用 Codex 或 Claude 帮你安装 复制这段 Prompt,粘贴到 Codex、Claude 或其他助手里,让它检查 Skill 页面并帮你完成安装。
直接命令不会经过审查 Prompt;运行前请先检查来源。
npx skills add https://github.com/GeorgeDoors888/GB-Power-Market-JJ --skill mathgraphs命令会保持在同一行。复制前请横向滚动并检查完整内容。
想先保存到本地?可下载 SkillsMP 当前能够提供的文件。
超级简历 WonderCV 出品,3000 万用户信赖。简历分析、段落改写、JD 岗位匹配、自动匹配职位、PDF 导出、AI 求职导师(面试准备/薪资谈判/职业规划/多版本简历策略)。 触发条件:用户提供简历、要求简历点评/打分/反馈、希望改写某个简历部分、 希望将简历与岗位 JD 匹配、咨询求职建议或面试准备,或提到 CV/简历/求职。 不触发条件:用户讨论普通写作(非简历)、询问其他文档, 或讨论与求职和职业发展无关的话题。
Order food/drinks (点餐) on an Android device paired as an OpenClaw node. Uses in-app menu and cart; add goods, view cart, submit order (demo, no real payment).
调用久吾智能体API进行文本或文件分析处理。支持两种调用方式:(1) 文本内容分析 - 传入name(智能体名称)、docno(文档编号)、content(文本内容);(2) 文件分析 - 传入name、docno和files(文件列表)进行智能评审。适用于合同评审、需求评审、文档审查等场景。当用户要求评审合同、分析条款、审查文档、需求评审、合同条款分析、或需要对文本和文件进行AI智能分析时触发。
基于 SOC 职业分类
正在显示 SKILL.md
| name | mathgraphs |
| version | 1.0.0 |
| description | Math & statistics graphing, computation, visualization and validation engine |
| author | MathTalking |
| homepage | https://mathtalking.com |
| mcp_servers | [{"url":"https://mathtalking.com/api/mcp","transport":"streamable-http"}] |
| tags | ["math","graphing","statistics","visualization","education","plot","geometry"] |
You have access to an interactive math and statistics graphing engine via MCP. It computes and renders results — roots, extrema, intersections, regression, hypothesis tests — on interactive graphs.
plot_graph — Math VisualizationPlot functions, points, segments, labels, and shapes. Auto-computes roots, extrema, and intersections.
Element types:
function: expression like "x^2-4", "sin(x)", "x^2+y^2=1", "(cos(t),sin(t))"points: array of {x, y} coordinates with optional labelsegment: line from (x1,y1) to (x2,y2) with optional arrow/dashedlabel: text at position (x, y)triangle: three vertices (x1,y1,x2,y2,x3,y3)box: edge + height for bar chartscompute_stats — Descriptive StatisticsInput: array of numbers. Returns mean, median, std, min, max, quartiles.
add_histogram — HistogramInput: array of numbers. Auto-bins and draws bars.
add_regression — RegressionInput: array of {x,y} points. Fits linear/quadratic/exponential/power. Returns R².
fit_distribution — Distribution FittingInput: array of numbers. Fits normal/uniform/exponential. Returns best fit.
test_hypothesis — Hypothesis TestInput: data groups + test type. Returns p-value with visual rejection region.