analyze-results
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
Profile →npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill analyze-results --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.
# Analyze Experiment Results Analyze: $ARGUMENTS ## Workflow ### Step 1: Locate Results Find all relevant JSON/CSV result files: - Check `figures/`, `results/`, or project-specific output directories - Parse JSON results into structured data ### Step 2: Build Comparison Table Organize results by: - **Independent variables**: model type, hyperparameters, data config - **Dependent variables**: prima
What does the analyze-results skill do?
Analyze ML experiment results, compute statistics, generate comparison tables and insights. Use when user says "analyze results", "compare", or needs to interpret experimental data.
How do I install it?
Run `npx skills add brycewang-stanford/Auto-Empirical-Research-Skills --skill analyze-results --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.