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ai-memory

Manage persistent memory files and session context for AI-assisted development.

Updated Dec 9, 2025
Search Tags:#automation#workflow#markdown#memory#session-management#team-collaboration#ai-context
One-click install
npx skills add https://github.com/caseproof/stripe-cli-demo --skill ai-memory
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ai-memory
Source: https://github.com/caseproof/stripe-cli-demo/tree/main/.claude/skills/ai-memory
Command: npx skills add https://github.com/caseproof/stripe-cli-demo --skill ai-memory

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

AI agents and developers often lose context between sessions, leading to repetitive explanations and misaligned work. This skill provides a persistent memory system that captures progress, decisions, and learnings in a centralized, human-readable format.

Core Features & Use Cases

  • Persistent memory set: Tracks STATUS.md, ROADMAP.md, DECISIONS.md, JOURNAL.md, and CLAUDE.md to maintain context across sessions.
  • Structured collaboration: Logs decisions and learnings to support team alignment and onboarding.
  • Seamless AI Fridays: Enables starting a new session with full context and minimal setup.

Quick Start

  • To initialize memory files: /memory-init
  • To begin a session and load context: /memory-start
  • To update the current status after work: /memory-update
  • To log a major decision: /memory-decision
  • To capture a learning: /memory-learn
  • To update the roadmap: /memory-roadmap
  • To generate a session summary: /memory-summary

Frequently Asked Questions about ai-memory

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

FAQPage Schema
How do I keep AI context alive across development sessions?▼

Persistent memory management captures and maintains context across sessions by storing progress, decisions, and learnings in structured markdown files (STATUS.md, ROADMAP.md, DECISIONS.md, JOURNAL.md, CLAUDE.md). Initialize with /memory-init, then start each session with /memory-start to load full context automatically.

What's the best way to avoid losing context when working with AI on long-term projects?▼

Session-context summarization paired with automated memory file maintenance prevents context loss by logging status updates, decisions, and learnings in human-readable format. Use /memory-update after work and /memory-summary to generate session overviews that persist across disconnects.

How do I structure team collaboration and onboarding for AI-assisted development workflows?▼

Structured collaboration logs decisions and learnings in centralized memory files, enabling new team members to onboard with full project context. Decision logging (/memory-decision) and learning capture (/memory-learn) ensure alignment and reduce repetitive explanations.

Can I use persistent memory management with AI Friday workflows?▼

Yes. Persistent memory is designed specifically for AI Friday workflows and team projects. /memory-start loads complete session context, and /memory-roadmap keeps work aligned across Friday sessions and between-session work.

What files does a persistent memory system create and maintain?▼

A persistent memory system creates and maintains five core files: STATUS.md tracks current progress, ROADMAP.md outlines project direction, DECISIONS.md logs major choices, JOURNAL.md captures session notes, and CLAUDE.md stores AI-specific context and instructions.

How do I capture learnings and decisions so they persist across sessions?▼

Use /memory-decision to log major decisions with context and /memory-learn to capture discoveries and insights. Both commands write to centralized memory files in markdown format, making learnings searchable and accessible in all future sessions.

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