Windbg User Wait Chain Analysis logo

Windbg User Wait Chain Analysis

Organization
microsoft
windbg-user-wait-chain-analysis

Use when an app, service, or user-mode driver host is unresponsive on locks, COM/RPC, I/O, or another process; follow the blocker chain. Not for a crash solely from an exception stack.

Overview

Publishermicrosoft
Repositorywin-dev-skills
Skill namewindbg-user-wait-chain-analysis
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 Wait Chain Analysis 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-wait-chain-analysis
  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-wait-chain-analysis .claude/skills/windbg-user-wait-chain-analysis
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Windbg User Wait Chain Analysis 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 Wait Chain Analysis 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 Wait Chain Analysis 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.

Cross-Process Wait Chain Analysis

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

Look for blocked work in an application, service, or user-mode driver host (including UMDF), and threads in wait, COM, RPC, or I/O paths. The process displaying the symptom may not own the underlying defect. Idle worker threads waiting normally are not evidence of a hang.

Workflow

1. Identify the affected operation and its thread

text
.lastevent
~*kb
!runaway

Confirm why the dump was collected. A dump is a snapshot; .lastevent does not by itself certify "hang" or "no crash." Identify the UI/serving thread from application evidence instead of assuming thread zero is always the UI thread. If work is spinning rather than blocked, collect appropriate CPU evidence.

2. Identify each wait and its owner

Inspect frames around WaitForSingleObject, WaitForMultipleObjects, COM send/receive, and RPC calls. Use matching symbols/source for your own proxy, interface, and server registration to determine what was requested.

For each edge record:

text
process / thread -> waited resource or request -> owner / serving thread

Do not guess a server PID from undocumented private COM layouts. Use available Wait Chain Traversal, RPC/COM tracing, application correlation IDs, or registration/process information, and explicitly mark unresolved edges.

3. Gather corresponding server evidence

Obtain authorized dumps or live state of the relevant server processes close enough in time to represent the same operation. Inspect their serving threads. For kernel context use documented commands such as:

text
!process 0 7
!thread <ethread>

Available user pages and symbol/context support vary by dump type. If the chain ends in a kernel lock use windbg-kernel-lock-deadlock-triage; if blocked I/O is supported by IRP evidence use windbg-kernel-irp-lifecycle-triage. Do not promise process heaps or user stacks absent from the dump.

4. Distinguish a cycle from a slow or missing responder

  • Cycle: show every required wait/owner edge back to the starting actor.
  • Contention: a runnable owner may eventually release the resource.
  • Starvation: queued work cannot obtain execution capacity.
  • Lost completion: the actor/event expected to unblock the waiter no longer exists or no path signals it.
  • Slow server: the endpoint is doing work, blocked on another dependency, or looping; gather its evidence before blaming the client.

Multiple snapshots or tracing may be needed to distinguish transient waits from persistent blocking.

Fix patterns and COM boundaries

A synchronous COM call from an STA can pump messages and permit reentrancy. It is not automatically a deadlock. Investigate locks held across calls, callbacks, apartment access, and any non-pumping waits that form an actual dependency cycle.

Possible remedies include asynchronous APIs, shorter lock scopes, moving destruction/cross-process calls outside critical sections, and bounded wait/cancellation protocols where the API supports them. A timeout on the caller does not cancel server work by itself.

If work moves to another apartment, marshal apartment-bound interfaces, keep captured state alive, and dispatch UI updates to the correct thread. Merely capturing a COM pointer and this in a worker lambda is not a safe fix.

Validation

Identify the affected thread, relevant resources and owners, and demonstrated cycle or deepest supported blocker. Test the proposed remedy under concurrency, reentrancy, shutdown, and timeout/cancellation. Do not stop at "waiting in RPC." If the server dump or an owner edge is missing, state the unresolved hypothesis.

References

Feedback

Follow FEEDBACK.md and submit only reviewed, sanitized feedback to WinDbg-Feedback. Include windbg-user-wait-chain-analysis and the package version from plugin.json; no automatic capture or public upload of process dumps or full diagnostic transcripts.

Frequently asked questions

What does the Windbg User Wait Chain Analysis AI skill do?

Use when an app, service, or user-mode driver host is unresponsive on locks, COM/RPC, I/O, or another process; follow the blocker chain. Not for a crash solely from an exception stack.

Why use Windbg User Wait Chain Analysis on TypingMind?

Because you install it once and use it with any model. Windbg User Wait Chain Analysis 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 Wait Chain Analysis 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-wait-chain-analysis. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Windbg User Wait Chain Analysis?

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 Wait Chain Analysis?

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

Is the Windbg User Wait Chain Analysis 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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