remember

Name: remember
Availability: InStock
Author: agenticnotetaking

Capture friction points as structured markdown methodology notes from conversations.

3.5k|220|Updated Feb 15, 2026

Search Tags:#methodology #knowledge management #friction capture #agent improvement #session mining

Ready to run

npx skills add https://github.com/agenticnotetaking/arscontexta --skill remember-agenticnotetaking

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: remember
Source: https://github.com/agenticnotetaking/arscontexta/tree/main/skill-sources/remember
Command: npx skills add https://github.com/agenticnotetaking/arscontexta --skill remember-agenticnotetaking

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes references (resource) components.

What problem does it solve?

This Skill helps you capture and codify friction points encountered during agent interactions, turning them into actionable methodology notes to improve the agent's behavior over time.

Core Features & Use Cases

Explicit Capture: Directly describe friction points for the agent to learn from.
Contextual Review: Automatically scan recent conversation for corrections and prompt for capture.
Session Mining: Analyze past session transcripts to identify recurring friction patterns.
Use Case: After an agent makes a mistake, you can tell it /remember "don't process personal notes like research", and it will create a formal methodology note to prevent future errors.

Quick Start

Use the remember skill to capture friction by describing it directly.

Frequently Asked Questions about remember

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

FAQPage Schema

How do I capture agent friction and turn it into methodology notes?▼

You can capture agent friction by explicitly describing the issue, prompting a contextual review of recent corrections, or mining session transcripts. The skill parses this input and writes structured markdown methodology notes to improve future behavior.

What is session mining for agent improvement and how does it work?▼

Session mining for agent improvement analyzes past conversation transcripts to identify recurring friction patterns. It parses the history to automatically extract behavioral corrections and codify them into structured markdown notes.

Can I automatically scan recent conversations for corrections to improve agent behavior?▼

Yes, you can automatically scan recent conversations for corrections through a contextual review. The skill analyzes your conversation history, identifies friction points, and prompts you to capture them as formal methodology notes.

What's the best way to codify system methodology from agent mistakes?▼

The best way to codify system methodology is to explicitly describe the mistake, such as issuing a command to remember a specific constraint. The skill then generates a structured markdown note to prevent the agent from repeating the error.

Do I need to provide transcripts for session mining of friction patterns?▼

Yes, session mining requires past session transcripts as input to identify recurring friction patterns. The skill parses these transcripts to extract behavioral corrections and write them into structured methodology notes.