replication-driven-research
Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Enforces end-to-end reproducibility — every number in the paper must be regenerable from raw data by a script with a fixed seed. Replaces TDD for the research domain.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill replication-driven-research --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.
# Replication-Driven Research ## Overview This skill is the research-domain analogue of test-driven development. The core philosophy is the same as TDD: evidence before claims, automated verification, invalidation on input change. No result is valid until the pipeline runs end-to-end without error. No number enters the paper until a script regenerates it from raw data. This is the single most impo
What does the replication-driven-research skill do?
Use when starting empirical analysis, creating a data pipeline, generating results, or when data or model specifications change. Enforces end-to-end reproducibility — every number in the paper must be regenerable from raw data by a script with a fixed seed. Replaces TDD for the research domain.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill replication-driven-research --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.