菜单
Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy.
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。
基于 SOC 职业分类
Create standardized metadata for data assets. Use when documenting new datasets, building data catalogs, improving data discoverability, or creating data dictionaries for teams.
Structure analysis approach before starting work. Use when receiving new analysis requests, breaking down complex questions into steps, or planning iterative analysis workflows.
Comprehensive data quality assessment against business rules, schema constraints, and freshness expectations. Activate when validating production pipelines, onboarding new data sources, or when stakeholders report data quality concerns.
Create effective, publication-ready data visualizations. Use when choosing chart types, designing presentation visuals, building dashboard charts, or applying visual design best practices to data output.
Create a clear, transparent explanation of analytical methodology for any audience level. Activate when you deliver findings that require the audience to trust the method — A/B tests, attribution models, forecasts, statistical analyses, or anything where "how did you get that?" is a likely question.
Analyse temporal patterns in data including trends, seasonality, anomalies, and forecasting. Activate when you need to understand trends over time, detect seasonality, identify anomalies in time series, or build simple forecasting models for planning.
| name | segmentation-analysis |
| description | Identify distinct customer or user segments based on behavior, attributes, or value. Activate when you need to answer "who are our best customers?" or "what distinct groups exist in our user base?" and need data-driven profiles to inform strategy. |
scripts/segmentation_engine.py supporting k-means clustering (for numeric behavioural data) or rule-based segmentation (for business-defined groups); validate with silhouette score > 0.3 for clusteringscripts/segment_builder.pyreferences/segment_naming_conventions.mdassets/segment_profiles_template.mdscripts/segmentation_engine.py — k-means or rule-based segmentation with validationscripts/segment_builder.py — per-segment profiling: mean/median metrics and top borough per segmentreferences/segment_naming_conventions.md — naming patterns and anti-patternsassets/segment_profiles_template.md (filled) — per-segment profiles with size, defining metrics, and strategic recommendations