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:

bash
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

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

Step 7: Run the Tool

bash
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 results
• Which categories had the highest-engagement posts (upvotes, comments)

9c. Top Subreddits Discovered

• Which subreddits appeared most frequently
• This tells the user where their prospects hang out — valuable for community marketing, not just outreach

9d. Highest-Intent Users

• List top 15-20 users by composite score
• For each: username, platform, categories they appeared in, sample post/comment, engagement
• Flag cross-platform users prominently

9e. Common Themes

• What are people specifically asking for or complaining about?
• Any patterns in the pain points that the user's product addresses?
• Any surprising findings (e.g., a competitor getting mentioned negatively much more than others)?

Step 10: Recommend Next Steps

Based on findings + user's product context:

1. If strong signals found (>50 high-intent users):

• Recommend enriching top users via SixtyFour
• For HN users: use their HN bio/karma + username for enrichment context
• For Reddit users: username is the only identifier — enrichment hit rates may be lower
• Suggest starting with HN users (more likely to have real names in bio)

1. If cross-platform matches found:

• These are highest priority — someone active on both HN and Reddit in your space is deeply engaged
• Recommend enriching these first

1. If specific subreddits emerged as hotspots:

• Recommend ongoing monitoring of those subreddits
• Suggest the user consider community engagement (commenting, answering questions) in those subreddits

1. If "alternative seeking" or "migration intent" signals dominate:

• These are the most time-sensitive leads — they're actively evaluating RIGHT NOW
• Recommend immediate outreach

1. If "DIY / built own" signals found:

• These are the highest-quality leads — they've validated the need
• Recommend personalized outreach referencing their project

1. Always include:

• Cost estimate for enrichment
• Suggested outreach angle per signal category
• Reminder that community forum users respond better to helpful engagement than cold outreach

Step 11: Ask for Go-Ahead

> "Would you like me to: > 1. Enrich the top N] users via SixtyFour (estimated cost: $X) > 2. Run a deeper scan on the hotspot subreddits > 3. Export this data for manual review first > 4. Combine these results with GitHub signals data (if available)"

Wait for user confirmation.

Output Schema

community_signals_users.csv — One row per unique user across all platforms

| Column | Description | |--------|-------------| | username | Forum username | | platform | hackernews or reddit | | composite_score | Overall lead score (intent + diversity + cross-platform) | | intent_score | Sum of category-weighted intent scores | | signal_count | Number of matching posts/comments | | categories | Which signal categories they appeared in | | platforms_active | Which platforms they were found on | | subreddits | Reddit subreddits they posted in | | hn_karma | HN karma score (HN users only) | | hn_bio | HN profile bio (HN users only) | | total_engagement | Sum of upvotes + comments across their signals | | first_seen | Earliest matching post/comment | | latest_seen | Most recent matching post/comment | | sample_url | Link to one of their matching posts |

community_signals_signals.csv — One row per matching post/comment

| Column | Description | |--------|-------------| | platform | hackernews or reddit | | author | Username | | category | Signal category code | | category_label | Human-readable category name | | content_type | story, comment, or post | | title | Post/story title | | text | Post/comment body (truncated) | | subreddit | Reddit subreddit (if applicable) | | score | Upvotes | | num_comments | Comment count | | created_at | Date posted | | query_matched | Which search query found this | | url | Permalink to the post/comment |

Scoring System

Intent scores by category: | Category | Score per Signal | |----------|-----------------| | Alternative Seeking | 9 | | DIY / Built Own | 9 | | Migration Intent | 9 | | Competitor Pain | 8 | | Tool Comparison | 8 | | Scaling Challenge | 7 | | Budget / Pricing | 7 | | Feature Gap | 7 | | Problem Space | 6 |

Composite score bonuses:

• +2 per unique category the user appeared in (diversity)
• +10 if user found on multiple platforms (cross-platform)
• +2 per signal (capped at +10)

Cost Estimates

| Platform | Cost | Notes | |----------|------|-------| | Hacker News | Free | Algolia API, 10k req/hr | | Reddit (Apify) | ~$0.004/result + $0.04/run | Pay per result | | Typical run (45 queries) | ~$5-10 total | HN free + Reddit ~$5-10 |

Limitations

• Reddit comments: Can't search comments directly — finds posts first, then fetches comments on those posts. Some comment-only discussions may be missed.
• Reddit date filter: No native date range parameter in Apify actor. Filtering happens in post-processing using created_at timestamps.
• User identity: Forum usernames are pseudonymous. Enrichment hit rates will be lower than GitHub (where people often use real names). HN users are more identifiable (many put real names in bio).
• Rate limits: HN Algolia: 10k req/hr. Apify: depends on plan.