From ors-skills
Formulates mathematical models and states provable results (theorems, propositions, lemmas) for Operations Research manuscripts. Use when defining optimization/stochastic/simulation models, assumptions, and contribution claims.
How this skill is triggered — by the user, by Claude, or both
Slash command
/ors-skills:ors-theory-developmentThe summary Claude sees in its skill listing — used to decide when to auto-load this skill
- You are turning an OR problem into a precise mathematical model.
Operations Research rewards a clean mathematical object and provable results. For the dominant OR/MS methodologies:
| Claim type | Use when |
|---|---|
| Theorem | A central, fully proved result (optimality, complexity, convergence rate, bound) |
| Proposition | A supporting proved result of lesser scope |
| Lemma | A technical step used inside a proof |
| Corollary | An immediate consequence |
| Conjecture | Stated explicitly as unproven; never disguised as a theorem |
Each formal statement needs explicit hypotheses; tie every assumption to where the
proof uses it (this is what ors-methods will then discharge).
OR requires an equation-free introduction: articulate the problem, the results, and their significance in words. Develop the model here, but draft the plain-language version of each result so the intro can state "we show that ..." without notation.
| Referee/AE remark | What it flags | Fix that meets the OR bar |
|---|---|---|
| "Model too stylized to matter" | structure stripped to triviality | restore the feature that makes the decision realistic; reprove |
| "Model too general to say anything" | no exploitable structure | impose convexity/submodularity/ergodicity that the application supports |
| "Assumption is convenient, not necessary" | proof-driven hypothesis | add a counterexample showing the result fails without it, or relax it |
| "This is a conjecture, not a theorem" | numerically-supported claim labeled Theorem | downgrade to Conjecture, or supply the proof in ors-methods |
| "Structural result not connected to the application" | theorem floats free of the decision | state which operational policy the structure prescribes |
Because Operations Research is the INFORMS flagship for rigorous OR/MS methodology, the editorial bar is a clean mathematical object whose structure both enables a theorem and maps to a decision. A model that admits no theorem reads as under-specified; one that admits a theorem but no operational reading reads as elegant but irrelevant — the two failure modes the table above pre-empts.
Stochastic-inventory control under correlated demand. Model: state = on-hand
inventory; action = order quantity; objective = expected discounted holding + backorder
cost; demand a Markov-modulated process (illustrative). Structure exploited:
K-convexity of the value function under the modulation. Result strength: Theorem 1
states an (s,S)-type policy is optimal (a proved central result); Proposition 1 gives
monotone comparative statics in the modulation rate (supporting); a Conjecture flags the
multi-product extension as unproven. Assumptions discipline: the bounded-demand
hypothesis is justified by capacity limits in the application and shown necessary via a
counterexample where unbounded demand breaks K-convexity. Plain-language for the
intro: "we show the optimal replenishment rule reduces to ordering up to a single
critical level that depends on the demand regime" — no notation, decision-relevant. This
gives ors-methods an explicit theorem-to-machinery handoff and keeps the structure
tethered to the operational policy.
【Model】variables / objective / constraints / process / estimand ...
【Structure exploited】convexity / submodularity / ergodicity / ...
【Results】Thm/Prop/Lemma list with one-line plain-language each
【Assumptions】each justified + necessity noted
【Plain-language for intro】"we show ..." (no notation)
【Next step】ors-methods
npx claudepluginhub brycewang-stanford/awesome-journal-skills --plugin ors-skillsSelects proof techniques, algorithm machinery, or simulation protocols for rigorous OR manuscripts. Invoked after model development to establish optimality, convergence, or statistical guarantees.
Builds formal models and develops theory for Production and Operations Management manuscripts, including analytical modeling, empirical hypothesis derivation, and behavioral experiment design.
Guides building the analytical model or operational mechanism at the core of an M&SOM manuscript — formulating decisions, objectives, and uncertainty, deriving structural results, or specifying operational mechanisms for empirical hypotheses. Adapts theory development to M&SOM's analytical/stochastic-modeling tradition.