Generate runnable competition code from modeling reports with constraint validation.
npx skills add https://github.com/lix965996-art/MMM --skill comp-code
Please help me install this Agent Skill. Skill: comp-code Source: https://github.com/lix965996-art/MMM/tree/main/resources/app/skills/comp-code Command: npx skills add https://github.com/lix965996-art/MMM --skill comp-code
This Skill turns a competition modeling report into deterministic, verifiable programming implementations by enforcing algorithm/constraint/parameter consistency and producing structured JSON outputs for paper figures.
Use the comp-code Skill to implement and run competition code based on the attached modeling package containing MODELING_REPORT.md, PROBLEM_ANALYSIS.md, TOPIC_PLAN.md, and user_data/.
High-intent search queries and answers about installing and using this skill.
To generate runnable Python code from a math modeling report, you provide your modeling package including analysis documents and user data. The implementation enforces algorithm, constraint, and parameter consistency to prevent silent simplification.
Validating data ingestion and constraints during math model implementation is handled through automated sanity checks and a mandatory validate_constraints function. This ensures all results stay within declared expected ranges and boundaries.
Ensuring modeling-to-code consistency for competition programming requires extracting and enforcing the report's required algorithms and validation checkpoints. This approach prevents silent simplification and guarantees verifiable implementations.
Yes, producing structured JSON outputs from math modeling code directly supports downstream paper figure generation. The implementation aggregates sub-problem results and formats them into structured JSON for easy visualization.
Yes, generating competition programming code requires a modeling package containing MODELING_REPORT.md, PROBLEM_ANALYSIS.md, TOPIC_PLAN.md, and a user_data directory for complete end-to-end workflow execution.
Error prevention when implementing math modeling algorithms in Python works through mandatory safety checks using reference materials. The system performs rationale checks and reports against a checklist to ensure expected boundaries are met.
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