When should I use a 'Full' disclaimer?

The Full disclaimer is recommended for formal deliverables, stakeholder presentations, or any output subject to regulatory or compliance requirements.

Does this help with EU AI Act compliance?

Yes, it provides structured templates that help satisfy transparency obligations for AI-generated content as required by the EU AI Act.

Is this skill required for all AI-assisted tasks?

While not needed for personal exploratory analysis, it is highly recommended for any output that influences decision-making or is shared with others.

Why should I use the GenAI Disclaimer skill?

It ensures transparency and trust by clearly labeling which parts of your analysis are deterministic results and which parts are AI-generated interpretations.

Does this skill work with the qsv data toolkit?

Yes, it is specifically optimized to work alongside qsv, distinguishing deterministic data wrangling from Claude's AI-generated insights.

GenAI Disclaimer & Attribution

Name: GenAI Disclaimer & Attribution
Author: dathere

bydathere

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3,614

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데이터 과학 및 ML

Provides standardized disclaimer and attribution templates for AI-generated or AI-assisted data analysis outputs.

The GenAI Disclaimer skill ensures transparency and regulatory compliance by providing structured attribution templates for AI-assisted data workflows. It bridges the gap between deterministic data processing—performed by tools like qsv—and the narrative interpretations generated by LLMs. By offering tiered disclosure levels ranging from short footnotes to comprehensive formal reports, this skill helps analysts clearly distinguish between reproducible statistical facts and AI-generated insights, making it an essential utility for professional data reporting in regulated environments.

주요 기능

01Three standardized disclosure levels: Short, Medium, and Full

023,614 GitHub stars

03Regulatory guidance for EU AI Act, HIPAA, and financial compliance

04Attribution matrix to distinguish deterministic results from AI interpretations

05Reproducibility documentation for AI-assisted narrative summaries

06Command-specific integration mapping for data profiling and validation

사용 사례

01Generating transparent attribution for stakeholder presentations and reports

02Ensuring compliance with AI transparency laws in regulated industries

03Clarifying the source of data dictionaries and quality assessments

주요 기능

01Three standardized disclosure levels: Short, Medium, and Full

023,614 GitHub stars

03Regulatory guidance for EU AI Act, HIPAA, and financial compliance

04Attribution matrix to distinguish deterministic results from AI interpretations

05Reproducibility documentation for AI-assisted narrative summaries

06Command-specific integration mapping for data profiling and validation

사용 사례

01Generating transparent attribution for stakeholder presentations and reports

02Ensuring compliance with AI transparency laws in regulated industries

03Clarifying the source of data dictionaries and quality assessments