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.
Best use case
expert-finder is best used when you need a repeatable AI agent workflow instead of a one-off prompt.
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.
Teams using expert-finder should expect a more consistent output, faster repeated execution, less prompt rewriting.
When to use this skill
- You want a reusable workflow that can be run more than once with consistent structure.
When not to use this skill
- You only need a quick one-off answer and do not need a reusable workflow.
- You cannot install or maintain the underlying files, dependencies, or repository context.
Installation
Claude Code / Cursor / Codex
Manual Installation
- Download SKILL.md from GitHub
- Place it in
.claude/skills/expert-finder/SKILL.mdinside your project - Restart your AI agent — it will auto-discover the skill
How expert-finder Compares
| Feature / Agent | expert-finder | Standard Approach |
|---|---|---|
| Platform Support | Not specified | Limited / Varies |
| Context Awareness | High | Baseline |
| Installation Complexity | Unknown | N/A |
Frequently Asked Questions
What does this skill do?
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.
Where can I find the source code?
You can find the source code on GitHub using the link provided at the top of the page.
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SKILL.md Source
# 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 idealRelated Skills
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