ijoc-theory-development
Use when the algorithm or model formulation, its correctness, and its theoretical guarantees are the bottleneck for an INFORMS Journal on Computing (IJOC) manuscript. Pins down formulation, complexity, and what the method provably does before experiments are finalized; it does not run the experiments.
Profile →npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijoc-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.
# Algorithm & Model Formulation (ijoc-theory-development) ## When to trigger - An algorithm "works" empirically but its **statement, invariants, and termination** are not written down rigorously - A formulation is proposed but its **validity** (the model exactly captures the problem; the cuts are valid; the relaxation is correct) is asserted, not argued - A referee asks for **complexity, convergence, approximation ratio, or correctness** and the paper has none - A heuristic or ML method needs the **theoretical scaffolding** (what it guarantees, when it can fail) that distinguishes IJOC from a pure benchmark paper ## What "theory" means at IJOC IJOC is not a pure-theory journal, but it expects the **method to be defined and defended**, not just demonstrated. The advance is computational, yet referees want to know *why the method is correct* and *what it provably achieves* before they trust the experiments. Match the rigor to the archetype — an exact method needs validity and finiteness; a heuristic needs a clear procedure and, where possible, bounds; an ML-for-OR method needs a stated learning task and a guarantee or a falsifiable claim. The theory and the experiments must agree: a
- When to trigger
- What "theory" means at IJOC
- Branch paths
- Branch A: Exact methods (B&B / B&C / B&P, decomposition)
- Branch B: Heuristics / metaheuristics / matheuristics
- Branch C: Machine learning for OR / learning-to-optimize
- Branch D: Simulation / computational probability
- Checklist
- Anti-patterns
- Worked vignette (illustrative)
- Output format
What does the ijoc-theory-development skill do?
Use when the algorithm or model formulation, its correctness, and its theoretical guarantees are the bottleneck for an INFORMS Journal on Computing (IJOC) manuscript. Pins down formulation, complexity, and what the method provably does before experiments are finalized; it does not run the experiments.
How do I install it?
Run `npx skills add brycewang-stanford/Awesome-Journal-Skills --skill ijoc-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.