Data & Analytics

Master modern business analysis with AI-powered analytics, real-time dashboards, and data-driven insights. Build comprehensive KPI frameworks, predictive models, and strategic recommendations.

Biopython is a comprehensive set of freely available Python tools for biological computation. It provides functionality for sequence manipulation, file I/O, database access, structural bioinformatics, phylogenetics, and many other bioinformatics tasks.

Azure Monitor Query SDK for Python. Use for querying Log Analytics workspaces and Azure Monitor metrics.

Azure Monitor Query SDK for Java. Execute Kusto queries against Log Analytics workspaces and query metrics from Azure resources.

Azure Application Insights SDK for .NET. Application performance monitoring and observability resource management.

Azure AI Text Analytics SDK for sentiment analysis, entity recognition, key phrases, language detection, PII, and healthcare NLP. Use for natural language processing on text.

Astropy is the core Python package for astronomy, providing essential functionality for astronomical research and data analysis.

Analytics de produto — PostHog, Mixpanel, eventos, funnels, cohorts, retencao, north star metric, OKRs e dashboards de produto.

Access 20+ years of global financial data: equities, options, forex, crypto, commodities, economic indicators, and 50+ technical indicators.

A hybrid memory system that provides persistent, searchable knowledge management for AI agents (Architecture, Patterns, Decisions).

Structured guide for setting up A/B tests with mandatory gates for hypothesis, metrics, and execution readiness.

Turn any coding agent into a senior Solana on-chain data analyst. Use whenever the user wants to measure, query, or interpret on-chain activity on Solana (EVM-aware too): write or optimize Dune / Flipside SQL, decode a program's logs or Anchor IDL into analytics tables, compute protocol KPIs (TVL, volume, fees, revenue, DAU/WAU/MAU, retention, cohorts, market share), analyze DeFi (DEX, lending, LST/staking, perps), study token holders and wallet cohorts (whale vs retail vs bot), run forensics...

对执行出来的结果、实验数据、模型输出、图表结果进行详细专业深入的分析。当用户实验跑完、需要解读数据、问"这些结果说明什么"时使用。不只描述好坏，而是解释为什么、哪些结果证明方法有效、哪些暴露问题、哪些异常需排查、哪些规律可成为论文亮点、哪些需补充实验。

强大地读文件并学习——Word、PDF、PPTX、Excel、CSV、图片、视频、代码、压缩包等。当用户提供任何文件、问"这个文件讲了什么"、或任务需要理解已有材料时使用（常驻，自动触发）。不只提取文字，而是理解结构、逻辑、图表、数据、实验结果、格式要求、章节关系、视觉风格、隐含要求与可复用内容，并转化为可执行任务。

数据处理、数据质量分析与数据集构建。当用户需要清洗数据、处理缺失/异常值、特征工程、数据增强、划分数据集、评估数据质量，或需自建数据集（采集、标注规范、格式、说明文档、隐私合规、发布）时使用。在提 idea 前优先判断现有数据是否足以支撑研究。

Use when constructing or revising regression tables, descriptive statistics tables, or figures after results are estimated and before submission for an AER, AER:Insights, or AEJ manuscript. Implements AER booktabs house style, regression-table layout, and figure-note conventions.

A Claude skill for economists that ensures rigorous data decisions and directs users only to authoritative sources. Interfaces with Playwright MCP for web data retrieval.

Classical end-to-end empirical analysis workflow in the modern tidyverse + econometrics R ecosystem — dplyr + tidyr + haven + fixest + sandwich + lmtest + clubSandwich + AER + ivreg + did + bacondecomp + HonestDiD + eventstudyr + rdrobust + rddensity + Synth + gsynth + synthdid + MatchIt + WeightIt + cobalt + ebal + grf + DoubleML + mediation + marginaleffects + modelsummary + kableExtra + gt + ggplot2 + ggpubr + cowplot + binsreg. **Defaults to economics empirical-paper style** (AER / QJE / ...

Classical end-to-end empirical analysis workflow in the traditional Stata ecosystem — native Stata + reghdfe + ivreg2 + csdid + did_imputation + eventstudyinteract + sdid + rdrobust + rddensity + synth + synth_runner + psmatch2 + teffects + ebalance + coefplot + esttab + asdoc + binscatter. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece...

Classical end-to-end empirical analysis workflow in the traditional Python econometric stack — pandas + numpy + scipy + statsmodels + linearmodels + pyfixest + rdrobust + econml + causalml + matplotlib/seaborn. **Defaults to economics empirical-paper style** (AER / QJE / AEJ) — every run produces a publication-ready output set with a multi-column regression table (M1→M6 progressive controls/FE) as the centerpiece, plus Table 1 (descriptives), mechanism / heterogeneity / robustness tables, and...

Use when the user asks to run a full empirical / causal analysis in Python — by default in the style of an applied economics paper (AER / QJE / JPE / ReStud / AEJ) with DID / RD / IV / SCM / DML / matching, written-out estimating equation + identifying assumption, Table 1 / Table 2 / event-study figure / robustness gauntlet — OR in epidemiology / public health style (target-trial emulation, IPTW + g-formula + TMLE triplet, Mendelian randomization, KM/AFT survival, E-value sensitivity, STROBE/...

Audit the biblemate skill in [biblemate](.agents/skills/biblemate) , the original prompt for creating the skill was recorded in [biblemate.md](developement/biblemate.md) . Make suggestions to further enhance the bible study workflow and the skill file.

Create manual_setup.zip containing .grok/, preferences/, and AGENTS.md for manual Grok Build repository setup. Use when the user runs /zip or requests this BibleMate workflow.

Retrieve bible cross-references from the local SQLite databases based on versions and references. Use when the user runs /xrefs or requests this BibleMate workflow.