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分類數據分析

數據分析

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數據分析

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數據分析除錯銷售同市場推廣 LLM 同 AI 系統管理生產力同整合代碼質素 Git 工作流程架構模式文件項目管理測試金融同投資安全知識庫技術文檔 Backend Frontend 教育 CI/CD 自動化工具機器學習內容創作數據工程遊戲開發學術腳本編寫 CMS 同平台流動裝置雲端 IDE 插件套件同發佈框架內部結構媒體設計 Full Stack 地產同法律生物信息學 CLI 工具 SQL 數據庫計算化學容器實驗室工具數據庫工具健康同健身智能合約監控域名同 DNS 工具 Web3 工具養生同健康支付文學同寫作電商開發哲學同倫理天文同物理電子商務占卜同玄學手工藝術科學計算商業應用 DeFi NoSQL 數據庫烹飪 $Undefined

睇全部分類

數據分析除錯銷售同市場推廣 LLM 同 AI 系統管理生產力同整合代碼質素 Git 工作流程架構模式文件項目管理測試金融同投資安全知識庫技術文檔 Backend Frontend 教育 CI/CD 自動化工具機器學習內容創作數據工程遊戲開發學術腳本編寫 CMS 同平台流動裝置雲端 IDE 插件套件同發佈框架內部結構媒體設計 Full Stack 地產同法律生物信息學 CLI 工具 SQL 數據庫計算化學容器實驗室工具數據庫工具健康同健身智能合約監控域名同 DNS 工具 Web3 工具養生同健康支付文學同寫作電商開發哲學同倫理天文同物理電子商務占卜同玄學手工藝術科學計算商業應用 DeFi NoSQL 數據庫烹飪 $Undefined

數據分析

Havsan Data Science

Python/R veri analizi projeleri için rehber. Jupyter Notebook, pandas, numpy kullanımı ve Dockerize çalışma standartlarını içerir.

atifertugrul-kan0

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Dhis2 Organisation Units

Extract organisation units and groups from DHIS2. Use for org unit hierarchy, metadata, levels, or groups. Routed via dhis2 skill for general DHIS2 requests.

Dhis2 Data Values

Extract raw data values from DHIS2 using the dataValueSets API. Use for actual submitted data values, not aggregated analytics. ALWAYS use with dhis2-query-optimization skill. Routed via dhis2 skill for general DHIS2 requests.

Dhis2 Analytics

Query aggregated analytics data from DHIS2. Use for calculated/aggregated values, indicator values, or cross-dimensional analysis. ALWAYS use with dhis2-query-optimization skill. Routed via dhis2 skill for general DHIS2 requests.

Dashboard Maps

Guide for creating map visualizations in dashboards. Covers geographic data detection, shape-based maps (polygons/GeoJSON), point-based maps (lat/lon coordinates), and world map backgrounds. Referenced by dashboard-builder skill. Do not use directly - use dashboard-builder instead.

D3 Viz

Creating interactive data visualisations using d3.js. This skill should be used when creating custom charts, graphs, network diagrams, geographic visualisations, or any complex SVG-based data visualisation that requires fine-grained control over visual elements, transitions, or interactions. Use this for bespoke visualisations beyond standard charting libraries, whether in React, Vue, Svelte, vanilla JavaScript, or any other environment.

Map Audit

For leaders evaluating dashboards, metrics, or AI-generated reports to determine if they measure reality or generate confident-looking noise (Potemkin maps). Helps identify gaming potential, blind spots, and whether you should trust a metric more, less, or differently. Use when implementing new metrics, questioning existing dashboards, evaluating vendor claims, or when something feels off about your data. Keywords: dashboard audit, metrics, KPIs, gaming, Goodhart's law, AI reports, data quality, measurement validity, blind spots, Potemkin, are my metrics real, can I trust this data

Anomaly Detection

Detects statistical anomalies in liquidity metrics using Z-score, IQR, and time-series analysis techniques

Yigo Dataobject Generator

生成与解读 YIGO DataObject XML 配置，包含 TableCollection、Column、IndexCollection 等数据模型定义

Visualization

Use when the main agent must create or validate figures directly, either to understand data during analysis or to produce real visual assets for reports, writer handoff, and user-facing delivery. Build figures with matplotlib first, then inspect rendered images for readability, layout defects, missing content, Chinese font failures, and whether the figure actually serves the intended analytical or communication goal.

benbaobaoshigemi0

Visualization

Use when the main agent must create or validate figures directly, either to understand data during analysis or to produce real visual assets for reports, writer handoff, and user-facing delivery. Build figures with matplotlib first, then inspect rendered images for readability, layout defects, missing content, Chinese font failures, and whether the figure actually serves the intended analytical or communication goal.

benbaobaoshigemi0

Test Learning Loop

通过 causal-learner MCP 工具执行 Scenario D：水垢学习闭环。提交误预测观测 → 记录修复 → 触发归纳 → 验证因果图更新 → 计算学习指标。无需编译。

Scientific Hypothesis Generation

Structured hypothesis formulation from observations. Use when you have experimental observations or data and need to formulate testable hypotheses with predictions, propose mechanisms, and design experiments to test them. Follows scientific method framework. For open-ended ideation use scientific-brainstorming; for automated LLM-driven hypothesis testing on datasets use hypogenic.

Example Chartjs

Chart.js integration patterns for Helios. Use when creating animated data visualizations with Chart.js.

Analysis Statistical Analysis

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, test selection guidance. For implementing specific models programmatically use statsmodels.

Lib Statsmodels

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis.

Ml Hypogenic

Automated LLM-driven hypothesis generation and testing on tabular datasets. Use when you want to systematically explore hypotheses about patterns in empirical data (e.g., deception detection, content analysis). Combines literature insights with data-driven hypothesis testing. For manual hypothesis formulation use hypothesis-generation; for creative ideation use scientific-brainstorming.

Data Dictionary

Generate or update a comprehensive markdown data dictionary from any database — auto-detects schema source and DB engine, includes live statistics, sample rows, null rates, Mermaid ER diagram, and quick-reference table. Supports --update to refresh stats while preserving hand-written descriptions.

Data Commons Client

Work with Data Commons, a platform providing programmatic access to public statistical data from global sources. Use this skill when working with demographic data, economic indicators, health statistics, environmental data, or any public datasets available through Data Commons. Applicable for querying population statistics, GDP figures, unemployment rates, disease prevalence, geographic entity resolution, and exploring relationships between statistical entities.

Viz Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly.

MATLAB/Octave Scientific Computing

MATLAB and GNU Octave numerical computing for matrix operations, data analysis, visualization, and scientific computing. Use when writing MATLAB/Octave scripts for linear algebra, signal processing, image processing, differential equations, optimization, statistics, or creating scientific visualizations. Also use when the user needs help with MATLAB syntax, functions, or wants to convert between MATLAB and Python code. Scripts can be executed with MATLAB or the open-source GNU Octave interpreter.

Lib Matplotlib

Low-level plotting library for full customization. Use when you need fine-grained control over every plot element, creating novel plot types, or integrating with specific scientific workflows. Export to PNG/PDF/SVG for publication. For quick statistical plots use seaborn; for interactive plots use plotly; for publication-ready multi-panel figures with journal styling, use scientific-visualization.

Chart Visualization

This skill should be used when the user wants to visualize data. It intelligently selects the most suitable chart type from 26 available options, extracts parameters based on detailed specifications, and generates a chart image using a Python script.

Ff Insights

Aggregate meeting analytics and statistics. Use when analyzing meeting patterns, speaker time, or trends.

Data Report Generator

**데이터 분석 리포트 생성기**: 엑셀/CSV 데이터를 분석하여 한국식 보고서 양식의 시각화 리포트를 자동 생성합니다. 경영진 대상 요약 보고서, KPI 대시보드, 인사이트 분석을 포함합니다. - MANDATORY TRIGGERS: 데이터 분석, 데이터 리포트, KPI, 대시보드, 데이터 시각화, 분석 보고서, 매출 분석, 성과 분석, 데이터 보고서, 통계 분석, 트렌드 분석

Grafana Mcp

Manages Grafana dashboards, data sources, users, alerts, and configuration through the Grafana MCP Server for visualization and monitoring operations. Use when user mentions Grafana, dashboards, metrics visualization, alerts, monitoring panels, or Grafana API interactions.

Statistical Analysis

Guided statistical analysis with test selection and reporting. Use when you need help choosing appropriate tests for your data, assumption checking, power analysis, and APA-formatted results. Best for academic research reporting, analysis planning, and test selection guidance. For implementing 多因素分析, 多变量回归, 亚组回归, 临床队列回归, coefficient tables, or other specific regression models programmatically use statsmodels. Also use for 医学统计, 科研复现, 统计分析方案, Table 1, 描述统计, 假设检验, effect size reporting, and manuscript-ready statistical summaries.

Scientific Visualization

Meta-skill for publication-ready figures. Use when creating journal submission figures requiring multi-panel layouts, significance annotations, error bars, colorblind-safe palettes, and specific journal formatting (Nature, Science, Cell). Orchestrates matplotlib/seaborn/plotly with publication styles. For quick exploration use seaborn or plotly directly. Also use for 科研作图, 论文图, 多面板图, publication figure assembly, RNA-seq figures, 火山图, 热图, enrichment figures, Kaplan-Meier figure polishing, and publication-ready PDF or SVG export, especially when the user wants paper-ready rather than merely exploratory plots.

Statsmodels: Statistical Modeling and Econometrics

Statistical models library for Python. Use when you need specific model classes (OLS, GLM, mixed models, ARIMA) with detailed diagnostics, residuals, and inference. Best for econometrics, time series, rigorous inference with coefficient tables. For guided statistical test selection with APA reporting use statistical-analysis. Also use for 计量经济学, 因果推断, 双重差分, 事件研究, 工具变量, 面板回归, 稳健标准误, 多因素分析, 多变量回归, 临床队列回归, retrospective cohort regression, subgroup regression, multivariable adjustment, policy evaluation, and regression tables for paper reproduction.

Dhis2 Visualizations

Extract DHIS2 visualizations (favorites) metadata and data. Use for chart/table configurations, visualization data, or dashboard items. Routed via dhis2 skill for general DHIS2 requests.