memories

Persist and organize durable project memories in a centralized store.

1|1|Updated Jun 2, 2025

Search Tags:#tooling #workflows #knowledge-management #architecture-decisions #memory-management #memories

Ready to run

npx skills add https://github.com/BumpyClock/dotfiles --skill memories

💬 Prompt for ChatGPT / Claude Chat▼

Please help me install this Agent Skill.
Skill: memories
Source: https://github.com/BumpyClock/dotfiles/tree/main/.ai_agents/skills/memories
Command: npx skills add https://github.com/BumpyClock/dotfiles --skill memories

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Capture, update, and curate durable project memories such as architecture decisions, coding preferences, workflows, tooling choices, and domain rules. Use when the user asks to save/remember a memory or when you identify high-leverage knowledge that should persist across sessions. Also use when asked to review, validate, or prune stored memories.

Core Features & Use Cases

Centralized memory store for project knowledge with evolution history.
Automated tagging, categorization, and indexing for quick recall.
Review and prune memories to keep the knowledge base relevant and durable.

Quick Start

Tell the AI to save a memory to the memories repository.

Frequently Asked Questions about memories

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

FAQPage Schema

How do I save architecture decisions so they persist across coding sessions?▼

To save architecture decisions across sessions, tell the AI to save a memory to the centralized memory store. The skill automatically captures, tags, and indexes durable project knowledge for quick recall and reuse.

What is the best way to organize project memories like coding preferences and tooling choices?▼

Organizing project memories like coding preferences and tooling choices is handled by applying automated tagging, categorization, and indexing in a centralized store. The skill manages metadata using YAML frontmatter to maintain a structured knowledge base.

Can I review and prune outdated domain rules from my project knowledge base?▼

You can review and prune outdated domain rules by asking the AI to validate or clean up stored memories. This process ensures the persisted knowledge base remains relevant and durable over time.

How does memory evolution work for tracking changes to workflows and tooling?▼

Memory evolution for tracking workflow and tooling changes works by storing metadata in YAML frontmatter and maintaining an evolution history within the centralized memory store. This allows updates to existing memories while preserving their historical context.

Do I need any specific dependencies to manage architecture decisions with this approach?▼

No specific dependencies are required to manage architecture decisions. The skill operates independently using internal scripts and references to handle the creation, updating, and indexing of memories in the centralized store.

When should I not use a centralized memory store for project knowledge?▼

You should avoid a centralized memory store for transient or non-reusable project knowledge. The approach is designed specifically to preserve high-leverage, durable rules and decisions across multiple sessions and project reviews.