菜单
Reframe technical analysis findings in business language. Activate when findings need to reach a non-technical audience, when jargon is obscuring the message, or when a stakeholder needs to understand and act on statistical results without a technical background.
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。
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.
基于 SOC 职业分类
| name | technical-to-business-translator |
| description | Reframe technical analysis findings in business language. Activate when findings need to reach a non-technical audience, when jargon is obscuring the message, or when a stakeholder needs to understand and act on statistical results without a technical background. |
scripts/jargon_detector.py to flag technical terms; score reading level against grade-10 targetreferences/audience_personas.mdreferences/translation_patterns.md (e.g. "statistically significant at p < 0.05" → "we're 95% confident this result is real, not random"); use metaphor bank for complex conceptsscripts/jargon_detector.py on the translated version; verify grade-10 readability and no residual jargon