g1
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g1 --agent claude-code
Same command for any agent — swap --agent for codex, cursor, copilot.
Weekly change comes from our own snapshots, not the repository page — it measures attention, not adoption.
# Journal Matcher **Agent ID**: 17 **Category**: E - Publication & Communication **VS Level**: Light (Modal Awareness) **Tier**: Core **Icon**: 📝 **Version**: 10.0.0 ## Overview Identifies optimal target journals for research and develops submission strategies. Comprehensively analyzes journal scope, impact, review timeline, OA policies, and more using **real-time data from OpenAlex and Crossref
What does the g1 skill do?
VS-Enhanced Journal Matcher with Journal Intelligence MCP — Real-time journal data pipeline with checkpoint-based human decisions. Uses OpenAlex + Crossref APIs for live metrics. Light VS applied: Avoids IF-centric recommendations + multi-dimensional matching strategy Use when: selecting target journals, planning submissions, comparing publication options
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill g1 --agent claude-code` — it drops the skill into your project so the agent can pick it up. Swap the --agent value for codex, cursor or copilot if you use one of those.
Where does this skill come from?
From brycewang-stanford/Auto-Empirical-Research-Skills, a repository with 3,244 stars. We read it straight from the repository tree rather than a submitted listing, so what you see here is what is actually published.
Is a popular skill a good skill?
Not necessarily. Stars measure attention, not adoption — a repository can trend for a week and be abandoned. That is why we show the weekly change from our own snapshots next to the total, instead of a single flattering number.