Expert Finder

通过 xpoz MCP 服务：先做四层查询扩展，再按关键词检索 Twitter 帖子并按作者频次聚合，随后抓取用户资料，将人物分类为深度专家、意见领袖、实践者、传播者、教育者，并按维度深度、持续性、同行认可、广度、资历打 0-100 分，最后输出带引用与点赞数的 Markdown 专家报告。

atyachin

热度 88.07

最近更新 2026-05-17 11:52:24

分类：搜索依赖：MCP 服务 / 第三方 API / 读写文件应用场景：研究 / 社交媒体 / 推荐 / 报告撰写

安装技能

复制下面的话术发给你的 Agent，它会通过 secure-skills 检索并安装该技能。

执行命令安装此 skill： npx secure-skills-cli add expert-finder

Metadata

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

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