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.From its SKILL.md
npx -y skills add XPOZpublic/xpoz-clawhub-skills --skill expert-finderAssembled from the repository path, not quoted from the project. Check it against their README if it does not work.
2 things to look at
- no licenseNo license file was found in the repository. Code published without one is not open source by default, so using it at work is a question for whoever answers licensing questions where you are.
- 4 stars4 stars. Stars are a popularity signal and not a quality one, but at this level it is likely that nobody has read this closely except its author, and you would be relying on your own review.
SKILL.md
2.9 KB, 505 tokens by cl100k_base, as published. Nobody here has run it
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: β₯3 posts, β₯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: π¬ Deep Expert (uses Tier 2 naturally) | π‘ Thought Leader (trends, large audience) | π οΈ Practitioner ("I built") | π£ Evangelist (aggregates) | π Educator (explains)
Score (0-100): Domain depth 30%, consistency 20%, peer recognition 20%, breadth 15%, credentials 15%.
Phase 4: Report
## Expert Report: [Domain] β X,XXX posts analyzed
#### π₯ @username β π¬ Deep Expert (92/100)
**Followers:** 12.4K | **Why:** 23 posts on reward optimization, advanced terminology
**Key:** "[quote]" β β€οΈ 342
Tips
Narrow > broad | Tier 2 jargon = gold | Reddit comments reveal depth | 6mo window ideal
What ships with it
Read from the repository
Just SKILL.md. No reference files, no scripts.