introspection

Automate structured introspection with taxonomy-aligned self-assessment and auditable memory recording.

Updated Apr 27, 2026

Search Tags:#introspection #taxonomy #self-assessment #self-review #session-end #llmem

Ready to run

npx skills add https://github.com/MichielDean/LLMem --skill introspection-michieldean

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: introspection
Source: https://github.com/MichielDean/LLMem/tree/main/skills/introspection
Command: npx skills add https://github.com/MichielDean/LLMem --skill introspection-michieldean

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Operational framework that guides reflective work, self-assessment, and post-mmortem analysis for LLMem agents, enabling consistent, auditable introspection.

Core Features & Use Cases

Self-assessment and self-review triggers
Session-end analysis and sampajanna checks
Taxonomy-driven recording and memory externalization

Quick Start

Load this skill during session end to enable automatic introspection questions and taxonomy-guided recordings.

Frequently Asked Questions about introspection

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

FAQPage Schema

How do I automate self-assessment and session-end analysis for AI workflows?▼

Self-assessment applies taxonomy-driven introspection rules to automate session-end analysis, ensuring consistent and traceable memory recording. This framework guides post-mortem reviews across research workflows and LLMem integrations with auditable steps.

What is structured introspection and how does it guide error-pattern analysis?▼

Structured introspection is an operational framework that applies taxonomy alignment and traceable memory recording to detail actionable rules for self-review. It standardizes error-pattern analysis by defining explicit categories, checks, and safe recording commands.

Can I use taxonomy-guided recording to externalize memory in LLMem integrations?▼

Yes, taxonomy-guided recording externalizes memory by requiring explicit taxonomy alignment during reflective work. This ensures safe, auditable steps and defined recording commands are used to capture session-end checks within LLMem integrations.

Does this introspection framework require specific dependencies or components to run?▼

No specific dependencies or components are required to run the introspection framework. It operates as a standalone operational guide loaded during session-end to enable automatic introspection questions and taxonomy-guided recordings.

When should I trigger post-mortem reviews using this self-review framework?▼

Post-mortem reviews should be triggered at session-end or during reflective work to analyze error patterns. The framework applies sampajanna checks and taxonomy-driven recording to ensure safe, auditable post-mortem analysis.

What is the best way to ensure traceable memory recording during self-assessment?▼

The best way to ensure traceable memory recording is to apply explicit taxonomy alignment and defined recording commands during self-assessment. This operational framework standardizes memory externalization with auditable steps and defined categories.