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 second-order-cone-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/second-order-cone-programming)<a href="https://agentmods.dev/skills/verymath/ai4math-optimization/second-order-cone-programming"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/second-order-cone-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/second-order-cone-programming"><img src="https://agentmods.dev/badge/skills/verymath/ai4math-optimization/second-order-cone-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 | $0.00114 | $0.03009 |
| Opus 5.5 | $0.00046 | $0.01204 |
| Sonnet 5.5 | $0.00023 | $0.00602 |
| Haiku 4.5 | $0.00011 | $0.00301 |
Grade A, and why
second-order-cone-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.
How it starts
The opening of the file, as written. The whole thing — 251 lines — stays where its author put it; the contents beside it link to each section on GitHub.
二阶锥规划（Second-Order Cone Program, SOCP）求解
适用场景
- 标准 SOCP：线性目标 + 二阶锥约束
||A_i x + b_i||_2 <= d_i^T x + e_i - 可转化为 SOCP 的问题：二次约束（通过 Cholesky 分解）、分式目标（通过 Charnes-Cooper 变换）、概率约束（正态分布假设下）、Group Lasso 等
- 投资组合优化：最小化风险、最大化夏普比率、鲁棒组合等
- 鲁棒优化：椭球不确定集下的线性规划鲁棒对等
- 工程优化：天线阵列校准、塑性极限分析、FIR 滤波器设计等
输入：可以是自然语言/应用题，也可以是已给的系数矩阵或 JSON。
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 SOCP，并明确用户或当前任务已选定的求解器后端
- 目标需要安装或配置时，将后端名称、cvxpy 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描述、矩阵/JSON，还是要求从 GitHub 找代码
- 求解器选择：优先使用可用求解器（COPT > Gurobi > MOSEK > CPLEX > CLARABEL > ECOS > SCS > CVXOPT > COSMO > OSQP），无可用求解器时走 GitHub 搜索路径
- 问题类型：是标准的 SOCP（锥约束），还是可转化为 SOCP 的问题
- 输出重述（1-2 句）
- 列变量/目标/约束（符号化）
- 需要时提关键澄清问题，或明确写出假设
- 给出求解结果（目标值 + 变量值）
- 用 1-2 句解释业务含义
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_socp_via_cvxpy]
B --> B1[Restate]
B1 --> B2[Clarify_or_assume]
B2 --> B3[Symbolic_model]
B3 --> B4[Model_with_cvxpy]
B4 --> B5[Solve_and_report]
A1 --> B5
C --> C1[Search_GitHub_for_SOCP_code]
C1 --> C2[Fetch_and_adapt_code]
C2 --> C3[Run_and_report]
路径 A：已有矩阵或 JSON
- 核对维度：目标系数、约束矩阵、锥约束参数一致。
- 直接用
cvxpy建模求解。
路径 B：自然语言 / 应用题
用户未给数字矩阵时，Agent 不要先索要 JSON。按下面交付物顺序推进：
| 步骤 | 内容 |
|---|---|
| 1. 重述 | 用一两句话复述题意，便于用户确认理解是否正确。 |
| 2. 符号化 | 变量表：名称、含义、单位（若有）、是否非负。目标：min 还是 max，线性式。约束：逐条写出，标明是线性约束还是锥约束。 |
| 3. 数值化 | 把符号模型写成 c、锥约束参数等；或用 cvxpy.Variable + cvxpy.SOC 直接建模。 |
| 4. 求解与回答 | 给出最优值、各变量取值；必要时用一句话解释经济/物理含义。 |
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 315e18fc12b0
- 1mo ago First seen · 251 lines · 114 tokens per session scan A c6a14de5605f
second-order-cone-programming is a skill published in the GitHub repository VeryMath/AI4Math-Optimization (11 stars, last pushed 1mo ago), licensed MIT. It adds 114 tokens to every session and 3,009 once invoked, about $0.0005 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.