No red flags found in any of the 11 categories — no credential harvesting, no data exfiltration, no curl-pipe-shell installer. Scanned against the SlowMist agent-security taxonomy, refreshed every 8 hours. Full audit →
by handsomeZR-netizen · Claude Skill · ★ 190
Last updated: · Indexed by AgentSkillsHub · Auto-synced every 8h
🔒 Is mathmodel-skill safe to install? View the security audit →
mathmodel-skill (v6.0.0) 面向 CUMCM (国赛) / MCM·ICM (美赛) / 电工杯 三类数学建模竞赛的 10 阶段工程化流程。全程问答式——用户只需回答编号问题, 不必手敲 bash / python / json。同时支持 Codex 与 Claude Code, 状态文件跨 harness 互通。带 4 层反馈、跨阶段一致性回检、终局多视角评审、题型差异化加权、实测分位锚定打分。 [ 和 美赛 MCM/ICM(A-F) 全部题型。
Claude Code skill for free Google AI Mode search with citations. Zero-config setup, persistent browser profile
Always keep your codebases ready for Agents. Improve any coding workflow by atleast 2x by maintaing a live, pl
Best Practices for Claude Code Configuration
Contest-native, evidence-focused math modeling workflow for Codex and Claude Code
Contest-native, evidence-focused CUMCM workflow for Codex and Claude Code
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mathmodel-skill is 三竞赛 (CUMCM/MCM/电工杯) 数学建模 skill — harness-agnostic, 同时支持 Claude Code 与 Codex CLI, 全程问答式 (Friendly Mode), 10 阶段 + 4 反馈层 + per-Qi 加权聚合 + 题型 dim 加权 + empirical 实测分位锚定. It is categorized as a Claude Skill with 190 GitHub stars.
mathmodel-skill is primarily written in Python. It covers topics such as agents-md, claude-code, claude-skill.
You can find installation instructions and usage details in the mathmodel-skill GitHub repository at github.com/handsomeZR-netizen/mathmodel-skill. The project has 190 stars and 5 forks, indicating an active community.
mathmodel-skill is released under the MIT license, making it free to use and modify according to the license terms.
The top alternatives to mathmodel-skill on Agent Skills Hub include math-modeling-skills, google-ai-mode-skill, unoplat-code-confluence. Each offers a different approach to the same problem space — compare them side-by-side by stars, quality score, and community activity.
Grades come from a rule-based scan built on the SlowMist agent-security taxonomy, covering 11 red-flag categories including credential harvesting, data exfiltration, and curl | sh installers. It is a first-layer scan, not a manual audit — we say so rather than overstate it.
The scale of the problem is documented independently: Liu et al. (2026), in a study of 31,132 agent skills, report that 26.1% contain security vulnerabilities. Our own full-catalog census is published as a citable open dataset.
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