kiroku

Record autonomous decisions, rationales, and realizations in indexed sessions.

1|Updated Apr 18, 2026

Search Tags:#verification #session management #transparency #decision logging #autonomy #audit trail

Ready to run

npx skills add https://github.com/ntholm86/autonomous-agent-skills --skill kiroku

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: kiroku
Source: https://github.com/ntholm86/autonomous-agent-skills/tree/main/archive/v2/kiroku
Command: npx skills add https://github.com/ntholm86/autonomous-agent-skills --skill kiroku

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill requires powershell, and includes scripts (resource) and references (resource) components.

What problem does it solve?

This Skill manages comprehensive evidence trails to make autonomous agent actions observable and auditable, supporting trust and control.

Core Features & Use Cases

Session Management: Initiates, closes, and indexes autonomous sessions with detailed metadata.
Trail Recording: Records decisions, rationales, and realized insights during work to ensure accountability.
Use Case: During AI-driven software refactoring, log each decision, reasoning, and outcome for future audit and trust validation.

Quick Start

Use the kiroku skill to start a session with a specific identifier, then record decisions and close the session when done.

Frequently Asked Questions about kiroku

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

FAQPage Schema

How do I create an audit trail for autonomous AI decisions during a session?▼

To create an audit trail for autonomous AI decisions, you initiate a session with a specific identifier, record decisions and rationales during work, and close the session to index the metadata. This ensures accountability and transparency in AI operations.

What is decision logging for AI transparency and when do I need it?▼

Decision logging for AI transparency is the process of recording every autonomous decision, rationale, and realization during a session. You need it when running AI-driven tasks like software refactoring to ensure actions are observable, auditable, and trustworthy.

How do I track autonomous agent actions and session metadata end to end?▼

You track autonomous agent actions by using session management to initiate, close, and index operations with detailed metadata. During the session, trail recording captures every decision and insight, ensuring comprehensive evidence trails for future audits.

Do I need PowerShell to use this decision logging and session management skill?▼

Yes, you need PowerShell available in your environment, as it is a required dependency for running the scripts that manage session initiation, trail recording, and indexing.

Can I use this audit trail tool for AI-driven software refactoring?▼

Yes, you can use this audit trail tool for AI-driven software refactoring. It logs each decision, reasoning, and outcome during the work, providing the evidence trails needed for future audit and trust validation of the autonomous process.

What's the best way to ensure accountability in autonomous work without manual logs?▼

The best way to ensure accountability in autonomous work is to automate evidence trail recording. This approach captures decisions and rationales in real-time during a session, eliminating manual logging gaps and providing a complete, auditable history of AI actions.