mathodology-agent-pipeline
About mathodology-agent-pipeline
A prompt skill for guiding mathematical modeling tasks, from problem formulation and testing assumptions to communicating results. It emphasizes adapting the process to the task and supporting conclusions with evidence.
Key features
- 01Problem and objective framing
- 02Baseline model formulation
- 03Assumption and uncertainty checks
- 04Evidence-based communication
- 05Focused specialist collaboration
Use cases
- 01Structure a modeling problem
- 02Compare a baseline with justified complexity
- 03Test assumptions and adverse scenarios
- 04Coordinate bounded specialist contributions
Install
Copy the command and run it in a terminal.
npx skills add https:/After installing, type/mathodology-agent-pipelinein Claude Code to confirm it loaded.
Bundled filesagents/openai.yaml
Details
From the project READMEMathodology Modeling Prompts
Use the following questions in whatever order the task needs. They are prompts for reasoning, not mandatory stages or files.
Understand the problem
What decision is the reader trying to make? What is given, unknown or required? Which mechanisms must the solution represent? Check the actual contest rules when applicable, including deadline, page limits and AI-use requirements.
Formulate a model
Start with a useful baseline. Define variables, units, assumptions, constraints and the objective. Compare plausible alternatives when there is a real choice; do not invent extra models to meet a quota. Explain why the added complexity changes the answer. Check identifiability, data requirements and limiting cases.
Challenge the result
Which observation could disprove the model? Can a simpler baseline perform as well? Test influential assumptions and plausible adverse scenarios. Separate parameter uncertainty, observation noise and structural uncertainty. Match the paper's claims to the implemented mathematics and the data actually used.
Communicate the answer
Answer the problem's questions with interpretable quantities and limitations. Choose figures from figure presets, including the once-per-task image2 question. Build the explanation around the results, not the history of experiments. Review with review questions.
Focused collaboration
When delegation is useful and available, give a specialist a bounded question, relevant data, current assumptions and a concrete output. Agree file ownership for concurrent editing. Ask for ordinary prose: finding, reasoning, artifact paths and unresolved uncertainty. The lead integrates the answer and resolves conflicting evidence; it does not collect points or gate every intermediate step.
For a fresh task, a compact prompt is:
Solve the supplied modeling problem. State assumptions, build and test a useful baseline, add justified complexity, and connect each recommendation to evidence. Adapt the workflow to the available time. Select purposeful figures using mathodology-figure-presets and ask once about image2 availability. Keep calculations reproducible and explain what could change the conclusion.