A professional-grade assistant designed to transform complex business and engineering problems into rigorous mathematical optimization models.
Install & Download
1. ClawHub CLI
The fastest way to install a skill directly from the registry.
npx clawhub@latest install or-modeling
2. Manual Installation
Copy the skill folder to one of these locations
~/.openclaw/skills/ <project>/skills/ Priority: Workspace > Local > Bundled
3. Prompt Installation
Copy this prompt to OpenClaw to install it automatically.
Help me install or-modeling using Clawhub. If Clawhub is not installed, install it first (npm i -g clawhub).
Prefer to download?
Get the raw skill files in a ZIP archive.
What is Operations Research Modeling Assistant?
The OR-Modeling skill is a robust framework for developers and researchers to bridge the gap between verbal problem descriptions and formal mathematical formulations. It specializes in identifying decision variables, objective functions, and constraints across various domains such as supply chain optimization, scheduling, and game theory.
By integrating academic research capabilities with structural self-checking, this skill ensures that generated models are not only theoretically sound but also computationally feasible. It utilizes Openclaw Skills to streamline the modeling lifecycle from initial problem understanding to final LaTeX documentation, making it an essential asset for operations research and data science workflows.
Operations Research Modeling Assistant Use Cases
- Converting business logic into Linear Programming (LP) or Mixed-Integer Programming (MIP) models.
- Formulating vehicle routing (VRP) or traveling salesman problems (TSP) for logistics.
- Conducting literature reviews to identify state-of-the-art modeling approaches for niche industries.
- Validating mathematical models for physical feasibility and logical consistency.
- Generating high-quality LaTeX formulas for academic papers or technical reports.
How Operations Research Modeling Assistant Works
- Problem Identification: Analyzes the user input across six dimensions including optimization targets and decision variables.
- Literature Research: Executes paper_search.py to find relevant academic papers via OpenAlex or Semantic Scholar to ground the model in existing research.
- Reverse Validation: Lists all key assumptions for user confirmation to ensure the model aligns with real-world constraints.
- Model Construction: Identifies specific modules (like MTZ for sub-tour elimination) and estimates computational complexity.
- Self-Inspection: Performs a multi-point check for physical feasibility, completeness, and solver compatibility.
- LaTeX Output: Generates clean, copy-pasteable LaTeX code for the mathematical sets, parameters, and functions.
Operations Research Modeling Assistant Setup
To use the research capabilities of this skill, ensure your environment is configured for the search scripts:
# Install dependencies for paper search
pip install requests
# Run a search to test the setup
python3 scripts/paper_search.py "vehicle routing problem" --source openalex --max-results 5
Operations Research Modeling Assistant Data Schema & Taxonomy
The skill organizes optimization data using a structured taxonomy of constraints and model components:
| Component | Taxonomy Code | Description |
|---|---|---|
| Capacity | C1 | Vehicle load or warehouse limits |
| Time Window | C2 | Hard or soft service intervals |
| Flow Balance | C4 | Continuity of routes (In-degree = Out-degree) |
| Logical | C8 | If-then activations using Big-M methods |
| Chance | C10 | Stochastic demand and probability constraints |
Operations Research Modeling Assistant Advanced Features
- Automated Literature Integration: Multi-source search (OpenAlex + Semantic Scholar) to find citation-backed solutions.
- Complexity Estimation: Automatically predicts if a problem is NP-Hard and suggests appropriate solvers (CPLEX, Gurobi, or Metaheuristics).
- Fast Confirmation Mode: High-speed workflow for experienced users that bypasses redundant confirmation steps.
- Modular Constraint Library: Plug-and-play math blocks for rapid prototyping of common OR problems.
SKILL.md
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