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Get Started Free →Text-to-SQL, validation, cleaning, and reporting conventions for trustworthy analytics.
Apply statistical hypothesis testing, significance analysis, A/B test evaluation, and distribution comparisons for data science workflows.
Use this skill for any Kusto query or telemetry investigation specifically related to Fluid Framework or its partners. Triggers include: writing or running a Kusto query against the Office Fluid database, investigating Fluid Framework telemetry or error rates, querying Office_Fluid_FluidRuntime_* tables, looking up a Fluid session by Session_Id or docId, investigating a Fluid-related error in Loop or Whiteboard telemetry, monitoring an FF bump or partner ring deployment, checking Fluid render re
Selects the most appropriate chart type for a given dataset and analytical question, then produces a clean, honest, well-labeled visualization spec. Use this skill when a user asks "what chart should I use", "how should I visualize this data", "which graph fits", "make a chart for X", "is a pie chart right here", "how do I show change over time / comparison / distribution / correlation / part-to-whole", or when reviewing an existing chart for clarity and honesty. Covers chart-type decision-makin
Analyzes time-to-event data using Kaplan-Meier curves, log-rank tests, and Cox proportional hazards regression with lifelines. Builds survival models from clinical and omics features. Use when predicting patient survival or modeling time-to-event outcomes.
Betting analysis — odds conversion, de-vigging, edge detection, Kelly criterion, arbitrage detection, parlay analysis, and line movement. Pure computation, no API calls. Works with odds from any source: ESPN (American odds), Polymarket (decimal probabilities), Kalshi (integer probabilities). Use when: user asks about bet sizing, expected value, edge analysis, Kelly criterion, arbitrage, parlays, line movement, odds conversion, or comparing odds across sources. Also use when you have odds from E
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/TRI
Econometrics skill for time series analysis. Activates when the user asks about: "time series", "stationarity", "unit root test", "ADF test", "KPSS test", "ARIMA", "ARMA", "autocorrelation", "ACF", "PACF", "VAR model", "VECM", "Granger causality", "cointegration", "impulse response function", "forecast", "seasonal decomposition", "ARCH", "GARCH", "时间序列", "平稳性检验", "单位根", "自回归", "格兰杰因果", "协整", "脉冲响应", "预测", "向量自回归"
Python population genetics with scikit-allel. Read VCF files, compute allele frequencies, calculate diversity statistics, perform PCA, and run selection scans using GenotypeArray and HaplotypeArray data structures. Use when analyzing population genetics in Python.
根据文件行数动态切换大文件处理策略（Parquet转换），通过逐行扫描或列匹配提取关键指标并计算占比、均值等统计量，最终输出结构化Excel报告及可视化图表。
Run regression analysis (OLS or logistic) on uploaded CSV/Excel data, generating coefficients, R², p-values, VIF, and plain-language interpretation. Triggered by requests for regression modeling, fitting data, testing significance, checking multicollinearity, or keywords like OLS, logit, coefficient, p-value, or R-squared.
Comprehensive Stata reference for writing correct .do files, data management, econometrics, causal inference, graphics, Mata programming, and 20 community packages (reghdfe, estout, did, rdrobust, etc.). Covers syntax, options, gotchas, and idiomatic patterns. Use this skill whenever the user asks you to write, debug, or explain Stata code.
Performs pandas DataFrame operations for data analysis, manipulation, transformation, time series analysis, merging, aggregation, and performance optimization.
根据数据规模动态选择处理策略，对多表数据进行合并、统计筛选，并利用 openpyxl 实现关键指标的自动化样式高亮与格式化导出。
Exploratory data analysis notebook exemplar — notebook-to-src extraction workflow, tested EDA library, deterministic dataset, diagnostic figures.
Econometrics skill for descriptive statistics and summary tables. Activates when the user asks about: "descriptive statistics", "summary statistics", "summary table", "Table 1", "balance table", "means and standard deviations", "correlation matrix", "data summary", "sample characteristics", "variable distributions", "描述性统计", "描述统计", "汇总统计", "统计表", "均值标准差", "平衡性检验", "相关矩阵", "样本特征", "变量分布"
对时间序列或分类数据进行多维度趋势分析、百分比清洗、绩效分级建模与预测，并生成高分辨率的可视化综合报告，适用于业务指标监控与预测场景。
读取多 Sheet Excel 文件并统计规模，支持大文件向 Parquet 格式转换、分类数据统计及可视化报告生成。
Analytics your AI agent can actually use. Track, analyze, run A/B experiments, and optimize across all your projects via CLI. Includes a growth playbook so your agent knows HOW to grow, not just what to track.
Pick the right y-axis unit when creating or updating a TrendsQuery insight via `posthog:insight-create` or `posthog:insight-update`. Use when the agent is about to add a `formula` purely to convert units (e.g. dividing seconds by 60 to display minutes), when a `math_property` is a duration, currency, ratio, or large count, or whenever the user mentions \"format the y-axis\", \"duration\", \"second
precise statistics (median / percentile) decisions per internal policy
Analyzes campaign performance with multi-touch attribution, funnel conversion analysis, and ROI calculation for marketing optimization. Use when analyzing marketing campaigns, ad performance, attribution models, conversion rates, or calculating marketing ROI, ROAS, CPA, and campaign metrics across channels.
Python computations in science and engineering (pycse) - helps with scientific computing tasks including nonlinear regression, uncertainty quantification, design of experiments (DOE), Latin hypercube sampling, surface response modeling, and neural network-based UQ with DPOSE. Use when working with numerical optimization, data fitting, experimental design, or uncertainty analysis.
Guides exploration of $autocapture events captured by posthog-js to understand user interactions, find CSS selectors (especially data-attr attributes), evaluate selector uniqueness, query matching clicks ad-hoc, and create actions. Use when the user asks about autocapture data, wants to find what users are clicking, needs to build actions from click events, asks about elements_chain, wants to buil
Warehouse operations analyst at Northwind Logistics — the INSIDE-warehouse axis (inventory turnover, picking efficiency, labor utilization, dock-to-stock lag). Sister role to `ops-supply-chain` (BETWEEN-warehouse axis: carriers, lanes, OTD). Reads from `public.inventory_snapshots` (daily on-hand snapshots), `public.pick_events` (flow), `public.putaway_events`, `public.shifts`, `public.warehouse_locations`, `public.skus`, `public.cycle_counts`. Critical distinction: snapshot vs. flow — inventory_
Inspects URL paths and proposes, tests, orders, and applies project-level path cleaning rules so dynamic segments (numeric IDs, UUIDs, slugs, dates) collapse into readable aliases. Use when the user says \"clean the paths\", \"normalize URLs\", \"group similar pages\", \"too many distinct paths\", \"/users/123 and /users/456 are the same page\", \"set up path cleaning\", or asks why a Web analytic
Econometrics skill for panel data models. Activates when the user asks about: "panel data", "fixed effects", "random effects", "Hausman test", "within estimator", "between estimator", "two-way fixed effects", "clustered standard errors panel", "FE model", "RE model", "pooled OLS", "unobserved heterogeneity", "panel regression", "first difference estimator", "entity fixed effects", "time fixed effects", "面板数据", "固定效应", "随机效应", "豪斯曼检验", "双向固定效应", "面板回归", "个体效应", "时间效应", "一阶差分"
Opinionated Bayesian modeling workflow with PyMC and ArviZ. Contains critical guardrails (nutpie sampler, prior/posterior predictive checks, LOO-PIT calibration, prior sensitivity checks, 94% HDI, non-centered parameterizations, reproducible seeds) that agents won't apply unprompted — always consult before writing Bayesian model code. Trigger on: building probabilistic/Bayesian models, prior elicitation, MCMC inference, convergence diagnostics (divergences, R-hat, ESS), model comparison (LOO-CV,
Causal inference framework for answering "does X cause Y?" beyond correlation. DoWhy (Microsoft Research) provides the identify-estimate-refute loop: define a causal graph (DAG), identify the causal effect using backdoor/frontdoor/instrumental variable criteria, estimate treatment effects with multiple estimators, and validate results with automated refutation tests. Use when: distinguishing causation from correlation, estimating treatment effects (ATE, ATT, CATE), designing and analyzing A/B te
KQL language expertise for writing correct, efficient Kusto Query Language queries. Covers syntax gotchas, join patterns, dynamic types, datetime pitfalls, regex patterns, serialization, memory management, result-size discipline, and advanced functions (geo, vector, graph). USE THIS SKILL whenever writing, debugging, or reviewing KQL queries — even simple ones — because the gotchas section prevents the most common errors that waste tool calls and cause expensive retry cascades. Trigger on: KQL,
动态统计多Sheet Excel文件行数以判断大文件处理逻辑，并根据特定条件筛选数据、重命名字段后导出为包含下载链接的新Excel文件，适用于多Sheet数据探查与条件过滤导出场景。
Analyze data evolution patterns in construction organizations. Assess digital maturity and data strategy for construction companies
This skill should be used when the user asks to "run initial analysis", "analyze single-cell data", "QC my data", "run bioinformatics pipeline", "generate analysis report", "explore my dataset", "do exploratory data analysis", "initial data analysis", or needs to perform quality control, dimensionality reduction, clustering, or marker analysis on single-cell biology data (CyTOF, scRNA-seq, flow cytometry, proteomics).
Create UpSet plots to visualize set intersections as an alternative to Venn diagrams using UpSetR or upsetplot. Use when comparing overlapping gene sets, peak sets, or sample groups with more than 3 sets.
Orchestration for bibliometrics, topological network analysis, and hypothesis scoring.
Automated DataFrame analysis skill for statistical summaries, missing value detection, data type inference, and memory optimization recommendations.
Model team, game, and player outcomes from repeated sports events, matchup history, schedules, injuries, and performance metrics. Use for sports rankings, matchup forecasts, season projections, or fantasy-oriented state models. Load `okhp3-outcome-modeling-core` first; this adapter does not provide live odds or trade execution.
Write ggsql queries — a grammar of graphics for SQL. Use when the user wants to create, modify, or understand a ggsql visualization query.
Draw and export phylogenetic trees using Biopython Bio.Phylo with matplotlib. Use when creating publication-quality tree figures, customizing colors and labels, or exporting to image formats.
Performs placebo-in-time sensitivity analysis with hierarchical null model and optional Bayesian assurance. Use when checking model robustness, verifying lack of pre-intervention effects, or estimating study power.
从结构化数据中提取分类分布执行清洗与统计，生成多维度交叉分析、高分辨率对比图表及包含下载链接的完整分析报告，适用于大文件处理与嵌入式可视化场景。
从Excel提取多类型数据，并生成包含可视化图表与下载链接的综合分析报告。
利用交叉表与热力图对分类数据进行多维度占比分析，适用于奖项分布、绩效评估或市场占有率等结构化数据的清洗与可视化。
Python visual creation and matplotlib/seaborn patterns for PBIR reports. Automatically invoke when the user mentions "Python visual", "matplotlib in Power BI", "seaborn in Power BI", "pythonVisual", or asks to "create a Python visual", "add a matplotlib chart", "write a Python visual script".
用于分析包含多个Sheet的Excel文件，动态判断数据量级以决定是否转换为Parquet进行大文件处理，并支持跨Sheet的特定字段统计、数据清洗、交叉分析与可视化，最终生成带下载链接的汇总报告。
Generate SQL queries from natural language descriptions. Supports BigQuery, PostgreSQL, MySQL, and other dialects. Reads database schemas from uploaded diagrams or documentation. Use when writing SQL, building data reports, exploring databases, or translating business questions into queries.
Calculate read depth and coverage across genomic intervals using bedtools genomecov and coverage. Generate bedGraph files, compute per-base depth, and summarize coverage statistics. Use when assessing sequencing depth, creating coverage tracks, or evaluating target capture efficiency.
Use this skill whenever the user wants to conduct an event study, create event study plots, test for parallel trends, implement difference-in-differences designs, or work with any panel data estimation that involves pre/post treatment comparisons. Trigger on phrases like "event study", "parallel trends", "pre-trends", "dynamic treatment effects", "leads and lags", "TWFE", "two-way fixed effects", "staggered adoption", "staggered treatment", "difference-in-differences", "DiD", "Sun and Abraham",
Create clustered heatmaps with row/column annotations using ComplexHeatmap, pheatmap, and seaborn for gene expression and omics data visualization. Use when visualizing expression patterns across samples or identifying co-expressed gene clusters.
Create, read, edit, and analyse Excel spreadsheet files (.xlsx, .xlsm, .xltx, .csv, .tsv). Use whenever a spreadsheet file is the primary input or output — adding columns, computing formulas, formatting cells, building financial models, cleaning messy data, extracting tables, creating charts, or converting between tabular formats. Trigger when the user mentions an .xlsx file, "spreadsheet", "Excel", "budget", "data export", or any tabular data that belongs in a file rather than in chat.
Create circular genome visualizations with Circos and pyCircos. Display multi-track data including ideograms, genes, variants, CNVs, and interaction arcs. Use when creating circular genome visualizations.
用于大规模Excel数据的预处理，通过统计总行数判断是否转换为Parquet格式以提升读写效率，并使用正则表达式清洗指定文本列（如仅保留中文字符），最后导出清洗后的文件并提供下载链接。
Format user-provided CSV snippets into readable Markdown tables.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," or "tracking plan." For A/B test measurement, see ab-test-setup.
根据 Excel 数据量级自动判断处理策略，执行数值列清洗、条件过滤，并使用 openpyxl 对符合条件的单元格进行样式标记与导出。
Separates signal from noise in metrics you cannot observe directly, updating a belief from evidence instead of reacting to the latest reading - Bayesian updates over competing explanations, and a filter that says whether this week's move is real. Use when a metric moves and someone wants to act, when diagnosing why traffic or revenue changed, when a dashboard number contradicts intuition, or when deciding whether a trend is real yet.
读取多工作表Excel文件，自动处理合并单元格与数据清洗，进行交叉分组统计并生成带总计行的结果表，最后绘制支持中英文字体的美化柱状图，适用于多维度数据汇总与可视化分析。
Transform data into compelling narratives using visualization, context, and persuasive structure. Use when presenting analytics to stakeholders, creating data reports, or building executive presentations.
Core interval arithmetic operations including intersect, subtract, merge, complement, map, and groupby using bedtools and pybedtools. Use when finding overlapping regions, removing overlaps, combining adjacent intervals, or transferring annotations between interval files.
Choose, encode, color, label, and ship accurate accessible charts and dashboards — perceptual encoding ranking, chart-by-intent selection, data color scales, Tufte's data-ink discipline, and interaction patterns with concrete do/don't rules.
读取并解析单个Excel工作表数据，支持合并单元格处理、数据清洗、交叉分析及多维度可视化，适用于需要从单表中提取关键指标并进行趋势模拟与图表生成的场景。
Excel Automation: create workbooks, manage worksheets, read/write cell data, and format spreadsheets via Microsoft Excel and Google Sheets integration
对比Excel多表中的特定系数并对异常值进行颜色标记。
This skill covers causal machine learning methods in applied economics and quantitative social science. Use when implementing or choosing between modern ML-based causal estimators — including double machine learning, DML, partially linear models, interactive regression models, cross-fitting, Neyman orthogonality, debiased ML, causal forests, generalized random forest, GRF, honest causal trees, AIPW with machine learning, doubly robust with machine learning, DR-Learner, T-Learner, S-Learner, X-Le
How to author, edit, and adapt PostHog Signals scouts — the scheduled agents that scan a project and emit findings into the Signals inbox. Use when a user wants to customize a canonical scout for their own setup (narrow its scope, retune its thresholds, add disqualifiers), tweak a scout's schedule or dry-run posture, or write a brand-new scout from scratch for a specific use case (a custom event,
Designs and analyzes A/B tests end-to-end — frames a sharp hypothesis, computes required sample size and test duration, runs significance tests (two-proportion z-test, Welch's t-test, chi-square), reports confidence intervals and lift, and flags common pitfalls like peeking, multiple comparisons, and Simpson's paradox. Use this skill when the user mentions A/B testing, split testing, experiment design, conversion-rate experiments, statistical significance, p-values, sample size or power calculat
Search and query TikHub APIs for TikTok, Douyin, Xiaohongshu, Lemon8, Instagram, YouTube, Twitter, Reddit, and more. Use when user asks about social media data. Supports both English and Chinese queries.
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 / AEJ
Design and operationalize outcome models that compress noisy event histories into calibrated forecasts and constrained decisions. Use when a user asks about feature reduction, probability, expected value, aggregation, or moving a prediction method between sports, business, sales, advertising, finance, or prediction markets. Load a domain adapter when one fits.
Read this skill when you need to read or write Google Sheets. Works without opening a browser tab for reads.
Interactive naming convention standardization for TMDL-based Power BI semantic models. Automatically invoke when the user asks to "standardize naming conventions", "fix naming conventions", "clean up model names", "apply naming standards", "audit naming", "make names human readable", "rename fields", "fix abbreviations in model", or mentions renaming measures, columns, or tables for consistency across a model.
Build, manipulate, execute, validate, and version-control Jupyter notebooks programmatically. Use when working with .ipynb files, creating notebooks from code, running parameterised analyses, diffing/merging notebooks in git, stripping outputs, or converting notebooks to other formats. Triggers on: jupyter, notebook, ipynb, nbformat, nbconvert, nbclient, papermill, nbdime, cell, kernel, execute notebook, parameterise, notebook diff, notebook merge.
执行数值型数据的分布分析与异常值检测，支持通过正则表达式从文本中提取误差项并生成高分辨率的箱线图与直方图报告。
Guide economists to authoritative data sources with explicit, confirmed data specifications before retrieval; interfaces with Playwright MCP to navigate portals and extract real data, not articles about data.
When the user wants to plan, design, or implement an A/B test or experiment. Also use when the user mentions "A/B test," "split test," "experiment," "test this change," "variant copy," "multivariate test," "hypothesis," "conversion experiment," "statistical significance," or "test this." For tracking implementation, see analytics-tracking.
根据数据规模动态选择处理策略。
Analysiert bereitgestellte Bestandslisten oder Exporte ohne Bindung an ein bestimmtes Lagerprogramm.
Generate insightful, publication-quality visualizations from complex datasets.
When the user wants to set up, improve, or audit analytics tracking and measurement. Also use when the user mentions "set up tracking," "GA4," "Google Analytics," "conversion tracking," "event tracking," "UTM parameters," "tag manager," "GTM," "analytics implementation," "tracking...
Diagnoses why a session recording is missing or was not captured. Use when a user asks why a session has no replay, why recordings aren't appearing, or wants to troubleshoot session replay capture issues for a specific session ID or across their project. Covers SDK diagnostic signals, project settings, sampling, triggers, ad blockers, and quota/billing scenarios.
Expert data analyst and reporting specialist focused on transforming raw data into actionable business insights, performance tracking, and strategic decision support. Specializes in data visualizat...
- **Purpose**: {What question does this analysis answer?}
Query Solana wallets, tokens, txs, and NFTs in USD.
Perform cohort analysis on user engagement data — retention curves, feature adoption trends, and segment-level insights. Use when analyzing user retention by cohort, studying feature adoption over time, investigating churn patterns, or identifying engagement trends.
统计多Sheet Excel总行数并根据规模选择处理策略，提取特定维度信息进行去重统计，并生成摘要与明细报表。
Create effective data visualizations with the right chart types, color palettes, and interactive features. Based on Anthropic's Claude Cookbooks (vision capabilities).
Select NFL fantasy players, DFS lineups, salary-cap rosters, waiver priorities, or trade targets under a scoring and budget system. Use when comparing projected points per cost, replacement value, positional scarcity, floor, ceiling, or roster correlation. Load `okhp3-outcome-modeling-core` and `okhp3-outcome-modeling-sports` first; this is fantasy decision support, not sportsbook betting advice.
Produce a chart, graph, dashboard, or any data visualization that reads as one system — elegant, accessible, and consistent in light and dark — BRAND-NEUTRAL, shipping a placeholder palette to swap for your own. Read this BEFORE generating ANY chart (bar, line, area, heatmap, scatter, sparkline, donut), choosing chart colors, building a stat tile / meter / KPI row, or laying out a dashboard. Teaches a design-system-AGNOSTIC method: a form heuristic, a color formula with a runnable validator, mar
Signals scout that watches a PostHog project's most-viewed dashboards and insights for recent anomalies — sudden bursts, drops, flat-lines, and trend breaks at the daily or hourly level. It discovers what the team actually looks at (view counts, dashboard access), curates a durable watchlist in the scratchpad, and balances re-checking known high-value insights (exploit) against discovering new one
万行以上 Excel 数据集的高性能分析引擎。提供 openpyxl read_only 流式读取（iter_rows 支持 10 万行以上）、Parquet 转换加速、内存优化、分块处理和大文件写入模式。**遇到以下任一情况就主动使用本 skill**：①数据行数 ≥ 10k（由 sn-da-excel-workflow 的行数评估步骤触发）；②用户出现触发词：大文件 / 大数据量 / 性能优化 / 内存不足 / OOM / 百万行 / 十万行 / 流式读取 / Parquet / 分块处理 / large file / big data / streaming read / chunked processing；③直接使用 pd.read_excel() 导致超时或内存溢出；④用户明确要求对大规模数据集进行高性能处理。仅不用于：小于 10k 行的常规 Excel 分析（使用 sn-da-excel-workflow 即可）。
Econometrics skill for OLS regression and linear models. Activates when the user asks about: "run OLS", "linear regression", "ordinary least squares", "interpret regression results", "heteroskedasticity", "multicollinearity", "regression assumptions", "robust standard errors", "GLS", "WLS", "fit a regression model", "check regression diagnostics", "OLS假设", "最小二乘法", "线性回归", "回归系数", "残差检验", "异方差", "多重共线性", "普通最小二乘", "稳健标准误", "回归诊断"
Create genome browser-style visualizations showing multiple data tracks (coverage, peaks, genes) using pyGenomeTracks, Gviz, and IGV. Use when visualizing genomic data at specific loci with multiple aligned tracks.
Operate Moralis EVM wallet and token reads through UXC with a curated OpenAPI schema, API-key auth, and wallet-intelligence guardrails.
End-to-end spatial transcriptomics workflow for Visium/Xenium data. Covers data loading, preprocessing, spatial analysis, domain detection, and visualization with Squidpy. Use when analyzing spatial transcriptomics data.
End-to-end data analysis workflow in R or Python — from exploration through regression to publication-ready tables and figures. Make sure to use this skill whenever the user wants to run any empirical analysis, write analysis code, or produce output from data. Triggers include: "analyze this data", "run a regression", "write R code for this", "write Python code for this", "I have a dataset", "help me with this regression", "run a DiD", "run an RDD", "event study", "IV regression", "fit a model",
图片理解与数据提取 skill。当图片文件（.png/.jpg/.jpeg/.gif/.webp/.bmp）是主要输入且用户需要理解、提取数据或分析图片内容时使用。提供预配置的 caption 脚本（scripts/caption.py），通过 vision 模型将图片转为文本描述，无需额外配置 API Key。覆盖：(1) 通过 scripts/caption.py 对图表/表格/截图/流程图进行 caption，(2) 将 caption 文本解析为结构化 DataFrame，(3) 基于提取数据重新生成可视化图表，(4) 导出为 Excel/CSV。**遇到以下任一情况就主动使用本 skill，不要自行猜测图片内容**：①用户出现触发词：图片分析 / 图表提取 / 表格识别 / OCR / 图片描述 / 截图分析 / 图表数据 / 提取图片中的数据 / 图片转表格 / 识别图片 / image caption / extract data from image / chart analysis / table OCR；②用户上传或指定了图片文件（.png / .jpg / .jp
读取多 Sheet Excel 文件，根据数据量动态选择处理策略，支持特定区域数据提取、大文件 Parquet 转换、统计分析及可视化图表生成。
Create effective data visualizations with Python (matplotlib, seaborn, plotly). Use when building charts, choosing the right chart type for a dataset, creating publication-quality figures, or applying design principles like accessibility and color theory.
Google Sheets: Append a row to a spreadsheet.
Product analytics analyst at Northwind Logistics — owns in-product behavior on the Northwind portal AFTER signup (feature adoption, A/B test outcomes, funnel conversion, engagement depth). Sister role to `growth-marketing` (which owns acquisition + retention BEFORE/AFTER signup). Reads from `public.events`, `public.feature_flags`, `public.experiment_assignments`, `public.experiment_outcomes`, `public.feature_usage`, `public.user_properties`. Adoption-first, ordered-event funnels, A/B tests with
Standard data analysis - comprehensive statistical analysis (Sonnet-tier)