dvcrn Expert Finder
Find domain experts by analyzing social media activity. Expands topics into search terms, searches Twitter/Reddit, classifies by type, and ranks.
Setup
Run xpoz-setup skill. Verify: mcporter call xpoz.checkAccessKeyStatus
4-Phase Process
Phase 1: Query Expansion
Research domain with web_search/web_fetch. Generate tiered queries:
| Tier | Purpose | Example (RLHF) |
|---|---|---|
| Tier 1: Core | Exact terms | "RLHF" |
| Tier 2: Technical | Deep jargon (strongest signal) | "reward model overfitting" |
| Tier 3: Adjacent | Related | "preference optimization" |
| Tier 4: Discussion | Opinion | "RLHF vs" |
Phase 2: Search & Aggregate
mcporter call xpoz.getTwitterPostsByKeywords query='"RLHF"' startDate="<6mo>"
mcporter call xpoz.checkOperationStatus operationId="op_..." # Poll every 5s
Download CSVs via dataDumpExportOperationId (64K rows). Build author frequency: ≥3 posts, ≥2 tiers. Weight Tier 2 highest.
Phase 3: Classify & Score
Fetch profiles for top 20-30:
mcporter call xpoz.getTwitterUser identifier="user" identifierType="username"
Types: 🔬 Deep Expert (uses Tier 2 naturally) | 💡 Thought Leader (trends, large audience) | 🛠️ Practitioner ("I built") | 📣 Evangelist (aggregates) | 🎓 Educator (explains)
Score (0-100): Domain depth 30%, consistency 20%, peer recognition 20%, breadth 15%, credentials 15%.
Phase 4: Report
## Expert Report: [Domain] — X,XXX posts analyzed
#### 🥇 @username — 🔬 Deep Expert (92/100)
**Followers:** 12.4K | **Why:** 23 posts on reward optimization, advanced terminology
**Key:** "[quote]" — ❤️ 342
Tips
Narrow > broad | Tier 2 jargon = gold | Reddit comments reveal depth | 6mo window ideal