| name | competitor-signals |
| description | Extract leads from competitor product activity — Product Hunt commenters/upvoters, HN posts about competitors, case studies, testimonials, tech press, and switching signals. Detects people actively switching from competitors as highest-priority leads. |
| user-invocable | true |
| allowed-tools | Bash, Read, Write, Edit, Grep, Glob, WebFetch, WebSearch |
| argument-hint | ["config-json-path"] |
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:
- Who are your competitors? (product names and company names)
- Do you know their Product Hunt slugs? (the URL path on producthunt.com/posts/SLUG)
- Any specific competitor launches or announcements you've seen recently?
- 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:
- Navigate to the page using web fetch or Chrome DevTools
- Extract all customer company names and any associated person names/quotes
- Note the case study URL for reference
For each competitor's testimonials page:
- Extract: person name, title, company, quote text
- 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:
[
{
"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
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
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:
- Try Product Hunt API first (if
PRODUCTHUNT_TOKEN is set)
- Fall back to Apify PH scraper if API names are redacted
- Search HN for all competitor names (stories + comments, last 90 days)
- Load manual signals (case studies, testimonials, press)
- Detect "switching signals" (highest priority — people saying they're moving to/from a competitor)
- Deduplicate and score
- 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
-
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]...")
-
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..."
-
PH commenters asking questions:
- They're early in evaluation
- Can reply directly on Product Hunt (public, non-intrusive)
- Or enrich and reach out privately
-
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:
- Enrich the switching signal leads immediately (highest priority)
- Enrich the case study companies and find decision-makers
- Cross-reference with data from other signal skills
- Scrape additional competitor pages for more signals
- 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.
