菜单
Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks.
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.
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.
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.
| name | context-packager |
| description | Efficiently package context for AI-assisted analysis. Use when preparing to work with Claude on analysis, organizing context documents, or structuring prompts for complex analytical tasks. |
Before starting an AI-assisted analysis session when the task requires more than a single prompt — complex investigations, multi-step analyses, or work that depends on project-specific knowledge. A well-packaged context bundle reduces back-and-forth and produces better first responses.
references/context_layering_guide.md to decide which layers are needed: task definition, business context, data schema, prior findings, constraints, and output format.scripts/context_bundler.py to merge multiple context files into a single structured bundle; it deduplicates and applies the layering order.scripts/token_counter.py on the bundle to estimate token count; trim lower-priority layers if over budget (see references/context_layering_guide.md for trimming priority).references/context_quality_rubric.md; a good bundle scores ≥ 7/10 on completeness, clarity, and relevance.assets/context_package_template.md so it can be reused or updated for follow-up sessions.context_package_template.md)