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Expert Finder

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.

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版本1.4.0
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技能说明


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:

TierPurposeExample (RLHF)
Tier 1: CoreExact terms"RLHF"
Tier 2: TechnicalDeep jargon (strongest signal)"reward model overfitting"
Tier 3: AdjacentRelated"preference optimization"
Tier 4: DiscussionOpinion"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

如何使用「Expert Finder」?

  1. 打开小龙虾AI(Web 或 iOS App)
  2. 点击上方「立即使用」按钮,或在对话框中输入任务描述
  3. 小龙虾AI 会自动匹配并调用「Expert Finder」技能完成任务
  4. 结果即时呈现,支持继续对话优化

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