i0
Systematic Review Pipeline Orchestrator - Coordinates systematic literature review automation Manages the complete 7-stage PRISMA 2020 pipeline from research question to RAG system Delegates to specialized agents (I1, I2, I3) while enforcing human checkpoints Use when: conducting systematic reviews, building knowledge repositories, PRISMA automation
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill i0 --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.
## ⛔ Prerequisites (v8.2 — MCP Enforcement) No prerequisites required for this agent. ### Checkpoints During Execution - 🔴 SCH_DATABASE_SELECTION → `diverga_mark_checkpoint("SCH_DATABASE_SELECTION", decision, rationale)` - 🔴 SCH_SCREENING_CRITERIA → `diverga_mark_checkpoint("SCH_SCREENING_CRITERIA", decision, rationale)` - 🟠 SCH_RAG_READINESS → `diverga_mark_checkpoint("SCH_RAG_READINESS", deci
What does the i0 skill do?
Systematic Review Pipeline Orchestrator - Coordinates systematic literature review automation Manages the complete 7-stage PRISMA 2020 pipeline from research question to RAG system Delegates to specialized agents (I1, I2, I3) while enforcing human checkpoints Use when: conducting systematic reviews, building knowledge repositories, PRISMA automation
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill i0 --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.