Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx agentmods add skills/woodfishhhh/ez_math_model/systematic-debuggingnpx skills add woodfishhhh/EZ_math_model --skill systematic-debugginggit clone --depth 1 https://github.com/woodfishhhh/EZ_math_modelWhat it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5 | $0.00031 | $0.00193 |
| Opus 5 | $0.00015 | $0.00097 |
| Sonnet 5 | $0.00006 | $0.00039 |
| Haiku 4.5 | $0.00003 | $0.00019 |
Grade A, and why
systematic-debugging scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 2d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
What it actually says
systematic-debugging — 根因诊断
何时使用
coder单脚本 L1 重试 2 次仍失败。- 同一异常类型连续出现。
- packaging 转换失败且普通修复无效。
- 需要验证假设而不是继续“再试一次”。
调用方式
优先加载宿主 systematic-debugging skill。输入：
- 失败代码。
- 完整 traceback。
modeling_plan.md对应章节。- 附件数据预览。
- 已尝试修复记录。
要求输出：
- 5-10 条可能根因。
- 每条根因对应的验证步骤。
- 按概率排序的修复建议。
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 2d ago First seen · 30 lines · 31 tokens per session scan A 7f4fd48dadab
systematic-debugging is a skill published in the GitHub repository woodfishhhh/EZ_math_model (39 stars, last pushed 1mo ago), licensed MIT. It adds 31 tokens to every session and 193 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
math-modeling-solver
数学建模竞赛解题全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)全部题型(A-F)，提供12种问题本质分析、95+场景模型决策矩阵、5本算法Cookbook、11本完整例题Playbook、22个Python+7个MATLAB可运行代码模板。与math-modeling-paper形成"解题→写作"配对。当用户提及建模思路、选什么模型、怎么建模、赛题求解、粘贴赛题文本、美赛/国赛题目分析、CVaR/NSGA-II/Monte Carlo/时间序列/ANOVA/灰色关联、网络流/图论/生态建模、模型命名/Memo/Letter/Our Work流程图时，使用此skill。.
mathmodel-skill
CUMCM 国赛、MCM/ICM 美赛与电工杯数学建模竞赛的端到端协作工作流。Use when a user explicitly works on one of these modeling contests or asks to run/review a modeling-competition paper from problem selection through modeling, solving, robustness, writing, compliance, and final submission review. Provides 10 stages, persistent decision state…
math-modeling-paper
数学建模竞赛论文写作全流程指导。覆盖国赛(CUMCM)和美赛(MCM/ICM)，从论文结构规划、各章节撰写、模型检验、参考文献规范到最终格式检查。与math-modeling-solver形成"解题→写作"配对——可接收solver输出的论文草稿片段直接展开写作。当用户提及数学建模论文写作、建模比赛、国赛/美赛/电工杯/亚太杯/深圳杯/华为杯论文、CUMCM、MCM/ICM、数模论文结构、摘要写作、模型检验、灵敏度分析、latex建模模板、word建模排版、Memo/Letter写作、模型命名、Our Work流程图，或需要写/修改/优化/检查建模论文的任何部分时，都必须使用此 skill。.
interpret-modeling-problems
根据原始赛题和附件，生成可回查证据的国赛及类似数学建模赛题解读，建立小问输入输出、约束与歧义、附件审计、模型蓝图、验证方案和论文交付接口。适用于选题比较、正式建模前的完整解读和已有解读复核；不用于在缺少原题时臆测题意，也不把候选模型伪装成已经验证的数值求解。.
math-modeling-finalizer
数学建模项目收口与终审 Skill。用于正式结果已基本冻结后的参赛者尺度代码简化与行为回归、最终完整复审、AI 工具使用详情 DOCX、Word/PDF 排版检查、支撑材料 ZIP 与提交前卫生检查。典型请求包括“结果已经定了，把代码改得更像学生项目”“最后完整审一遍”“检查最终 Word/PDF”“生成 AI 使用详情和支撑材料”。若需要重新建模、修改 evaluator 或正式结果，路由回 math-modeling-solver；正文、摘要、引用语义或公式写作问题路由回 math-modeling-paper；提交后的项目复盘/教学交给 math-modeling-growth。.
math-modeling-growth
把真实项目的阶段结果、决策、证据、失败记录和当前进度，转化为通俗、循序渐进、可执行的学习解释与赛后成长报告。用户要求复盘项目、理解自己学到了什么、解释某次决策、生成赛后报告、从项目进度推进学习，或需要按接受度逐步教学时使用；优先读取 Solver、Paper、Finalizer 等上游阶段产物和当前冻结结果。.