ors-theory-development
Use when formulating the model and stating results for an Operations Research (OR) manuscript — defining the optimization/stochastic/simulation model, assumptions, and the theorems, propositions, and lemmas that carry the contribution. Builds the mathematical object and its claimed results; it does not prove them in detail (ors-methods) or run the computational study (ors-data-analysis).
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ors-theory-development --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.
# Model & Result Development (ors-theory-development) ## When to trigger - You are turning an OR problem into a precise mathematical model. - You need to decide what to claim — and as what (theorem vs. proposition vs. conjecture). - A reviewer will ask whether your assumptions are necessary or merely convenient. ## Build the model the OR way *Operations Research* rewards a clean mathematical object and **provable** results. For the dominant OR/MS methodologies: - **Optimization model:** state decision variables, objective, constraints, and the feasible region precisely. Identify structure (convexity, total unimodularity, submodularity, conic representability) — structure is what enables theorems and efficient algorithms. - **Stochastic / probabilistic model:** specify the probability space, the process (Markov chain, queue, MDP), the information/filtration, and the performance measure (steady-state cost, regret, tail probability). State stability/ergodicity conditions. - **Simulation model:** specify the stochastic dynamics and the estimand, and how a consistent estimator with quantifiable error will be obtained. - **Decision-analytic model:** specify the utility/risk measure, the
- When to trigger
- Build the model the OR way
- State results at the right strength
- Assumptions discipline
- Frame significance without equations (for the intro)
- Model-level pushback patterns and the OR fix
- Worked formulation vignette (illustrative)
- Anti-patterns
- Output format
What does the ors-theory-development skill do?
Use when formulating the model and stating results for an Operations Research (OR) manuscript — defining the optimization/stochastic/simulation model, assumptions, and the theorems, propositions, and lemmas that carry the contribution. Builds the mathematical object and its claimed results; it does not prove them in detail (ors-methods) or run the computational study (ors-data-analysis).
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ors-theory-development --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.