community-signals

Community Signals

Extract high-intent leads from developer community forums by detecting buying signals in public discussions. Currently supports Hacker News and Reddit.

When to Use

• User wants to find leads from developer communities or forums
• User wants to identify people publicly expressing pain with competitors
• User wants to find people asking "what tool should I use for X"
• User mentions Hacker News, Reddit, Stack Overflow, or developer forums as lead sources
• User describes prospects who discuss tools, complain about solutions, or ask for recommendations in public forums
• User wants to find developers who built DIY/hacky solutions for problems the user's product solves

Prerequisites

• Python 3.9+ with requests and optionally python-dotenv
• Apify API token in .env (for Reddit scraping)
• No auth needed for Hacker News (free Algolia API)
• Working directory: the project root containing this skill

Phase 1: Collect Context

Step 1: Gather Product & ICP Information

Ask the user for the following. Do NOT proceed without this — the entire query generation depends on it.

"To find the right leads from developer communities, I need to understand:

1. What does your product do? (one-liner)

2. Who are your competitors? (list the main ones)

3. What specific problems does your product solve? (the pain points)

4. Who is your ideal buyer? (role, company type, tech stack)

5. Any specific technologies or keywords associated with your space?"

If the user has already provided this context (e.g., from running the github-repo-signals skill), use that — don't ask again.

Phase 2: Generate Search Queries

Step 2: Generate Queries Across 9 Categories

Based on the user's product info, generate 3-5 search queries per category. These are the fixed categories — do not skip any:

Category 1: Alternative Seeking (intent score: 9) People actively looking to switch tools.

• Pattern: "[competitor] alternative", "alternative to [competitor]", "looking for [product type]"
• Example: "twilio alternative", "alternative to agora", "looking for video SDK"

Category 2: Competitor Pain (intent score: 8) People frustrated with a specific competitor.

• Pattern: "[competitor] issues", "frustrated with [competitor]", "[competitor] doesn't support"
• Example: "twilio video quality issues", "frustrated with agora pricing", "vonage api unreliable"

Category 3: Problem Space Questions (intent score: 6) People trying to solve the exact problem the product addresses.

• Pattern: "how to [thing product does]", "best way to [problem]", "recommendations for [category]"
• Example: "how to add video calling to app", "best webrtc framework", "real-time communication SDK"

Category 4: Tool Comparison (intent score: 8) People actively comparing options — in buying mode.

• Pattern: "[competitor A] vs [competitor B]", "comparing [tools]", "which [product type] should I use"
• Example: "twilio vs agora", "comparing video APIs", "which webrtc platform"

Category 5: DIY / Built Own Solution (intent score: 9) People who built a custom solution — validated the need, would pay for a proper product.

• Pattern: "I built my own [thing]", "Show HN: [thing product replaces]", "custom [solution type]"
• Example: "I built my own video conferencing", "Show HN: open source video call", "custom webrtc server"

Category 6: Scaling Challenges (intent score: 7) People hitting limits that the product solves.

• Pattern: "[problem] at scale", "scaling [thing]", "[thing] breaks with many users"
• Example: "webrtc scaling issues", "video calls lagging with 50+ participants", "scaling real-time communication"

Category 7: Migration Intent (intent score: 9) People who have already decided to leave — looking for where to go.

• Pattern: "migrating from [competitor]", "moving away from [competitor]", "switching from [competitor]"
• Example: "migrating from twilio video", "moving away from agora", "switching video API providers"

Category 8: Budget / Pricing Pain (intent score: 7) Cost is the trigger — open to cheaper or better-value alternatives.

• Pattern: "[competitor] too expensive", "[competitor] pricing", "cheaper alternative to [competitor]"
• Example: "twilio too expensive", "agora pricing 2026", "cheaper video API"

Category 9: Feature Gap Complaints (intent score: 7) Needs something their current tool doesn't do — and the user's product does.

• Pattern: "does [competitor] support [feature]", "[competitor] missing [feature]", "wish [competitor] had"
• Example: "does twilio support recording", "agora missing breakout rooms", "wish vonage had better docs"

Step 3: Discover Relevant Subreddits

Do a web search to find subreddits where the user's ICP is active. Search for:

• "[product category] subreddit"
• "[technology] subreddit"
• "[competitor name] subreddit"

Common developer subreddits to consider (pick the relevant ones):

• r/programming, r/webdev, r/devops, r/selfhosted
• r/kubernetes, r/aws, r/googlecloud, r/azure
• r/node, r/python, r/golang, r/rust
• r/startups, r/SaaS, r/entrepreneur
• r/sysadmin, r/networking
• Technology-specific: r/VOIP, r/machinelearning, r/dataengineering, etc.

Select 5-10 subreddits most relevant to the user's space.

Step 4: Present Queries for Review

Present ALL generated queries to the user in a structured table:

Category                  | Queries
--------------------------|------------------------------------------
Alternative Seeking       | "twilio alternative", "agora alternative", ...
Competitor Pain           | "twilio issues", "frustrated with agora", ...
...                       | ...

Subreddits to scan: r/webdev, r/VOIP, r/programming, ...

Ask:

"Here are the search queries I've generated. Would you like to:

1. Run with these as-is

2. Add or remove specific queries

3. Add or remove subreddits

Estimated cost: HN is free. Reddit via Apify will cost approximately $[estimate based on query count x ~$0.05 per query]."

Wait for user approval before proceeding.

Step 5: Save Queries File

Once approved, save the queries as a JSON file:

cat > ${CLAUDE_SKILL_DIR}/../.tmp/community_queries.json << 'QUERIESEOF'
{
    "product": "Product Name",
    "queries": [
        {"category": "alternative_seeking", "query": "twilio alternative"},
        {"category": "alternative_seeking", "query": "agora alternative"},
        {"category": "competitor_pain", "query": "twilio video quality issues"}
    ],
    "subreddits": ["r/webdev", "r/VOIP", "r/programming"]
}
QUERIESEOF

Phase 3: Execute Scan

Step 6: Verify Environment

python3 -c "import requests; print('OK')"

Step 7: Run the Tool

python3 ${CLAUDE_SKILL_DIR}/scripts/community_signals.py \
    --queries ${CLAUDE_SKILL_DIR}/../.tmp/community_queries.json \
    --days 30 \
    --max-reddit-posts 50 \
    --max-reddit-comments 20 \
    --output ${CLAUDE_SKILL_DIR}/../.tmp/community_signals.csv

The tool will:

1. Search Hacker News (stories + comments) for all queries — free
2. Search Reddit via Apify for all queries + scan subreddits — pay per result
3. Filter to last 30 days
4. Deduplicate users across platforms
5. Score by intent strength, signal count, category diversity, and cross-platform presence
6. Fetch HN user profiles (karma, bio) — free
7. Export two CSV files: _users.csv and _signals.csv

Optional flags:

• --skip-reddit — only search HN (free, for testing)
• --skip-hn — only search Reddit
• --days 7 — narrower time window for very fresh signals

Phase 4: Analyze & Recommend

Step 9: Analyze the Results

Read the output CSV files and present a structured briefing:

9a. Overall Stats

• Total signals found (HN + Reddit)
• Unique users
• Split by platform (HN vs Reddit)
• Cross-platform matches (same username on both)

9b. Signal Category Breakdown

• How many signals per category
• Which categories produced the most