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 skills add VeryMath/AI4Math-Optimization --skill mixed-integer-programminggit clone --depth 1 https://github.com/VeryMath/AI4Math-OptimizationWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/verymath/ai4math-optimization/mixed-integer-programming)<a href="https://agentmods.dev/skills/verymath/ai4math-optimization/mixed-integer-programming"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/mixed-integer-programming/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/verymath/ai4math-optimization/mixed-integer-programming"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/mixed-integer-programming.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What 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 5.1 | $0.00030 | $0.01092 |
| Opus 5.5 | $0.00012 | $0.00437 |
| Sonnet 5.5 | $0.00006 | $0.00218 |
| Haiku 4.5 | $0.00003 | $0.00109 |
Grade A, and why
mixed-integer-programming 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 1mo 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
混合整数规划（Mixed-Integer Programming, MIP）求解
适用场景
- 混合整数线性规划：线性目标、线性约束，部分或全部变量为整数。
- 二进制决策问题：选址、指派、覆盖、启停、固定费用建模。
- 组合优化建模：背包、TSP、排产、车辆路径、网络设计等。
- 可线性化问题：逻辑约束、Big-M、indicator、SOS1/SOS2 等。
输入：可以是自然语言/应用题，也可以是矩阵、JSON、已有模型代码或求解器报错。
Quick Start（先做这个）
按下面清单执行并在回答中保留结构。环境准备必须先于求解。
- 求解器选择与环境准备：
- 本建模任务先确认问题类型为 MIP/MILP，并明确用户或当前任务已选定的求解器和接口。
- 目标需要安装或配置时，将求解器名称、接口、Python 环境及实际报错交给
../or-solver/SKILL.md；该 skill 只配置指定目标。 - 配置后由本任务继续求解；配置失败时，由本任务决定下一步，必要时走 GitHub 搜索路径。
- 路径判断：用户给的是自然语言、矩阵/JSON、代码，还是要求从 GitHub 找代码。
- 符号化：列变量、变量类型、目标、约束和单位。
- 数值化：给出矩阵、JSON，或直接用求解器 API 建模。
- 求解并报告：状态、目标值、变量值、MIP gap、求解时间。
- 验证：检查约束可行性和整数变量取值。
执行流程（三条路径）
flowchart LR
A[Matrix_or_JSON]
B[Natural_language]
C[GitHub_search]
A --> A1[Build_or_solve_mip]
B --> B1[Restate]
B1 --> B2[Clarify_or_assume]
B2 --> B3[Symbolic_model]
B3 --> B4[Numeric_or_solver_API]
B4 --> B5[Solve_and_report]
A1 --> B5
C --> C1[Search_GitHub_for_MIP_code]
C1 --> C2[Fetch_and_adapt_code]
C2 --> C3[Run_and_report]
路径 A：已有矩阵、JSON 或模型
- 核对维度、变量类型、上下界、约束方向和目标方向。
- 优先复用已有建模结构，避免把稀疏模型强行转成稠密矩阵。
- 使用可用求解器求解，并保存求解器状态和日志要点。
路径 B：自然语言 / 应用题
用户未给数字矩阵时，不要先索要 JSON。按下面顺序推进：
| 步骤 | 内容 |
|---|---|
| 1. 重述 | 用一两句话复述题意，便于用户确认。 |
| 2. 变量 | 列出变量名称、含义、单位、类型（binary/integer/continuous）。 |
| 3. 模型 | 写出目标函数和约束，并标明 <= / >= / =。 |
| 4. 求解 | 建模求解，报告目标值、变量值、gap 和状态。 |
| 5. 解释 | 用 1-2 句解释业务含义，必要时说明假设。 |
路径 C：GitHub 搜索开源代码
当本地无可用求解器，或用户明确要求从 GitHub 找代码时，搜索：
site:github.com mixed integer programming solver python <problem feature>
优先选择近期维护、有 README、纯 Python 或主流求解器接口的项目。抓取 README 和关键文件后，适配用户数据并注明来源。
输出模板（推荐）
### 环境与依赖
- Python 版本：...
- 可用求解器：...
- 选用求解器：...
### 问题重述
...
### 符号化模型
- 决策变量：...
- 目标函数：...
- 约束：...
### 求解结果
- status: ...
- objective: ...
- variables: ...
- mip_gap: ...
### 验证与解释
...
What ships with it
4 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
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.
- 1mo ago Changed 59f06f7ebd43
- 1mo ago First seen · 108 lines · 30 tokens per session scan A ada745e183c2
mixed-integer-programming is a skill published in the GitHub repository VeryMath/AI4Math-Optimization (11 stars, last pushed 1mo ago), licensed MIT. It adds 30 tokens to every session and 1,092 once invoked, about $0.0001 per session on Opus 5.5. 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 2026-08-31.
Other skills, from other repositories
math-beamer
Use when creating, revising, or auditing source-grounded mathematical Beamer slide decks from papers, notes, proofs, lectures, experiments, or existing LaTeX slides across pure mathematics, applied mathematics, computational mathematics, statistics, optimization, geometry, topology, algebra, number theory…
paper-writing
Use when drafting, revising, or auditing source-grounded mathematical paper text, including abstracts, introductions, related work, theorem exposition, experiment narratives, revision plans, and response letters.
latex-build-and-layout-audit
Use when checking LaTeX paper projects for compilation, latexmk logs, undefined references, citation issues, duplicate labels, macro/package hygiene, layout warnings, floats, arXiv, or venue compatibility.
claim-evidence-ledger
Use when auditing mathematical paper drafts for supported claims, missing citations, overclaims, proof status, experiment support, or source-to-text traceability.
paper-skeleton-and-logical-architecture
Use when turning mathematical notes, theorem statements, proof sketches, experiments, or reading outputs into a paper skeleton, section plan, result dependency map, or contribution architecture before prose drafting.
proof-obligation-and-assumption-audit
Use when checking mathematical paper results for assumptions, quantifiers, domains, dependency fit, edge cases, external theorem use, proof coverage, or theorem-to-claim consistency.