| name | community-mining |
| description | Use during Founder mode SCOUT to extract structured signals from public communities. The 7-signal framework (pain, frequency, severity, willingness-to-pay, competitor, trend, contradictory) applied across Reddit, G2, Hacker News, ProductHunt, Crunchbase. Verbatim quotes only. Contradictory signals mandatory. Bottom-up TAM only. |
Community Mining
Community mining is structured listening. You scan public communities for evidence of pain, willingness-to-pay, competitor moves, and trend direction — then merge them into hypotheses backed by ≥2 independent signals.
The 7-signal framework
Every community pass extracts these seven signal types:
| Signal | What to look for | Weight if found |
|---|
| Pain | Complaints, workarounds described, time/money wasted, "every time I have to..." | High |
| Frequency | Recurring posts, temporal patterns ("every Monday"), repeated complaints across users | Medium |
| Severity | Emotional intensity ("I'm losing my mind"), quantified business impact ("cost us $40K") | High |
| Willingness-to-Pay | "I'd pay for...", current vendor spend mentioned, budget discussions | Critical |
| Competitor | Tools mentioned, switching stories, comparison threads, churn reasons | High |
| Trend | Growing/declining discussion volume, new entrants, shifting sentiment over months | Medium |
| Contradictory | "Not a problem", "Already solved", pushback, "you're overthinking this" | Mandatory |
Communities to scan
Scan ≥3 per cycle. Read comments, not just headlines — signals live in replies.
General
- Reddit — 3–5 relevant subreddits + keyword search. Read top 30 comments per thread, not just top 3.
- G2 Reviews — 1-star and 3-star reviews of incumbent tools. Skip 5-star (signal-poor for what's missing).
- Hacker News — keyword search, Ask HN + Show HN, read full comment threads.
- ProductHunt — competitor launch comments + adjacent products.
- Crunchbase — funding trends as investor-conviction proxy.
- Twitter/X — keyword + advanced search; favor threads over tweets.
B2B Enterprise add-ons
- Gartner Peer Insights, TrustRadius, LinkedIn Groups, PeerSpot.
Cybersecurity add-ons
- r/cybersecurity, r/netsec, Dark Reading, SecurityWeek, CyberScoop, CISO Series podcast notes, RSA/Black Hat session content, SANS Reading Room, PeerSpot.
Parallel execution
Community scans are embarrassingly parallel — independent WebFetch calls per source. Run them simultaneously. After all complete:
- Merge and deduplicate signals (multi-source signals weighted higher).
- Preserve ALL contradictory signals. Never drop them as outliers.
- Resolve conflicting competitor data using the most recent source.
Signal extraction format
Per signal:
| Signal ID | Type | Source | URL | Date | Verbatim Quote | Hypothesis |
| SIG-NNN | Pain | r/devops | https://… | YYYY-MM-DD | "exact quote, no paraphrasing" | H-NNN |
Verbatim or nothing. Paraphrasing introduces interpretation. The quote IS the evidence.
Contradictory signals are mandatory
Every SCOUT pass must surface contradictory signals. If your scan returned zero pushback on the hypothesis, your scan was incomplete. People disagree about everything on the internet — if no one's disagreeing about your hypothesis, you're searching wrong.
The market-scout agent will refuse to produce a SCOUT report without contradictory signals.
Bottom-up TAM only
Compute as (target buyer count in segment) × (deal size you can credibly extract). No analyst reports. No top-down BCG numbers.
Example: "12,000 SOC managers at companies of 500-5,000 employees in North America × $25K/year average ACV = $300M TAM". Each multiplier is checkable. The "300M" is bottom-up.
Top-down ("the cybersecurity market is $200B") tells you nothing about your reachable revenue.
Per-competitor analysis
Every competitor mentioned 2+ times in scans gets:
### [Name]
Category: Direct | Indirect | Adjacent
Founded / Funding / Target customer
Features / Pricing / Positioning
Strengths (evidence-based, with sources)
Weaknesses (evidence-based, with sources)
Why they might win ← REQUIRED
Counter-positioning assessment
Community sentiment (verbatim quotes with URLs)
The "Why they might win" is the load-bearing field. Every competitor must have one. If you can't articulate why a competitor might win, you don't understand the landscape.
Hypothesis generation rules
- New hypotheses require ≥2 independent signals from different sources.
- Every hypothesis has
falsification_criteria — specifically what evidence would disprove it. REQUIRED.
- Hypotheses are categorized: problem | pricing | market | wedge | competition | defensibility | buying_process.
- Priority: P1 (existential) | P2 (important) | P3 (nice to validate).
Common mistakes
- Reading top-level posts only. Discussions are in the replies.
- Filtering for confirmation. "I searched for [problem] and found 100 people complaining about it" — you searched for the answer you wanted.
- Treating one Reddit thread as a source type. Reddit + G2 + HN are still one source type collectively (community mining). Triangulate against interviews.
- Skipping contradictory signals. They're not noise. They're the most important data in the scan.
- Top-down TAM. "$200B market" is meaningless. Bottom-up or skip.
- Paraphrasing quotes. Verbatim or nothing.
How the market-scout agent enforces this
The market-scout agent will refuse to:
- Generate a hypothesis from <2 independent signals
- Use top-down TAM
- Skip the contradictory section in the SCOUT report
- Paraphrase community quotes
- Mark a competitor without "Why they might win"
When the agent surfaces a SCOUT report, you can trust the structure. The judgment about which signals matter is yours.
