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Memory Leak Debugging

OrganizationPopular
ChromeDevTools
memory-leak-debugging

Diagnoses and resolves memory leaks in JavaScript/Node.js applications. Use when a user reports high memory usage, OOM errors, or wants to capture, compare, or inspect heap snapshots with Chrome DevTools MCP memory tools.

Overview

PublisherChromeDevTools
Repositorychrome-devtools-mcp
Skill namememory-leak-debugging
Stars
52.3K
Forks
4.2K
Bundled files
1
LicenseApache-2.0
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 ChromeDevTools on GitHub. Read the source before you install it.

Installation

Install the Memory Leak Debugging 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/ChromeDevTools/chrome-devtools-mcp.git /tmp/chrome-devtools-mcp
mkdir -p .claude/skills
cp -r /tmp/chrome-devtools-mcp/skills/memory-leak-debugging .claude/skills/memory-leak-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Memory Leak Debugging 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 Leak Debugging 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 Leak Debugging 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 Leak Debugging

This skill provides expert guidance and workflows for finding, diagnosing, and fixing memory leaks in JavaScript and Node.js applications using Chrome DevTools MCP tools.

Prerequisites

Advanced memory debugging tools (compare_heapsnapshots, get_heapsnapshot_details, etc.) are only available when the server is started with the --memoryDebugging flag. First check if these tools are available; if not, try to read the MCP configuration file to check if --memoryDebugging is enabled.

Core Principles

  • Prefer MCP memory tools: Do NOT attempt to read raw .heapsnapshot files directly, as they are extremely large and will consume too many tokens. Use the Chrome DevTools MCP heap snapshot tools to summarize, compare, and inspect snapshots.
  • Isolate the Leak: Determine if the leak is in the browser (client-side) or Node.js (server-side).
  • Common Culprits: Look for detached DOM nodes, unhandled closures, global variables, event listeners not being removed, and caches growing unbounded. Note: Detached DOM nodes are sometimes intentional caches; always ask the user before nulling them.
  • Close Loaded Snapshots: Heap snapshots can be large. After completing an investigation, use close_heapsnapshot for each loaded snapshot to release memory held by the MCP server.

Workflows

1. Capturing Snapshots

When investigating a frontend web application memory leak, utilize the chrome-devtools-mcp tools to interact with the application and take snapshots.

  • Use page-scoped tools like click, navigate_page, fill, etc. (specifying pageId) to manipulate the page into the desired state.
  • Revert the page back to the original state after interactions to see if memory is released.
  • Repeat the same user interactions 10 times to amplify the leak.
  • Use take_heapsnapshot (with pageId) to save .heapsnapshot files to disk at baseline, target (after actions), and final (after reverting actions) states.

2. Comparing Snapshots

Once you have generated .heapsnapshot files using take_heapsnapshot, compare them with Chrome DevTools MCP memory tools.

  • Start with get_heapsnapshot_summary for each snapshot to confirm that the files load and to compare high-level totals.
  • Use compare_heapsnapshots to compare baseline and target snapshots. Start without classIndex for the summary diff, then request detailed class diffs only for suspicious growth by specifying classIndex.
  • Use the summary output from compare_heapsnapshots before drilling into specific node IDs.

3. Inspecting Retainers and Dominator Chains

When a class or object type grows unexpectedly, inspect the retaining chain and dominators with the MCP tools before changing code.

  • Use get_heapsnapshot_class_nodes to list instances of the suspicious class.
  • Use get_heapsnapshot_retainers, get_heapsnapshot_retaining_paths, get_heapsnapshot_dominators, and get_heapsnapshot_edges to understand why representative nodes are still reachable.
  • Use get_heapsnapshot_object_details with a specific nodeId to retrieve detailed object metadata (size, type, distance, and DOM detachedness).
  • Use get_heapsnapshot_duplicate_strings when string growth dominates the diff.
  • Read references/common-leaks.md for examples of common memory leaks and how to fix them after the retaining path points at application code.

4. Advanced Analysis and Categorized Filters

Use built-in MCP memory tools and filters to pinpoint specific leak categories directly without external tools.

  • Use get_heapsnapshot_details or get_heapsnapshot_class_nodes with filterName to target common leak causes:
    • objectsRetainedByDetachedDomNodes: Identifies detached DOM elements retained in memory.
    • objectsRetainedByEventHandlers: Identifies objects kept alive by unremoved event listeners.
    • objectsRetainedByContexts: Identifies objects trapped in closures or execution contexts.
    • objectsRetainedByConsole: Identifies objects retained by console logging.

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 Leak Debugging AI skill do?

Diagnoses and resolves memory leaks in JavaScript/Node.js applications. Use when a user reports high memory usage, OOM errors, or wants to capture, compare, or inspect heap snapshots with Chrome DevTools MCP memory tools.

Why use Memory Leak Debugging on TypingMind?

Because you install it once and use it with any model. Memory Leak Debugging 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 Leak Debugging in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/ChromeDevTools/chrome-devtools-mcp/tree/main/skills/memory-leak-debugging. 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 Leak Debugging?

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 Leak Debugging?

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

Is the Memory Leak Debugging AI skill free?

Yes. It is published on GitHub by ChromeDevTools under the Apache-2.0 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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