slm-show-patterns

Name: slm-show-patterns
Availability: InStock
Author: Qualixar

Extract learned tech preferences, workflow sequences, and project context from SuperLocalMemory.

206|34|Updated Feb 5, 2026

Search Tags:#patterns #memory #workflows #preferences #learned-patterns #superlocalmemory #coding-identity

Ready to run

npx skills add https://github.com/qualixar/superlocalmemory --skill slm-show-patterns

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: slm-show-patterns
Source: https://github.com/qualixar/superlocalmemory/tree/main/ide/skills/slm-show-patterns
Command: npx skills add https://github.com/qualixar/superlocalmemory --skill slm-show-patterns

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Shows patterns learned by SuperLocalMemory about your preferences, workflow sequences, and coding identity, helping you recall and audit what the AI has captured about you.

Core Features & Use Cases

Surface tech preferences, workflow patterns, and active project context learned across your sessions.
Support quick recall, review, and debugging of memory-based insights to tailor AI interactions.
Use case: review long-term patterns before onboarding new tools or teammates to align AI behavior with your style.

Quick Start

Ask the AI to list your learned patterns.

Frequently Asked Questions about slm-show-patterns

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

FAQPage Schema

How do I review learned patterns and tech preferences captured by AI memory?▼

To check captured workflow sequences, extract structured pattern outputs from your memory data. This surfaces active project context and long-term preferences, enabling targeted debugging of memory-based insights.

Do I need SuperLocalMemory V3 to surface learned coding patterns?▼

Yes, extracting learned coding patterns requires SuperLocalMemory V3 with learning features enabled. You must also have access to memory data to generate the structured pattern outputs for auditing.

What is the best way to audit AI memory for my coding identity and workflow history?▼

The best way to audit AI memory for coding identity is to extract and surface learned patterns. This reveals your tech preferences and workflow sequences, helping you recall long-term behaviors before onboarding new tools.

Can I debug memory-based insights and active project context across sessions?▼

Yes, you can debug memory-based insights by reviewing extracted patterns and active project context. This helps identify what the AI has captured across sessions, enabling targeted debugging of workflow sequences and preferences.

Why does extracting memory patterns fail without learning features enabled?▼

Extracting memory patterns fails without learning features enabled because the Skill relies on SuperLocalMemory V3 to store tech preferences and workflow sequences. Without this underlying data access, structured pattern outputs cannot be generated.