ors-data-analysis
Use when running and reporting the computational study for an Operations Research (OR) manuscript — benchmark instances, baselines, reproducible experiments, statistical care for stochastic output, and the ORJournal code-and-data reproducibility workflow. Executes and reports the numerical evidence; it does not prove the results (ors-methods) or lay out the exhibits (ors-tables-figures).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ors-data-analysis --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.
# Computational Study & Reproducibility (ors-data-analysis) ## When to trigger - Theory is in place and you need numerical evidence that the method works and scales. - You must benchmark against credible baselines on standard instances. - You are preparing the code/data deposit for the ORJournal reproducibility review. ## Design a defensible computational study *Operations Research* judges computation as evidence supporting a methodological claim, not as the contribution by itself. Make it convincing: - **Instances:** use recognized benchmark libraries (e.g., MIPLIB, TSPLIB, DIMACS, QPLIB) plus, where relevant, instances from the motivating application; report sizes and characteristics so difficulty is visible. - **Baselines:** compare against the *closest* prior methods and a strong off-the-shelf solver, not a weak strawman. Tie experiments to the claims in `ors-literature-positioning`. - **Metrics:** report what the theory predicts — optimality gap, solution time, iterations/oracle calls, scaling with size, and where relevant the quality at a fixed budget. Show how empirics corroborate proved bounds/rates. - **Reporting:** specify hardware, solver versions, time limits, and termi
- When to trigger
- Design a defensible computational study
- Statistical care for stochastic output
- The ORJournal code-and-data workflow (mandatory where applicable)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
What does the ors-data-analysis skill do?
Use when running and reporting the computational study for an Operations Research (OR) manuscript — benchmark instances, baselines, reproducible experiments, statistical care for stochastic output, and the ORJournal code-and-data reproducibility workflow. Executes and reports the numerical evidence; it does not prove the results (ors-methods) or lay out the exhibits (ors-tables-figures).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ors-data-analysis --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/Awesome-Journal-Skills, a repository with 909 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.