Windbg User Virtual Memory Exhaustion logo

Windbg User Virtual Memory Exhaustion

Organization
microsoft
windbg-user-virtual-memory-exhaustion

Use when a native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.

Overview

Publishermicrosoft
Repositorywin-dev-skills
Skill namewindbg-user-virtual-memory-exhaustion
Stars
477
Forks
37
Bundled files
Instructions only
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

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

Installation

Install the Windbg User Virtual Memory Exhaustion 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.
    https://github.com/microsoft/win-dev-skills/tree/main/plugins/windbg/skills/windbg-user-virtual-memory-exhaustion
  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/microsoft/win-dev-skills.git /tmp/win-dev-skills
mkdir -p .claude/skills
cp -r /tmp/win-dev-skills/plugins/windbg/skills/windbg-user-virtual-memory-exhaustion .claude/skills/windbg-user-virtual-memory-exhaustion
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Windbg User Virtual Memory Exhaustion 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 Windbg User Virtual Memory Exhaustion 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 Windbg User Virtual Memory Exhaustion 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.

Virtual Memory Exhaustion

Load windbg-diagnostic-method first if it is not already loaded in this conversation, and apply it throughout for evidence ranking, hypothesis testing, confidence calibration, independent review, and report validation. This skill adds the bug-family-specific commands and evidence requirements.

Detection

Investigate E_OUTOFMEMORY (0x8007000E), STATUS_NO_MEMORY (0xC0000017), bad_alloc, or failed heap/virtual allocations in an application, service, or user-mode driver host (including UMDF). Record the actual API, requested size/alignment, architecture, flags, and error. Free physical RAM does not rule out insufficient virtual address space, commit pressure, or applicable limits.

Workflow

1. Examine the process VA map

text
!address -summary
!address
!heap -s

Compare available regions with the attempted allocation's contiguous range and alignment requirements. Distinguish reserved, committed, free, image, mapped, stack, and heap ranges. A large reservation consumes VA without committing its entire range. Heap internals may require more than the user's requested bytes.

Report unavailable pages/map data rather than inferring completeness from a limited dump.

2. Separate pressure mechanisms

MechanismEvidence needed
VA exhaustionProcess address range/architecture and little usable free VA
FragmentationFree space exists, but no suitable contiguous/aligned region
System commit pressureCommit usage/limit near the failure, not just free RAM
Process/job memory limitActual configured limit and relevant usage/accounting
Allocation policy/API failureAPI-specific constraints, flags, or allocator policy

Use contemporaneous system memory counters or an appropriate live/kernel session for system commit data. A process PEB is not a documented MaximumCommit source. A process's committed bytes alone do not establish a system-wide or job limit.

For 32-bit targets inspect /LARGEADDRESSAWARE and the host architecture; 2 GB versus up to 4 GB depends on those settings. Verify current documented limits for the target Windows version and architecture instead of applying one 64-bit limit to every system.

3. Identify dominant consumers

text
!address -f:Heap
!address -f:Stack
!heap -stat -h <heap-address>

Use !address -? and !heap -? for allocator/version-specific support. Classify dominant heaps, stacks, mappings, images, and reserved arenas. Large or numerous allocations are not automatically leaks: map them to cache policy, owners, workload, and expected lifetimes.

4. Establish growth with history

Collect authorized time-series counters, repeated comparable snapshots, or allocation/free tracing across the workload. Compare warm-up with steady-state behavior and verify whether memory is reclaimed when work finishes.

One snapshot plus process uptime cannot prove a leak rate. For a suitable user-mode recording use windbg-user-ttd-reverse-debugging-triage; for damaged allocations use windbg-user-heap-corruption-investigation instead.

Fix patterns

  • Bound caches by an appropriate size/cost policy and verify eviction. Define the lifetime of any references returned before eviction.
  • Match VirtualAlloc reservations with the correct VirtualFree release contract; distinguish decommit from release.
  • Use RAII to release resources on all supported exit/error paths.
  • Reduce large contiguous allocation requirements where the design permits.
  • Do not blindly increase limits or stack reservations before finding the pressure source, and do not convert a failed allocation into a silent success.

Validation

Name the failing allocation and pressure mechanism, identify the consumer with evidence, and exercise the remedy through warm-up, sustained load, and cleanup. Verify memory remains bounded or the intended allocation succeeds under the required constraints. Document configured system/job limits separately.

References

Feedback

Follow FEEDBACK.md and submit reviewed, sanitized feedback to WinDbg-Feedback. Include windbg-user-virtual-memory-exhaustion and the package version from plugin.json; no automatic dump or memory-content upload.

Frequently asked questions

What does the Windbg User Virtual Memory Exhaustion AI skill do?

Use when a native app, service, or user-mode driver host allocation fails; distinguish VA exhaustion, fragmentation, and commit pressure. Not for managed .NET heap growth, proving a leak from one snapshot, or corruption.

Why use Windbg User Virtual Memory Exhaustion on TypingMind?

Because you install it once and use it with any model. Windbg User Virtual Memory Exhaustion 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 Windbg User Virtual Memory Exhaustion in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/microsoft/win-dev-skills/tree/main/plugins/windbg/skills/windbg-user-virtual-memory-exhaustion. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Windbg User Virtual Memory Exhaustion?

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 Windbg User Virtual Memory Exhaustion?

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

Is the Windbg User Virtual Memory Exhaustion AI skill free?

Yes. It is published on GitHub by microsoft 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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