Expert Finder

SKILL.md

---
name: expert-finder
description: "Find domain experts, thought leaders, and subject-matter authorities on any topic. Searches Twitter and Reddit for people who demonstrate deep knowledge, frequent discussion, and above-average expertise in a specific field. Expert discovery, talent sourcing, researcher identification, and KOL (Key Opinion Leader) mapping."
homepage: https://xpoz.ai
metadata:
  {
    "openclaw":
      {
        "requires":
          {
            "bins": ["mcporter"],
            "skills": ["xpoz-setup"],
            "tools": ["web_search", "web_fetch"],
            "network": ["mcp.xpoz.ai"],
            "credentials": "Xpoz account (free tier) — auth via xpoz-setup skill (OAuth 2.1)",
          },
        "install": [{"id": "node", "kind": "node", "package": "mcporter", "bins": ["mcporter"], "label": "Install mcporter (npm)"}],
      },
  }
tags:
  - expert-finder
  - domain-expert
  - thought-leader
  - talent-sourcing
  - researcher
  - KOL
  - twitter
  - reddit
  - social-media
  - knowledge
  - authority
  - subject-matter-expert
  - people-search
  - intelligence
  - mcp
  - xpoz
---

# 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