survival-analysis-km
Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.
Kaplan-Meier survival analysis tool for clinical and biological research. Generates publication-ready survival curves with statistical tests.
Automated generation of baseline characteristics tables (Table 1) for clinical research papers.
Analyze data with `volcano-plot-labeler` using a reproducible workflow, explicit validation, and structured outputs for review-ready interpretation.
Summarize core safety information from Investigator's Brochures for clinical.
Specialized utility for advanced manipulation, analysis, and creation of spreadsheet files, including (but not limited to) XLSX, XLSM, CSV formats. Core functionalities include formula deployment, complex formatting (including automatic currency formatting for financial tasks), data visualization, and mandatory post-processing recalculation.
Apply KIP history-series reconciliation and mapping constraints across dashboard and chart/history flows.
Compare multiple strategies or directions (long vs short vs both) on the same symbol. Generates side-by-side stats table.
将多维经营分析结果整合为管理层可读的结构化摘要,支持经营分析会汇报材料的快速生成
Ashare 最轻量 A 股行情获取工具(3.2k Stars),基于新浪+腾讯双核心数据源,零依赖(仅需 requests+pandas),无需注册,支持日/周/月线及 1m/5m/15m/30m/60m 分钟级K线。当用户需要快速获取 A 股/指数行情而其他数据源不可用时,Ashare 是最可靠的回退方案——它使用新浪为主、腾讯为备的双核心架构,自动切换,极少出错。
adata 免费A股量化数据源,专注A股市场,提供股票行情、资金流向、核心财务指标(43个字段)、北向资金、热门股票排行、分红数据、概念板块、指数成分股、基金及可转债数据。当用户需要A股行情、资金流、北向资金、热门排行、财务指标、分红信息时可使用此 skill。与 akshare-finance 和 efinance-data 互补——adata 的独特优势在于:(1)北向资金实时数据 (2)热门股票排行 (3)核心财务指标一次返回43个字段(EPS/ROE/毛利率/资产负债率/周转率等)(4)概念板块成分股。
A股图神经网络/关联图谱量化。当用户说"图神经网络"、"GNN"、"graph network"、"知识图谱"、"关联图谱"、"股票关系网络"、"供应链图谱"时触发。基于 cn-stock-data 获取数据,构建股票关系图谱与GNN模型。支持 formal/brief 两种输出风格。
A股相关性分析/联动关系/相关系数计算。当用户说"相关性"、"联动"、"correlation"、"XX和YY相关吗"、"哪些股票走势相似"、"分散化"、"对冲"、"beta"、"同涨同跌"、"相关性分析"、"相关系数"、"相关性矩阵"时触发。MUST USE when user asks about correlation analysis, correlation coefficient between stocks/sectors, or diversification analysis based on correlation. 计算个股/指数/行业之间的收益率相关系数、Beta值,分析联动关系和分散化效果,辅助组合构建和对冲决策。支持研报风格(formal)和快速查看风格(brief)。
Load simulation results and query values at specific points, domain-wide statistics, flood extent, or cross-section profiles. Use this when the user wants to read, query, probe, or analyze simulation results.
Test at extremes (1000x bigger/smaller, instant/year-long) to expose fundamental truths hidden at normal scales. Unsure if approach will scale. Edge cases unclear. Want to validate architecture. "Will this work at production scale?" Need to find fundamental limits.
Use this skill when performing exploratory data analysis, statistical testing, data visualization, or building predictive models. Triggers on EDA, pandas, matplotlib, seaborn, hypothesis testing, A/B test analysis, correlation, regression, feature engineering, and any task requiring data analysis or statistical inference.
Single-cell pseudotime and lineage inference after clustering, with DPT, Palantir, VIA, CellRank, or Slingshot plus post-hoc trajectory gene ranking.
Spatial statistics for spatial transcriptomics using neighborhood enrichment, Ripley's statistics, co-occurrence, Moran/Geary autocorrelation, local Moran, Getis-Ord Gi*, bivariate Moran, and spatial graph centrality summaries.
Alignment statistics from SAM/BAM files: mapping rate, MAPQ distribution, insert size, duplicate rate, proper pair rate. Mirrors samtools-flagstat.
Create interactive data visualizations using Vega-Lite declarative JSON grammar. Supports 20+ chart types (bar, line, scatter, histogram, boxplot, grouped/stacked variations, etc.) via templates and programmatic builders. Use when users upload data for charting, request specific chart types, or mention visualizations. Produces portable JSON specs with inline data islands that work in Claude artifacts and can be adapted for production.
Content experimentation and A/B testing guidance covering experiment design, hypotheses, metrics, sample size, statistical foundations, CMS-managed variants, and common analysis pitfalls. Use this skill when planning experiments, setting up variants, choosing success metrics, interpreting statistical results, or building experimentation workflows in a CMS or frontend stack.