mgsci-data-analysis
Use when executing and reporting the analysis for a Management Science (INFORMS) manuscript — proving and numerically verifying analytical results, or estimating and validating empirical models (identification, robustness, inference) to the standard of the relevant Department, and preparing a Data-and-Code-Disclosure-ready replication package. It executes; it does not design the study (mgsci-methods) or frame the contribution (mgsci-contribution-framing).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mgsci-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.
# Analysis & Verification (mgsci-data-analysis) ## When to trigger - Results are ready to be derived/estimated and reported - You are unsure whether the analysis actually supports the claim - Reviewers will probe proof correctness, identification, or robustness - You must assemble the replication package the Data Editor will verify Because Management Science is **bimethodological**, "analysis" means proving/computing results in the analytical lane or estimating/validating in the empirical lane. Both are held to their Department's rigor bar. ## Analytical lane — prove, then illustrate - **Proofs first.** Every proposition/theorem needs a correct, checkable proof (main text or appendix). Reviewers verify the algebra and the logic. - **Comparative statics** carry the managerial insight — report how the optimal policy/equilibrium moves with each primitive, with sign and intuition. - **Numerical illustration.** Where closed forms run out, provide computational examples; report parameter ranges and confirm the qualitative result is not knife-edge. - **Robustness of the model.** Show the insight survives relaxed assumptions / alternative timing / heterogeneity. - **Reproducible numerics.*
- When to trigger
- Analytical lane — prove, then illustrate
- Empirical lane — identify, estimate, stress-test
- Data and Code Disclosure (mandatory, verified)
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Two-lane evidence bar at Management Science
- Evidence pass for Management Science
- Output format
What does the mgsci-data-analysis skill do?
Use when executing and reporting the analysis for a Management Science (INFORMS) manuscript — proving and numerically verifying analytical results, or estimating and validating empirical models (identification, robustness, inference) to the standard of the relevant Department, and preparing a Data-and-Code-Disclosure-ready replication package. It executes; it does not design the study (mgsci-methods) or frame the contribution (mgsci-contribution-framing).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mgsci-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.