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Rr Debugger

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gadievron
rr-debugger

Deterministic debugging with rr record-replay. Use when debugging crashes, ASAN faults, or when reverse execution is needed. Provides reverse-next, reverse-step, reverse-continue commands and crash trace extraction.

Overview

Publishergadievron
Repositoryraptor
Skill namerr-debugger
Stars
3.9K
Forks
581
Bundled files
2
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.

  • 2 bundled files

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

  • Open source

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

Installation

Install the Rr Debugger 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/gadievron/raptor/tree/main/.claude/skills/crash-analysis/rr-debugger
  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/gadievron/raptor.git /tmp/raptor
mkdir -p .claude/skills
cp -r /tmp/raptor/.claude/skills/crash-analysis/rr-debugger .claude/skills/rr-debugger
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Rr Debugger 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 Rr Debugger 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 Rr Debugger 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.

rr Deterministic Debugger

rr provides deterministic record-replay debugging with full reverse execution capabilities.

Core Workflow

  1. Record: rr record <program> [args]

    Sandbox exemption (documented decision): rr needs ptrace and perf counters, which libexec/raptor-run-sandboxed denies, so recording runs the untrusted binary unsandboxed — behavior observed under earlier sandboxed runs says nothing about what the payload does with this ambient authority. Keep the recorded invocation to exactly the reproduced crash command (never a broader test suite or build), and treat any unexpected network or filesystem activity during recording as a finding in itself. Replay carries no such residual: it re-executes the recorded trace under gdb, not the binary against the host.

  2. Replay: rr replay -- -nx -iex 'set auto-load off' -iex 'set auto-load safe-path /dev/null' (enters gdb interface with reverse execution). The flags after -- go to gdb and disable auto-load with no trusted directory: the recorded binary is untrusted, and a permissive gdb config (e.g. an operator ~/.gdbinit widening auto-load safe-path) would otherwise execute scripts the binary or its build tree plants (.debug_gdb_scripts, *-gdb.py). Same hardening set as scripts/crash_trace.py — use it on EVERY manual replay.

Reverse Execution Commands

All standard gdb commands work, plus reverse variants:

  • reverse-next / rn: Step back over function calls
  • reverse-step / rs: Step back into functions
  • reverse-continue / rc: Continue backward to previous breakpoint
  • reverse-stepi / rsi: Step back one instruction
  • reverse-nexti / rni: Step back over one instruction

Crash Trace Extraction

Regular Crashes

After rr record <crashing-program>:

bash
rr replay -- -nx -iex 'set auto-load off' -iex 'set auto-load safe-path /dev/null'
# In gdb:
reverse-next 100    # Go back 100 steps (adjust N as needed)
# Now step forward to see execution leading to crash:
next
next
...

ASAN Crashes

After rr record <asan-program>:

bash
rr replay -- -nx -iex 'set auto-load off' -iex 'set auto-load safe-path /dev/null'
# In gdb:
bt                  # View stack trace
up                  # Issue "up" commands until last app frame (before ASAN runtime)
break *$pc          # Set breakpoint at that location
reverse-continue    # Go back to last app instruction before ASAN
# Now step forward to see execution leading to fault:
next
next
...

Inspecting Variables and Memory

Standard gdb commands work at any point:

  • print <var>: Print variable value
  • print *<ptr>: Dereference pointer
  • x/<format> <address>: Examine memory
    • x/10xb <addr>: 10 bytes in hex
    • x/s <addr>: String at address
  • info locals: Show local variables
  • info args: Show function arguments

Source vs Assembly View

  • list: Show source code around current location
  • disassemble: Show assembly around current location
  • layout src: TUI source view
  • layout asm: TUI assembly view
  • set disassemble-next-line on: Show assembly with each step

Automation Script

Use scripts/crash_trace.py to automatically extract execution trace before crash.

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 Rr Debugger AI skill do?

Deterministic debugging with rr record-replay. Use when debugging crashes, ASAN faults, or when reverse execution is needed. Provides reverse-next, reverse-step, reverse-continue commands and crash trace extraction.

Why use Rr Debugger on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/gadievron/raptor/tree/main/.claude/skills/crash-analysis/rr-debugger. 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 Rr Debugger?

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 Rr Debugger?

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

Is the Rr Debugger AI skill free?

It is published on GitHub by gadievron. Check the repository for licensing terms. 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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