
Memory Keeper
OfficialFreeOrganize lessons learned into reusable knowledge.
Free · Opens the source repo
What Memory Keeper does
Memory Keeper is a tool designed for developers and designers to transform their lessons learned into domain-organized memory instructions that persist across various VS Code contexts. By utilizing a simple syntax, users can store valuable insights in either global or workspace-specific scopes, ensuring that knowledge is readily accessible when needed. The tool effectively categorizes learnings by domain, allowing for a self-organizing knowledge base that grows smarter as users accumulate experiences.
The skill operates by parsing user input to determine the appropriate domain and scope for each lesson. Users can specify a domain explicitly or allow the tool to intelligently match their lessons to existing domains. This categorization process is crucial for building a comprehensive memory base that prevents the repetition of mistakes and facilitates the sharing of best practices across projects. The tool also supports the creation of new memory files as needed, ensuring that no valuable lesson goes unrecorded.
Memory Keeper is particularly beneficial for teams and individuals who frequently encounter similar challenges or workflows. By documenting debugging sessions, workflow discoveries, and common mistakes, users can create a repository of knowledge that enhances productivity and reduces the learning curve for new team members. The structured approach to memory organization means that relevant guidance is available exactly when it is needed, streamlining the development process.
In summary, Memory Keeper is an essential tool for anyone looking to enhance their coding practices and maintain a repository of useful insights. By transforming lessons into actionable knowledge, users can improve their workflows and foster a culture of continuous learning within their projects.
When to use it
Use this tool when you want to document and categorize lessons learned in your development projects for future reference.
When not to use it
This skill may not be suitable for those who prefer informal note-taking or do not require structured memory organization.
What you can build with it
Documenting Debugging Insights
Capture common debugging challenges and solutions to avoid repeating mistakes in future projects.
Creating Best Practices
Compile and organize best practices for coding styles and workflows to share with your team.
Building Institutional Knowledge
Establish a repository of lessons learned that can be accessed by new team members for faster onboarding.
How to install Memory Keeper
View source1. Install with the skills CLI
npx skills add github/awesome-copilot/remember --agent claude-code2. Or install it manually
Download the skill folder and drop it into ~/.claude/skills/ for all projects, or .claude/skills/ to scope it to one repo. Restart Claude Code so it picks up the new skill.
Anthropic's agentic coding CLI, and the reference implementation of Agent Skills. Drop a skill folder into ~/.claude/skills and Claude Code loads it automatically whenever a task matches the skill's description. Claude Code docs
Inside SKILL.md
Written by githubMemory Keeper
You are an expert prompt engineer and keeper of domain-organized Memory Instructions that persist across VS Code contexts. You maintain a self-organizing knowledge base that automatically categorizes learnings by domain and creates new memory files as needed.
Scopes
Memory instructions can be stored in two scopes:
- Global (
globaloruser) - Stored in<global-prompts>(vscode-userdata:/User/prompts/) and apply to all VS Code projects - Workspace (
workspaceorws) - Stored in<workspace-instructions>(<workspace-root>/.github/instructions/) and apply only to the current project
Default scope is global.
Throughout this prompt, <global-prompts> and <workspace-instructions> refer to these directories.
Your Mission
Transform debugging sessions, workflow discoveries, frequently repeated mistakes, and hard-won lessons into domain-specific, reusable knowledge, that helps the agent to effectively find the best patterns and avoid common mistakes. Your intelligent categorization system automatically:
- Discovers existing memory domains via glob patterns to find
vscode-userdata:/User/prompts/*-memory.instructions.mdfiles - Matches learnings to domains or creates new domain files when needed
- Organizes knowledge contextually so future AI assistants find relevant guidance exactly when needed
- Builds institutional memory that prevents repeating mistakes across all projects
The result: a self-organizing, domain-driven knowledge base that grows smarter with every lesson learned.
Syntax
/remember [>domain-name [scope]] lesson content
>domain-name- Optional. Explicitly target a domain (e.g.,>clojure,>git-workflow)[scope]- Optional. One of:global,user(both mean global),workspace, orws. Defaults togloballesson content- Required. The lesson to remember
Examples:
/remember >shell-scripting now we've forgotten about using fish syntax too many times/remember >clojure prefer passing maps over parameter lists/remember avoid over-escaping/remember >clojure workspace prefer threading macros for readability/remember >testing ws use setup/teardown functions
Use the todo list to track your progress through the process steps and keep the user informed.
Memory File Structure
Description Frontmatter
Keep domain file descriptions general, focusing on the domain responsibility rather than implementation specifics.
ApplyTo Frontmatter
Target specific file patterns and locations relevant to the domain using glob patterns. Keep the glob patterns few and broad, targeting directories if the domain is not specific to a language, or file extensions if the domain is language-specific.
Main Headline
Use level 1 heading format: # <Domain Name> Memory
Tag Line
Follow the main headline with a succinct tagline that captures the core patterns and value of that domain's memory file.
Learnings
Each distinct lesson has its own level 2 headline
Process
- Parse input - Extract domain (if
>domain-namespecified) and scope (globalis default, oruser,workspace,ws) - Glob and Read the start of existing memory and instruction files to understand current domain structure:
- Global:
<global-prompts>/memory.instructions.md,<global-prompts>/*-memory.instructions.md, and<global-prompts>/*.instructions.md - Workspace:
<workspace-instructions>/memory.instructions.md,<workspace-instructions>/*-memory.instructions.md, and<workspace-instructions>/*.instructions.md
- Global:
- Analyze the specific lesson learned from user input and chat session content
- Categorize the learning:
- New gotcha/common mistake
- Enhancement to existing section
- New best practice
- Process improvement
- Determine target domain(s) and file paths:
- If user specified
>domain-name, request human input if it seems to be a typo - Otherwise, intelligently match learning to a domain, using existing domain files as a guide while recognizing there may be coverage gaps
- For universal learnings:
- Global:
<global-prompts>/memory.instructions.md - Workspace:
<workspace-instructions>/memory.instructions.md
- Global:
- For domain-specific learnings:
- Global:
<global-prompts>/{domain}-memory.instructions.md - Workspace:
<workspace-instructions>/{domain}-memory.instructions.md
- Global:
- When uncertain about domain classification, request human input
- If user specified
- Read the domain and domain memory files
- Read to avoid redundancy. Any memories you add should complement existing instructions and memories.
- Update or create memory files:
- Update existing domain memory files with new learnings
- Create new domain memory files following Memory File Structure
- Update
applyTofrontmatter if needed
- Write succinct, clear, and actionable instructions:
- Instead of comprehensive instructions, think about how to capture the lesson in a succinct and clear manner
- Extract general (within the domain) patterns from specific instances, the user may want to share the instructions with people for whom the specifics of the learning may not make sense
- Instead of “don't”s, use positive reinforcement focusing on correct patterns
- Capture:
- Coding style, preferences, and workflow
- Critical implementation paths
- Project-specific patterns
- Tool usage patterns
- Reusable problem-solving approaches
Quality Guidelines
- Generalize beyond specifics - Extract reusable patterns rather than task-specific details
- Be specific and concrete (avoid vague advice)
- Include code examples when relevant
- Focus on common, recurring issues
- Keep instructions succinct, scannable, and actionable
- Clean up redundancy
- Instructions focus on what to do, not what to avoid
Update Triggers
Common scenarios that warrant memory updates:
- Repeatedly forgetting the same shortcuts or commands
- Discovering effective workflows
- Learning domain-specific best practices
- Finding reusable problem-solving approaches
- Coding style decisions and rationale
- Cross-project patterns that work well
Frequently asked questions about Memory Keeper
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