Setup & Installation
Or with the ClawHub CLI, for registry-managed skill folders outside a full OpenClaw workspace:
Version History
Added setup section
What This Skill Does
Searches Twitter and Reddit to identify people with demonstrated, technical knowledge in a given domain. Uses tiered keyword queries, from core terms to deep jargon, to find authors who discuss a topic beyond surface level. Ranks candidates by domain depth, posting consistency, and peer recognition, then produces a scored report with profile data and representative quotes.
Follower counts and bio keywords miss people who actually know a subject — this finds them by analyzing what they post, not how they describe themselves.
When to use it
- Finding researchers to interview before writing a technical deep-dive
- Sourcing candidates for a niche engineering role
- Building a KOL list before a product or content launch
- Mapping who shapes opinion in a competitor's space
- Identifying active Reddit contributors with hands-on knowledge of a specific framework
Example Workflow
Here's how your AI assistant might use this skill in practice.
User asks: Find the top RLHF experts on Twitter
- 1Expands 'RLHF' into tiered queries from broad terms to deep jargon like 'reward model overfitting'
- 2Searches Twitter for posts matching each tier over the past 6 months
- 3Aggregates authors by post frequency across tiers, weighting technical-tier posts highest
- 4Fetches profiles for the top 20-30 authors and classifies each as Deep Expert, Practitioner, Thought Leader, Evangelist, or Educator
- 5Scores each candidate on domain depth, consistency, and peer recognition, then compiles a ranked report
Ranked expert report listing usernames, expert type, score out of 100, follower count, and a representative post quote for each
Requirements
Accounts, API keys, or tools you or your AI assistant may need to set up while using this skill.