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Tracealyzer

Community
dariushoule
tracealyzer

Trace execution (into or over calls) for N steps or until a condition, then analyze the recorded instruction log

Overview

Publisherdariushoule
Repositoryx64dbg-skills
Skill nametracealyzer
Stars
210
Forks
18
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 dariushoule on GitHub. Read the source before you install it.

Installation

Install the Tracealyzer 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/dariushoule/x64dbg-skills/tree/main/skills/tracealyzer
  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/dariushoule/x64dbg-skills.git /tmp/x64dbg-skills
mkdir -p .claude/skills
cp -r /tmp/x64dbg-skills/skills/tracealyzer .claude/skills/tracealyzer
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Tracealyzer 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 Tracealyzer 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 Tracealyzer 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.

tracealyzer

Trace debuggee execution — stepping into or over calls — for a specified number of instructions or until a condition is met. The full instruction log is captured to a file and then analyzed.

Instructions

Follow these steps exactly:

1. Verify debugger connection

Call mcp__x64dbg__get_debugger_status to confirm the debugger is connected and a debuggee is loaded and paused. If it is running, call mcp__x64dbg__pause. If no debuggee is loaded, tell the user and stop.

2. Gather trace parameters

Ask the user for the following if not already provided:

  • Trace mode: trace into calls or trace over calls (default: over)
  • Stop condition — one of:
    • A maximum number of instructions (e.g. 1000)
    • An x64dbg expression that stops when true (e.g. cip == 0x7FF6A0001000, rax != 0)
    • Both (whichever triggers first)

If the user provides a symbol or address for the stop condition, resolve it with mcp__x64dbg__eval_expression and build the break_condition expression (e.g. cip == <resolved_addr>).

When the user only specifies a step count N and no explicit break condition, use break_condition 0 (never true — the trace runs until max_steps is hit).

3. Capture starting context

Call mcp__x64dbg__get_all_registers and mcp__x64dbg__disassemble at the current instruction pointer to record the starting state. Note the starting address.

4. Run the trace

Prepare the output log path: ./traces/trace_<timestamp>.log (create the traces directory if it doesn't exist via Bash).

Call the appropriate trace tool (mcp__x64dbg__trace_into or mcp__x64dbg__trace_over) with:

ParameterValue
break_conditionThe user's condition, or 0 if only a step count was given
max_stepsThe user's step count, or 50000 if only a condition was given
log_text`{p:cip} {i:cip}
log_fileThe output log path from above
wait_timeoutScale with max_steps — use max(60, max_steps // 500) seconds

5. Read and analyze the trace log

Read the trace log file. The log contains one line per executed instruction in the format:

<address> <disassembly> | Label=<label> Comment=<comment>

Ignore when Labels or Comments say [Formatting Error], it just means there is no label or comment at that instruction.

Analyze the trace and present a summary to the user:

  • Trace overview: total instructions executed, start address → end address, trace mode used
  • Execution flow: describe the high-level behavior — what the code did, which functions were called, loops observed, and notable control-flow patterns
  • Hot spots: addresses or regions that appear most frequently (loops, repeated calls)
  • Key observations: interesting register manipulations, memory accesses, syscalls, API calls, string operations, or anything else that stands out

Use mcp__x64dbg__get_symbol to resolve notable addresses to symbol names where possible.

If the trace log is very large (>2000 lines), read it in chunks and summarize progressively.

6. Follow-up actions

After presenting the summary, ask the user if they would like any follow-up actions such as:

  • Annotate: add comments/labels in x64dbg at key addresses using mcp__x64dbg__set_comment / mcp__x64dbg__set_label
  • Deeper analysis: re-trace a specific sub-region, or focus on a particular function
  • Deobfuscation: identify and explain obfuscated patterns found in the trace
  • Export: the trace log is already saved to disk at the path from step 4

Frequently asked questions

What does the Tracealyzer AI skill do?

Trace execution (into or over calls) for N steps or until a condition, then analyze the recorded instruction log

Why use Tracealyzer on TypingMind?

Because you install it once and use it with any model. Tracealyzer 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 Tracealyzer in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/dariushoule/x64dbg-skills/tree/main/skills/tracealyzer. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Tracealyzer?

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 Tracealyzer?

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

Is the Tracealyzer AI skill free?

Yes. It is published on GitHub by dariushoule 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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