self-reflection

Name: self-reflection
Availability: InStock
Author: gula00

Analyze JSONL session transcripts and route lessons to documentation files.

2|Updated Mar 10, 2026

Search Tags:#documentation #knowledge management #learning #self-reflection #session analysis #agent improvement

Ready to run

npx skills add https://github.com/gula00/autoclaw-skills --skill self-reflection-gula00

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: self-reflection
Source: https://github.com/gula00/autoclaw-skills/tree/main/agent-self-reflection-1.0.0
Command: npx skills add https://github.com/gula00/autoclaw-skills --skill self-reflection-gula00

SYSTEM DOCUMENTATION & REQUIREMENTS

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

What problem does it solve?

This Skill automates the process of reviewing past agent interactions, identifying successes and failures, and distilling actionable insights to improve future performance.

Core Features & Use Cases

Session Analysis: Automatically reviews recent agent sessions for key events, errors, and successful interactions.
Insight Generation: Extracts specific, actionable lessons learned from session analysis.
Knowledge Management: Routes insights to appropriate documentation files (AGENTS.md, TOOLS.md, memory/, etc.) for persistent learning.
Use Case: After a series of complex user requests, this skill will analyze the interactions, note any misunderstandings or inefficient tool usage, and update the agent's internal documentation with specific guidance to avoid similar issues in the future.

Quick Start

Run the self-reflection skill to analyze recent sessions and update documentation.

Frequently Asked Questions about self-reflection

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

FAQPage Schema

How do I automate session analysis to identify lessons learned from agent interactions?▼

Automating session analysis to identify lessons learned involves parsing JSONL session transcripts to detect successes, failures, and tool usage patterns. This process extracts actionable insights from agent interactions and distills them into concise improvements.

What is the best way to update AGENTS.md with actionable insights from recent agent sessions?▼

Updating AGENTS.md with actionable insights requires analyzing recent agent session transcripts for specific events and routing the extracted lessons directly into the file. This ensures internal documentation reflects guidance to avoid future issues.

How does parsing JSONL session data work for agent improvement and knowledge management?▼

Parsing JSONL session data for agent improvement works by applying critical thinking to review past interactions, identifying inefficient tool usage, and routing concise, actionable insights to relevant workspace files like TOOLS.md and memory logs.

Do I need JSONL session data to perform self-reflection on agent sessions?▼

Yes, you need JSONL session data to perform self-reflection on agent sessions. Parsing this structured transcript data is required to identify key events, errors, and successful interactions, and to generate specific, actionable lessons learned.

What are the limitations of automating self-reflection for agent session analysis?▼

A limitation of automating self-reflection for agent session analysis is its reliance on parsing JSONL data; unstructured or missing transcript data can hinder the extraction of successes, failures, and tool usage patterns needed for knowledge management.