — actively evaluating | | HN Post Commenter | 7 | MEDIUM — interested in space | | HN Post Author | 6 | MEDIUM — sharing competitor news | | PH Launch Upvoter | 6 | MEDIUM — interested but passive | | Tech Press Mention | 6 | MEDIUM — following the space | | PH Product Maker | 5 | LOW — competitor team member | | Changelog Engager | 5 | LOW — power user or evaluator |

Output Schema (Single Sheet)

| Column | Description | |--------|-------------| | person_name | Name or username of the person | | company | Company/headline from their profile | | signal_type | Internal signal type code | | signal_label | Human-readable label | | competitor | Which competitor this signal is about | | context | Comment text, case study excerpt, or description | | url | Link to the source (PH comment, HN post, case study page) | | profile_url | Link to the person's profile (PH, HN) | | date | Date of the signal | | signal_score | Weighted score | | source | Product Hunt API, Hacker News, Manual | | engagement | Upvotes/points on the post or comment |

Cost Estimates

| Source | Cost | Notes | |--------|------|-------| | Product Hunt API | Free | Developer token (may have name redaction) | | Product Hunt Apify | ~$5-10/run | Fallback if API names redacted | | Hacker News | Free | Algolia API | | Manual scraping | Free | Agent scrapes competitor websites | | Typical run | $0-10 | Free if PH API works; $5-10 if using Apify |

Lookback Period

Default: 90 days. Competitor launches and case studies have a longer shelf life than Reddit posts. Someone who commented on a competitor's PH launch 60 days ago is still a viable lead.