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Memory Md Management

Community
giuseppe-trisciuoglio
memory-md-management

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates with user approval.

Overview

Publishergiuseppe-trisciuoglio
Repositorydeveloper-kit
Skill namememory-md-management
Stars
345
Forks
41
Bundled files
3
LicenseMIT
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.

  • 3 bundled files

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

  • Open source

    Published by giuseppe-trisciuoglio on GitHub. Read the source before you install it.

Installation

Install the Memory Md Management 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/giuseppe-trisciuoglio/developer-kit.git /tmp/developer-kit
mkdir -p .claude/skills
cp -r /tmp/developer-kit/plugins/developer-kit-core/skills/memory-md-management .claude/skills/memory-md-management
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Md Management 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 Md Management 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 Md Management 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.md Management

Provides comprehensive project memory file management capabilities including auditing, quality assessment, and targeted improvements. This skill ensures the coding agent has optimal project context by maintaining high-quality documentation files such as CLAUDE.md.

Overview

Project memory files such as CLAUDE.md are the primary mechanism for providing project-specific context to coding agent sessions. This skill manages their complete lifecycle: discovery, quality assessment, reporting, and improvement. It follows a 5-phase workflow that ensures documentation is current, actionable, and concise.

The skill evaluates CLAUDE.md files against standardized quality criteria across 6 dimensions: Commands/Workflows, Architecture Clarity, Non-Obvious Patterns, Conciseness, Currency, and Actionability. Each file receives a score (0-100) and letter grade (A-F) with specific improvement recommendations.

When to Use

Use this skill when:

  • User explicitly asks to "check", "audit", "update", "improve", "fix", or "maintain" CLAUDE.md
  • User mentions "CLAUDE.md quality", "documentation review", or "project memory optimization"
  • A project memory file needs to be created from scratch for a new project
  • User asks about improving Claude's understanding of the codebase
  • Documentation has become stale or outdated
  • Starting work on a new codebase and need to understand existing documentation
  • User presses # during a session to incorporate learnings into a memory file

Trigger phrases: "audit CLAUDE.md", "check documentation quality", "improve project context", "review CLAUDE.md", "validate documentation"

Instructions

Phase 1: Discovery

Find all CLAUDE.md files in the repository:

bash
find . -name "CLAUDE.md" -o -name ".claude.md" -o -name ".claude.local.md" 2>/dev/null | head -50

File Types & Locations:

TypeLocationPurpose
Project root./CLAUDE.mdPrimary project context (checked into git, shared with team)
Local overrides./.claude.local.mdPersonal/local settings (gitignored, not shared)
Global defaults~/.claude/CLAUDE.mdUser-wide defaults across all projects
Package-specific./packages/*/CLAUDE.mdModule-level context in monorepos
SubdirectoryAny nested locationFeature/domain-specific context

Phase 2: Quality Assessment

For each CLAUDE.md file, read references/quality-criteria.md and evaluate against these criteria:

CriterionWeightWhat to Check
Commands/workflows20 ptsAre build/test/deploy commands present and working?
Architecture clarity20 ptsCan Claude understand the codebase structure?
Non-obvious patterns15 ptsAre gotchas and quirks documented?
Conciseness15 ptsIs content dense without filler?
Currency15 ptsDoes it reflect current codebase state?
Actionability15 ptsAre instructions executable and copy-paste ready?

Quality Scores: A (90-100), B (70-89), C (50-69), D (30-49), F (0-29)

Phase 3: Quality Report Output

CRITICAL: Always output the quality report BEFORE making any updates.

Format:

markdown
## CLAUDE.md Quality Report

### Summary
- Files found: X
- Average score: X/100
- Files needing update: X

### File-by-File Assessment
#### 1. ./CLAUDE.md (Project Root)
**Score: XX/100 (Grade: X)**

| Criterion | Score | Notes |
|-----------|-------|-------|
| Commands/workflows | X/20 | ... |
| Architecture clarity | X/20 | ... |
| Non-obvious patterns | X/15 | ... |
| Conciseness | X/15 | ... |
| Currency | X/15 | ... |
| Actionability | X/15 | ... |

**Issues:**
- [List specific problems]

**Recommended additions:**
- [List what should be added]

Phase 4: Targeted Updates

After report output, ask user for confirmation before updating.

Read references/update-guidelines.md for detailed update principles.

Key principles:

  1. Propose targeted additions only - commands, gotchas, patterns, configs
  2. Keep it minimal - avoid obvious info, generic advice, one-off fixes
  3. Show diffs - file, change (diff format), explanation

Diff format:

markdown
### Update: ./CLAUDE.md
**Why:** [Brief explanation of why this helps]

```diff
+ ## Quick Start
+
+ ```bash
+ npm install
+ npm run dev
+ ```

### Phase 5: Apply Updates

After user approval, use Edit tool to apply changes. Preserve existing content structure.

## Examples

### Example 1: Quality Assessment

**User request:** "Check if my CLAUDE.md is good"

**Response:**
~~~markdown
## CLAUDE.md Quality Report

### Summary
- Files found: 1
- Average score: 65/100
- Files needing update: 1

### File-by-File Assessment

#### 1. ./CLAUDE.md
**Score: 65/100 (Grade: C)**

| Criterion | Score | Notes |
|-----------|-------|-------|
| Commands/workflows | 10/20 | Only npm install documented |
| Architecture clarity | 15/20 | Basic tree, missing module relationships |
| Non-obvious patterns | 5/15 | No gotchas documented |
| Conciseness | 15/15 | Concise and dense |
| Currency | 10/15 | Some outdated paths |
| Actionability | 10/15 | Some vague instructions |

**Issues:**
- Missing test command
- Missing build command
- No documentation of auth flow complexity
- References to old src/structure

**Recommended additions:**
- Add Commands section with test/build
- Document auth module gotcha
- Update architecture paths

Example 2: Creating from Scratch

User request: "Create a CLAUDE.md for this React project"

Response:

markdown
I'll create a CLAUDE.md for your React project. Let me first analyze the codebase structure.

[Scans package.json, src/, and key config files]

## CLAUDE.md Quality Report

### Summary
- Files found: 0
- Creating new CLAUDE.md at project root

### Proposed CLAUDE.md Content:

# Project Name

## Commands

| Command | Description |
|---------|-------------|
| `npm install` | Install dependencies |
| `npm run dev` | Start dev server (Vite on port 5173) |
| `npm run build` | Production build |
| `npm run test` | Run Vitest tests |
| `npm run lint` | ESLint check |

## Architecture

src/ ├── components/ # Reusable UI components ├── pages/ # Route pages ├── hooks/ # Custom React hooks ├── lib/ # Utilities and API clients └── main.tsx # Entry point


## Gotchas

- Vite requires files in `public/` for static assets
- State updates in `useEffect` need proper dependency arrays

Shall I create this file?


### Example 3: Update Proposal

**User request:** "Update my CLAUDE.md with what you learned"

**Response:**
```markdown
### Update: ./CLAUDE.md

**Why:** Discovered that tests require database setup which wasn't documented.

```diff
## Testing

+ **Prerequisites:** Start test database first with `docker-compose up -d test-db`
+ `npm run test` - Run all tests
+ `npm run test:unit` - Unit tests only (no DB required)

Also found issue with API_BASE_URL:

diff
## Environment

- `API_BASE_URL` - API endpoint (default: localhost:3000)
+ `API_BASE_URL` - API endpoint (required for production, defaults to http://localhost:3000 in dev)

Apply these changes?


## Best Practices

1. **Always read references before acting**: Quality criteria, templates, and update guidelines contain essential details
2. **Report before editing**: Never modify CLAUDE.md without first presenting a quality report
3. **Preserve existing structure**: When updating, maintain the existing organization and style
4. **Be project-specific**: Only add information specific to this codebase, not generic advice
5. **Verify commands work**: Before suggesting commands, mentally or actually verify they execute correctly
6. **Use progressive disclosure**: Keep SKILL.md lean, reference detailed rubrics in separate files
7. **Score consistently**: Apply the same scoring standards across all files for fair comparison

## Constraints and Warnings

1. **Never modify without approval**: Always get user confirmation before editing CLAUDE.md files
2. **Don't remove content without asking**: If suggesting deletions, explicitly mark them and get approval
3. **Respect `.claude.local.md`**: These are personal settings; never suggest modifying them in shared docs
4. **Avoid generic advice**: Do not add "write good code" type content - focus on project-specific patterns
5. **Keep diffs concise**: Show only the actual changes, not entire file contents
6. **Verify file paths**: Ensure all referenced files exist before documenting them
7. **Score objectively**: Use the rubric consistently; don't inflate scores for incomplete documentation

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 Md Management AI skill do?

Provides comprehensive memory file management capabilities including auditing, quality assessment, and targeted improvements for files such as CLAUDE.md. Use when user asks to check, audit, update, improve, fix, maintain, or validate project memory files. Also triggers for "project memory optimization", "CLAUDE.md quality check", "documentation review", or when a project memory file needs to be created from scratch. This skill scans memory files, evaluates quality against standardized criteria, outputs detailed quality reports with scores and recommendations, then makes targeted updates wit...

Why use Memory Md Management on TypingMind?

Because you install it once and use it with any model. Memory Md Management 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 Md Management in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/giuseppe-trisciuoglio/developer-kit/tree/main/plugins/developer-kit-core/skills/memory-md-management. 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 Md Management?

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 Md Management?

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

Is the Memory Md Management AI skill free?

Yes. It is published on GitHub by giuseppe-trisciuoglio under the MIT license. 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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