expert-finder

# 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 ```bash 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: ```bash 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 ```markdown ## 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