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provenance-audit

Record AI generation provenance with decision factors, data lineage, and reasoning chains.

783|62|Updated Jan 19, 2026
Search Tags:#ai#provenance#data lineage#audit trail#cost tracking#explainable ai
One-click install
npx skills add https://github.com/dadbodgeoff/drift --skill provenance-audit
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: provenance-audit
Source: https://github.com/dadbodgeoff/drift/tree/main/drift%20v1%20depreciated/skills/provenance-audit
Command: npx skills add https://github.com/dadbodgeoff/drift --skill provenance-audit

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill addresses the need for transparency and accountability in AI-generated content by meticulously tracking its origin, decision-making process, and associated costs.

Core Features & Use Cases

  • Decision Factor Tracking: Records the specific inputs and parameters that influenced an AI's output.
  • Data Lineage: Maps the flow of data used in the generation process.
  • Reasoning Chains: Documents the step-by-step logic the AI followed.
  • Confidence Scoring: Assigns a quantifiable measure of certainty to the AI's output.
  • Cost Tracking: Monitors the computational resources and expenses incurred during generation.
  • Use Case: Ensure regulatory compliance by providing a complete audit trail for AI-generated financial reports, detailing every factor that led to the final figures.

Quick Start

Use the provenance-audit skill to generate a detailed provenance record for a new content suggestion.

Frequently Asked Questions about provenance-audit

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I track AI generation provenance for regulatory compliance?▼

AI generation provenance tracking records decision factors, data lineage, reasoning chains, confidence scoring, and cost tracking to provide a comprehensive audit trail for explainable AI systems and regulatory compliance.

What is included in an AI audit trail for generated content?▼

An AI audit trail includes the specific decision factors and parameters influencing output, data lineage mapping, step-by-step reasoning chains, confidence scoring, and computational cost tracking for the generated content.

How do I document data lineage and reasoning chains for AI outputs?▼

Document data lineage and reasoning chains by mapping the flow of data used in the generation process and recording the step-by-step logic the AI followed to reach its output.

Can I track computational cost and confidence scoring for AI-generated content?▼

Yes, provenance tracking monitors computational resources and expenses incurred during generation and assigns a quantifiable confidence score to measure the certainty of the AI output.

Does provenance tracking work with TypeScript for defining data sources and metrics?▼

Yes, provenance tracking is implemented using TypeScript with defined interfaces for data sources, decision factors, reasoning steps, and generation metrics to structure the audit trail data.

When do I need explainable AI audit trails for financial reports?▼

You need explainable AI audit trails for financial reports when regulatory compliance requires detailing every factor, data source, and reasoning step that led to the final generated figures.

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