ResearchClaw
OpenClaw-powered agent that finds and ranks researchers from papers, writes plain-English hiring theses, and drafts cold emails referencing their work.
🛡️ AgentReady threat assessment
MAESTRO 7-layer threat model + OWASP AIVSS risk score for ResearchClaw, derived from its capabilities.
These scores are auto-generated from public information (the agent's own listing, docs, and repository) using the canonical OWASP AIVSS formula and the MAESTRO framework — an estimate for guidance, not a penetration test, audit, or certification. See the scoring methodology. Are you the vendor? Factual corrections are free.
Overview
ResearchClaw is an OpenClaw-powered research hiring agent that helps teams identify academics whose published work matches a specific product or AI use case. You describe your use case in plain English, and the system decomposes it into structured queries, pulls papers and author metadata from sources like Semantic Scholar, arXiv, and OpenAlex, and ranks candidates using embedding similarity combined with citation- and recency-weighted scoring. It then generates an LLM-written hiring thesis for each researcher (translating academic jargon into product-relevant reasoning) and drafts hyper-personalized cold emails that reference specific papers and contributions, with options like CSV export and outreach automation.
Key features
- academic search
- researcher discovery
- semantic search
- embedding similarity
- cosine similarity
- citation-weighted scoring
- hiring thesis
- cold outreach
- arxiv search
- openalex
Use cases
- Finding researchers whose published work directly aligns with your technical product challenge.
- Ranking candidates using embedding similarity with citation- and recency-weighted scoring.
- Converting dense academic work into plain-English hiring theses tied to your use case.
- Drafting personalized outreach emails that reference specific papers and contributions.