What is the Audit skill for Claude Code?

It is a specialized capability within the bkit plugin that provides full visibility into the AI's actions, logging every decision, phase transition, and automated task for better developer transparency.

Can I see why the AI made a specific decision?

Yes, the 'trace' command allows you to view the specific rationale, confidence score, and alternatives considered by the AI for any specific feature or task.

How long are the audit logs stored?

The skill maintains a 30-day retention policy with a 100MB total storage budget, automatically pruning the oldest entries to maintain project health.

Where are the audit logs stored locally?

Logs are stored as JSONL files within the .bkit/audit/ and .bkit/decisions/ directories of your project for easy programmatic access.

Does this skill help track automation efficiency?

Absolutely. The 'summary' command provides detailed metrics on your automation-to-manual ratio and trust score improvements over daily or weekly periods.

Audit & AI Transparency

Name: Audit & AI Transparency
Author: popup-studio-ai

bypopup-studio-ai

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525

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Analytics & Monitoring

Provides complete visibility into AI decision-making through detailed audit logs, activity traces, and performance summaries.

The Audit skill is a specialized transparency tool for the bkit Vibecoding Kit that enables developers to monitor and verify Claude's actions within the PDCA workflow. It provides a structured window into session histories, decision rationales, and automation metrics, allowing users to trace why specific code paths were chosen or how phases transitioned. By aggregating daily summaries and offering granular search capabilities, it ensures that AI-native development remains accountable, observable, and easy to debug for the human-in-the-loop.

Key Features

01Trace AI decision-making logic and rationales for specific features and code changes

02Monitor trust scores and automation ratios across the entire development lifecycle

03525 GitHub stars

04View real-time audit logs with timestamped action entries and session history

05Generate daily and weekly performance summaries of phase transitions and automation levels

06Search historical logs by action type, feature name, or specific dates using a unified query system

Use Cases

01Auditing automated code changes to ensure compliance and understand the 'why' behind AI-generated logic

02Reviewing daily productivity and phase transition metrics for team progress reporting

03Debugging unexpected AI behavior by reviewing specific decision rationales and alternatives

Key Features

01Trace AI decision-making logic and rationales for specific features and code changes

02Monitor trust scores and automation ratios across the entire development lifecycle

03525 GitHub stars

04View real-time audit logs with timestamped action entries and session history

05Generate daily and weekly performance summaries of phase transitions and automation levels

06Search historical logs by action type, feature name, or specific dates using a unified query system

Use Cases

01Auditing automated code changes to ensure compliance and understand the 'why' behind AI-generated logic

02Reviewing daily productivity and phase transition metrics for team progress reporting

03Debugging unexpected AI behavior by reviewing specific decision rationales and alternatives