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Remember

Store developer learnings and decisions in PostgreSQL with BGE embeddings.

8|1|Updated Jan 4, 2026
Search Tags:#developer tools#knowledge management#learning#memory#pattern recognition#decision logging
One-click install
npx skills add https://github.com/scooter-lacroix/Maestro --skill remember-scooter-lacroix
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: Remember
Source: https://github.com/scooter-lacroix/Maestro/tree/main/maestro/skills/context/remember
Command: npx skills add https://github.com/scooter-lacroix/Maestro --skill remember-scooter-lacroix

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) components.

What problem does it solve?

This Skill helps you capture and store important learnings, decisions, and patterns encountered during development, ensuring they are not lost and can be easily recalled later.

Core Features & Use Cases

  • Persistent Knowledge Storage: Saves key insights into a structured memory system (PostgreSQL with BGE embeddings).
  • Auto-Type Detection: Intelligently categorizes learnings (e.g., WORKING_SOLUTION, ARCHITECTURAL_DECISION, FAILED_APPROACH) if not explicitly specified.
  • Tag Extraction: Automatically identifies relevant tags within the content for better organization and retrieval.
  • Use Case: After resolving a complex bug, you can use /remember to store the fix, preventing future developers from encountering the same issue without a known solution.

Quick Start

Use the remember skill to store the learning that session affinity uses the terminal PID.

Frequently Asked Questions about Remember

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

FAQPage Schema
How do I store developer learnings and architectural decisions for future recall?▼

Developer learnings are stored in a persistent memory system using PostgreSQL and BGE embeddings. This captures decisions and patterns into a structured format for reliable future recall during development.

What is the best way to log failed approaches and working solutions during development?▼

Auto-type detection categorizes learnings into types like FAILED_APPROACH and WORKING_SOLUTION, automatically extracting tags. This structures bug fixes and working solutions for better organization and recall.

Do I need PostgreSQL to record development patterns and session affinity insights?▼

PostgreSQL is required to record development patterns and session affinity insights. It acts as the persistent storage backend alongside BGE embeddings to facilitate knowledge management and recall.

Can I manually categorize learnings or does the system auto-detect types?▼

You can manually specify learning types or rely on auto-detection. If not explicitly specified, the system intelligently categorizes content as WORKING_SOLUTION, ARCHITECTURAL_DECISION, or FAILED_APPROACH.

How does tag extraction work when saving bug fixes to memory?▼

Tag extraction automatically identifies relevant tags within the content when saving bug fixes to memory. This enhances recall by organizing stored learnings without requiring manual tagging input.

What are the limitations of using BGE embeddings for knowledge management?▼

Limitations include the dependency on PostgreSQL for persistent storage and BGE embeddings for vector indexing. The system requires this database environment to store and retrieve development patterns effectively.

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