mgsci-methods
Use when choosing and defending the method for a Management Science (INFORMS) manuscript — selecting an analytical modeling approach (optimization, stochastic, game/economic theory) or an empirical design (econometric identification, lab/field experiment, structural, data science) that matches the question and the Department's standards. It designs; it does not execute the analysis (mgsci-data-analysis).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mgsci-methods --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.
# Methods & Design (mgsci-methods) ## When to trigger - The method may not match the question (wrong model class, weak identification) - You must choose between an analytical model and an empirical study — or combine them - A Department Editor or reviewer will probe whether the design can actually support the claim - You need to pick the field-appropriate standard for your Department lane Management Science has **no single dominant method by design**. Each **Department** sets its own field-appropriate expectations. Pick the lane the question demands, then meet that lane's rigor bar. ## Analytical-modeling design | Question / claim | Approach | |----------------------------------------------------|----------------------------------------------------------| | Optimal policy under constraints | Mathematical programming / optimization (LP/MIP/convex) | | Dynamics, queues, inventory, uncertainty | Stochastic processes, MDPs, dynamic programming | | Strategic interaction among decision-makers | Game theory / mechanism design; state the equilibrium concept | | Pricing/incentives under information frictions | Economic-theory model; contracts, signaling, screening | | Intractable models / p
- When to trigger
- Analytical-modeling design
- Empirical design
- The unifying rigor + relevance bar
- Reproducibility is designed in, not bolted on
- Department-fit map: which lane bar applies
- Worked micro-example (illustrative): designing an operations field experiment
- Referee-pushback patterns and the venue-specific fix
- Calibration anchors
- Execution bridge (StatsPAI / Stata MCP)
- Anti-patterns
- Output format
What does the mgsci-methods skill do?
Use when choosing and defending the method for a Management Science (INFORMS) manuscript — selecting an analytical modeling approach (optimization, stochastic, game/economic theory) or an empirical design (econometric identification, lab/field experiment, structural, data science) that matches the question and the Department's standards. It designs; it does not execute the analysis (mgsci-data-analysis).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill mgsci-methods --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.