Does this skill require an external database?

No, The Memory is entirely file-based. It operates locally on your project files, requiring no external services, databases, or cloud dependencies.

How does the skill know when to learn a new rule?

The Memory triggers learning when it detects explicit language (e.g., 'Always use...'), when you manually edit AI-generated code, or when the Verifier identifies a recurring correction.

How does The Memory store my preferences?

It saves rules as human-readable Markdown files within the studio/rules/ directory of your project, making them easy to edit, audit, and commit to version control.

Can I override a stored rule for a specific task?

Yes, you can use the /task:ignore-rules command or provide an inline override to temporarily suspend specific preferences for a single task.

The Memory

Name: The Memory
Author: twofoldtech-dakota

bytwofoldtech-dakota

Productivity & Workflow

Persists user preferences and technical constraints across coding sessions to eliminate repetitive instructions and session amnesia.

About

The Memory is a self-learning system designed to bridge the gap between AI coding sessions by storing project-specific rules, coding conventions, and personal preferences in human-readable Markdown files. It automatically detects relevant technical domains—such as Frontend, Backend, or DevOps—to inject specific constraints into the agent's context. This ensures that every generated code snippet aligns with your established architectural standards and stylistic choices without the need for manual re-teaching or a centralized database.

Key Features

Automatic learning triggers from user corrections and explicit commands
Persistent preference learning across multiple coding sessions
Context-aware rule prioritization with support for manual overrides
Domain-specific rule injection for Frontend, Backend, Security, and DevOps
0 GitHub stars
Human-readable Markdown storage for easy auditing and version control

Use Cases

Enforcing consistent architectural patterns like the Repository or Singleton pattern
Automating the adherence to security protocols and data validation rules
Maintaining specific UI/UX styling conventions across a distributed development team

About

Key Features

Automatic learning triggers from user corrections and explicit commands
Persistent preference learning across multiple coding sessions
Context-aware rule prioritization with support for manual overrides
Domain-specific rule injection for Frontend, Backend, Security, and DevOps
0 GitHub stars
Human-readable Markdown storage for easy auditing and version control

Use Cases

Enforcing consistent architectural patterns like the Repository or Singleton pattern
Automating the adherence to security protocols and data validation rules
Maintaining specific UI/UX styling conventions across a distributed development team