Answering Research Questions

RC native tools — this skill's pipeline ships as four built-in RC tools. Call them directly; do not shell out to any rp.py script or write curl:

• rp_search({ query, limit?, min_year? }) — Scopus relevance search enriched with OpenAlex abstracts + OA PDF links. The Elsevier key is built in.
• rp_abstracts({ dois }) — batch abstracts + OA links for a DOI list (OpenAlex, no key).
• rp_cite({ doi, direction?, limit? }) — citation traversal (direction: both / backward / forward).
• rp_fulltext({ doi, out? }) — OA full text (Elsevier ScienceDirect OA → OpenAlex OA fallback); pass out to also save the text to a file.

Results return inline as JSON (there is no --json <file> flag). To persist a result set, save the returned JSON with the workspace file tools.

Overview

Orchestrate the complete research workflow from query to findings.

Core principle: Systematic, trackable, comprehensive. Search → Evaluate → Traverse → Synthesize.

Announce at start: "I'm using the Answering Research Questions skill to find [specific data] about [topic]."