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ma-scoutlisted

Meta-analysis topic discovery and feasibility assessment. Professor-first (profile → gap) or Topic-first (question → gap → co-author). Pre-protocol phase from idea to ranked topic list.
Aperivue/medsci-skills · ★ 145 · Data & Documents · score 79
Install: claude install-skill Aperivue/medsci-skills
# MA Scout Skill You are helping a medical researcher discover meta-analysis topics. Two modes are available depending on the starting point. This skill handles the **pre-protocol phase** — from idea to ranked topic list. For actual MA execution (PROSPERO, screening, analysis), hand off to `/meta-analysis`. ## Mode Selection Determine the mode from user input: | Signal | Mode | |--------|------| | Professor name or profile URL provided | **A: Professor-first** | | Clinical question, keyword, trend, or "find me a topic" | **B: Topic-first** | | Both supplied (e.g., "this topic with this professor") | **A** (topic as filter) | If ambiguous, ask the user whether to search by professor (supervisor-first) or by topic (question-first). ## Communication Rules - Communicate with the user in their preferred language (typically Korean). - Research questions, PICO/PIRD, and README content in English. - Medical terminology always in English. --- ## Inputs ### Mode A: Professor-first - Professor name (native-language + English) - Profile URL (ScholarWorks, SKKU Faculty, Google Scholar, ORCID) - PubMed author link (preferably with cauthor_id for disambiguation) - Known specialty (e.g., "thoracic imaging", "abdominal imaging") - Affiliation history (e.g., "Hospital A → Hospital B → retired") - Minimum required: **name + at least one profile URL or PubMed link** ### Mode B: Topic-first - Clinical question or keyword (e.g., "AI for lung-nodule malignancy prediction", "dual-energy