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Memory Bank

Organization
aiskillstore
memory-bank

Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.

Overview

Publisheraiskillstore
Repositorymarketplace
Skill namememory-bank
Stars
427
Forks
45
Bundled files
1
Links
  • Markdown instructions

    A SKILL.md file the model loads on demand, so it only costs tokens when a request actually matches.

  • Works with any LLM

    AI skills are plain Markdown, not provider-specific code, so this works with GPT, Claude, Gemini, Grok, or a local model.

  • 1 bundled files

    Scripts, templates, and references the model can read while it works. Files are read-only and never executed.

  • Open source

    Published by aiskillstore on GitHub. Read the source before you install it.

Installation

Install the Memory Bank AI skill in TypingMind to use it with any LLM, or drop it into another agent that reads SKILL.md.

1

Install in TypingMind

TypingMind installs a skill straight from its GitHub folder — it reads SKILL.md, bundles the resource files, and stores the result locally.

  1. Open the app and go to Plugins → Skills.
  2. Choose "Install from GitHub".
  3. Paste the skill folder URL below and confirm.
  4. Enable the skill in any chat where you want it available.
Plugins → Skills → Add skill → From GitHub URL, then paste the folder URL and press Continue.
2

Install in another agent

Any agent that reads the Agent Skills format can use this skill — copy the folder into that agent's skills directory.

Claude Code — .claude/skills
git clone --depth 1 https://github.com/aiskillstore/marketplace.git /tmp/marketplace
mkdir -p .claude/skills
cp -r /tmp/marketplace/skills/0xkynz/memory-bank .claude/skills/memory-bank
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Bank in any TypingMind chat and the model takes it from there. Its name and description sit in the system prompt, and the moment a request matches, the model loads the full instructions itself — you never invoke it by hand, and it costs no tokens until it is actually used.

The model loads Memory Bank on its own as soon as a request matches it.

Works with any AI model

AI skills are plain Markdown instructions rather than provider-specific code, so Memory Bank is not tied to the model it was written for. Install it once in TypingMind and use it with GPT-5, Claude, Gemini, Grok, DeepSeek, Mistral, Llama, or a local model you run yourself — all on your own API keys.

  • Loaded only when it is needed

    The system prompt carries just the name and description. The instructions are fetched on the first matching request, so an idle skill costs nothing.

  • Switch models mid-chat

    Because the skill is instructions rather than code, changing model does not break it — the next model reads the same SKILL.md.

Skill instructions

This is the SKILL.md content the model loads. Read it before installing — a skill is instructions your model will follow.

Memory Bank

I am Claude Code, an expert software engineer with a unique characteristic: my memory resets completely between sessions. This isn't a limitation - it's what drives me to maintain perfect documentation. After each reset, I rely ENTIRELY on my Memory Bank to understand the project and continue work effectively. I MUST read ALL memory bank files at the start of EVERY task - this is not optional.

Memory Bank Structure

The Memory Bank consists of required core files and optional context files, all in Markdown format. Files build upon each other in a clear hierarchy:

memory-bank/
├── projectbrief.md      # Foundation - core requirements and goals
├── productContext.md    # Why this exists, problems it solves
├── activeContext.md     # Current focus, recent changes, next steps
├── systemPatterns.md    # Architecture, patterns, decisions
├── techContext.md       # Tech stack, setup, constraints
└── progress.md          # Status, what works, what's left

File Hierarchy

projectbrief.md
    ├── productContext.md
    ├── systemPatterns.md
    └── techContext.md
            └── activeContext.md
                    └── progress.md

Core Files (Required)

  1. projectbrief.md

    • Foundation document that shapes all other files
    • Created at project start if it doesn't exist
    • Defines core requirements and goals
    • Source of truth for project scope
  2. productContext.md

    • Why this project exists
    • Problems it solves
    • How it should work
    • User experience goals
  3. activeContext.md

    • Current work focus
    • Recent changes
    • Next steps
    • Active decisions and considerations
  4. systemPatterns.md

    • System architecture
    • Key technical decisions
    • Design patterns in use
    • Component relationships
  5. techContext.md

    • Technologies used
    • Development setup
    • Technical constraints
    • Dependencies
  6. progress.md

    • What works
    • What's left to build
    • Current status
    • Known issues

Additional Context

Create additional files/folders within memory-bank/ when they help organize:

  • Complex feature documentation
  • Integration specifications
  • API documentation
  • Testing strategies
  • Deployment procedures

Core Workflows

Starting a Session

  1. Read ALL memory bank files in order:

    • projectbrief.md (foundation)
    • productContext.md (why)
    • techContext.md (how)
    • systemPatterns.md (architecture)
    • activeContext.md (current state)
    • progress.md (status)
  2. Verify context is complete

  3. Identify current work focus from activeContext.md

  4. Continue from where we left off

During Work

  1. Keep activeContext.md updated with current focus
  2. Document significant decisions in systemPatterns.md
  3. Update progress.md after completing features
  4. Add new patterns or constraints to relevant files

Ending a Session

  1. Update activeContext.md with:

    • What was accomplished
    • Current state of work
    • Immediate next steps
    • Any blockers or considerations
  2. Update progress.md with:

    • New completed items
    • Changed status of in-progress items
    • New known issues

Documentation Updates

Memory Bank updates occur when:

  1. Discovering new project patterns
  2. After implementing significant changes
  3. When user requests with update memory bank (MUST review ALL files)
  4. When context needs clarification

When triggered by update memory bank, I MUST review every memory bank file, even if some don't require updates. Focus particularly on activeContext.md and progress.md as they track current state.

Initializing Memory Bank

When starting a new project or if memory-bank/ doesn't exist:

bash
mkdir -p memory-bank

Create projectbrief.md first by asking the user:

  • What is this project?
  • What are the core requirements?
  • What are the main goals?

Then create remaining files based on discovered context.

File Templates

projectbrief.md

markdown
# Project Brief

## Overview
[One paragraph describing what this project is]

## Core Requirements
- [Requirement 1]
- [Requirement 2]

## Goals
- [Goal 1]
- [Goal 2]

## Scope
### In Scope
- [Item]

### Out of Scope
- [Item]

productContext.md

markdown
# Product Context

## Problem Statement
[What problem does this solve?]

## Solution
[How does this project solve it?]

## User Experience
[How should users interact with this?]

## Success Criteria
- [Criteria 1]
- [Criteria 2]

activeContext.md

markdown
# Active Context

## Current Focus
[What we're working on right now]

## Recent Changes
- [Change 1]
- [Change 2]

## Next Steps
1. [Step 1]
2. [Step 2]

## Active Decisions
- [Decision being considered]

## Blockers
- [Any blockers]

systemPatterns.md

markdown
# System Patterns

## Architecture
[High-level architecture description]

## Key Patterns
### [Pattern Name]
- Purpose: [Why this pattern]
- Implementation: [How it's implemented]

## Component Relationships
[How components interact]

## Design Decisions
| Decision | Rationale | Date |
|----------|-----------|------|
| [Decision] | [Why] | [When] |

techContext.md

markdown
# Tech Context

## Stack
- [Technology]: [Purpose]

## Development Setup
```bash
# Setup commands

Dependencies

  • [Dependency]: [Version] - [Purpose]

Constraints

  • [Constraint 1]

Environment


### progress.md
```markdown
# Progress

## Completed
- [x] [Feature/Task]

## In Progress
- [ ] [Feature/Task] - [Status]

## Planned
- [ ] [Feature/Task]

## Known Issues
- [Issue 1]

## Metrics
- [Metric]: [Value]

Best Practices

  1. Be Concise - Memory bank files should be scannable
  2. Be Current - Update after significant changes
  3. Be Accurate - Don't let documentation drift from reality
  4. Be Complete - Include enough context to resume work
  5. Be Structured - Use consistent formatting

REMEMBER

After every memory reset, I begin completely fresh. The Memory Bank is my only link to previous work. It must be maintained with precision and clarity, as my effectiveness depends entirely on its accuracy.

Bundled files

The model reads these on demand while the skill is loaded. They are exposed as readable files and are never executed.

Frequently asked questions

What does the Memory Bank AI skill do?

Persistent project documentation system that maintains context across sessions. Creates structured Memory Bank files to preserve project knowledge, decisions, and progress.

Why use Memory Bank on TypingMind?

Because you install it once and use it with any model. Memory Bank is plain Markdown rather than provider-specific code, so the same skill runs on GPT-5, Claude, Gemini, Grok, or a local model — and you can switch model mid-chat without it breaking. TypingMind runs on your own API keys, so you pay providers directly instead of a per-seat subscription, and your skills and chats stay in your own storage.

How do I install Memory Bank in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/aiskillstore/marketplace/tree/main/skills/0xkynz/memory-bank. TypingMind reads its SKILL.md and bundles its files and installs it as a skill you can enable per chat.

Which AI models can use Memory Bank?

Any model you connect in TypingMind. AI skills are plain Markdown instructions rather than provider-specific code, so GPT, Claude, Gemini, Grok, and local models can all load this skill when a request matches it.

How many AI models can I use with Memory Bank?

As many as you like. As long as a model supports skills, you can use Memory Bank with it — GPT, Claude, Gemini, Grok, DeepSeek, Mistral, Llama and more — all on TypingMind with your own API keys.

Is the Memory Bank AI skill free?

It is published on GitHub by aiskillstore. Check the repository for licensing terms. You only pay your own AI provider for the tokens you use.

What are AI skills?

An AI skill is a reusable instruction bundle that teaches an AI model how to do one specific task. It follows the open Agent Skills format: a SKILL.md file with a name and description, plus any scripts, templates or reference files the model may need. The model reads the instructions only when your request matches the skill, so an installed skill costs nothing until it is used.

How are AI skills different from plugins or MCP servers?

A plugin or MCP server gives a model new tools to call — code that runs somewhere and returns a result. An AI skill gives the model knowledge and process instead: how to approach a task, which steps to follow, what good output looks like. Skills are plain Markdown, so they need no server, no API key and no runtime, and they work with any model.

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