memory-init

name	memory-init
description	Initialize the ConKeeper memory system for the current project. Creates the directory structure and starter files for persistent AI context.

Memory Initialization

Initialize ConKeeper's file-based memory system for this project.

Pre-flight Checks

1. Confirm working directory is a project root (has package.json, Cargo.toml, pyproject.toml, go.mod, or similar)
2. Check if .ai/memory/ already exists
   • If yes: Ask user if they want to reset or review current memory
   • If no: Proceed with initialization

Initialization Steps

Step 1: Create Directory Structure

mkdir -p .ai/memory/decisions
mkdir -p .ai/memory/sessions

Step 2: Gather Project Context

Ask user (or infer from codebase):

• What is this project? (1-2 sentences)
• What's the primary tech stack?
• Any key architectural decisions already made?
• What are you working on right now?

Step 3: Create Initial Files

product-context.md - Populate with gathered info:

# Product Context

## Project Overview
<!-- What is this project? What problem does it solve? -->

## Architecture
<!-- High-level architecture description -->

## Key Stakeholders
<!-- Who uses this? Who maintains it? -->

## Constraints
<!-- Technical, business, or regulatory constraints -->

## Non-Goals
<!-- What this project explicitly doesn't do -->

---
*Last updated: [date] by Cursor*

active-context.md - Set current focus:

# Active Context

## Current Focus
<!-- What are we working on right now? -->

## Recent Decisions
<!-- Decisions made in recent sessions -->

## Open Questions
<!-- Unresolved questions that need answers -->

## Blockers
<!-- What's preventing progress? -->

---
*Session: [date]*

progress.md - Initialize with known tasks:

# Progress Tracker

## In Progress
- [ ] [Initial task if known]

## Completed (Recent)
<!-- Recently completed items -->

## Backlog
<!-- Future tasks -->

---
*Last updated: [date]*

patterns.md - Document code patterns:

# Project Patterns

## Code Conventions
<!-- Coding standards and conventions -->

## Architecture Patterns
<!-- Recurring architectural patterns -->

## Testing Patterns
<!-- Testing conventions -->

---
*Last updated: [date]*

glossary.md - Project terminology:

# Project Glossary

## Terms
| Term | Definition |
|------|------------|
<!-- Add project-specific terminology -->

## Abbreviations
| Abbrev | Expansion |
|--------|-----------|
<!-- Add common abbreviations -->

---
*Last updated: [date]*

Step 4: Configure Token Budget

Ask user:

What token budget preset would you like?

1. Economy (~2000 tokens): Minimal context, fast loading
2. Light (~3000 tokens): Smaller projects, lighter footprint
3. Standard (~4000 tokens): Balanced for most projects (default)
4. Detailed (~6000 tokens): Comprehensive context, rich handoffs

Create .ai/memory/.memory-config.md with their choice:

---
token_budget: standard
---

If user accepts default (Standard), this file can be omitted.

Step 5: Git Handling

Ask user:

Should .ai/memory/ be tracked in git?

• Yes: Memory persists with repo (recommended for solo projects)
• No: Add to .gitignore (recommended for shared repos)

If no:

grep -qxF '.ai/memory/' .gitignore 2>/dev/null || echo '.ai/memory/' >> .gitignore

Step 6: Confirm

Output summary:

Memory initialized for [project-name]

• Product context: [summary]
• Current focus: [focus]
• Token budget: [economy/light/standard/detailed]
• Git tracking: [yes/no]

Use memory-sync to update memory as you work (Tier A auto-apply vs Tier B ADRs — see skill and .ai/memory/README.md).