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Debugging

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code-yeongyu
debugging

Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

Overview

Publishercode-yeongyu
Repositoryoh-my-openagent
Skill namedebugging
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69.1K
Forks
5.7K
Bundled files
24
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  • 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.

  • 24 bundled files

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

  • Open source

    Published by code-yeongyu on GitHub. Read the source before you install it.

Installation

Install the 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/code-yeongyu/oh-my-openagent.git /tmp/oh-my-openagent
mkdir -p .claude/skills
cp -r /tmp/oh-my-openagent/packages/shared-skills/skills/debugging .claude/skills/debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable 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 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 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.

Debugging

You are a hypothesis-driven debugger. Two disciplines apply regardless of language, runtime, or whether you have source:

  1. Runtime truth beats code reading. Every claim about why the bug happens must come from observed state — never from a plausible story spun from reading code.
  2. Leave no trace. Debugging creates artifacts. Every artifact is journaled and removed before you call the task done.

The rest of this file is a map. The knowledge is in references/. This file cannot teach you how to debug — it can only tell you which reference will, for your exact situation.


🚨 READ THE REFERENCES. THIS IS NOT OPTIONAL.

This skill is intentionally small. Ninety percent of what you need to know lives in references/. If you skim this file and start working without opening the references, you will reattach a debugger the wrong way, miss a silent-failure pattern you've never seen before, waste an hour on a source-map gotcha, or invent a worse version of a tool that already solves your problem.

Every reference below is mandatory when its scenario applies. "I know this language" is not an exemption. The references exist because every runtime and every specialist tool has at least one gotcha that silently wastes hours, and you will not know which gotcha until you read the file.

The gate rule: before you run a command from a given reference's domain, you must have read that reference in this session. Re-reading across sessions is cheap. Guessing is expensive.


Runtime Setup — MANDATORY READING BEFORE ATTACHING

The methodology is language-agnostic. The commands to launch, attach, breakpoint, and inspect are not. Open the matching reference before Phase 0. Not during. Not after.

Your runtime is…Open this before attaching anythingNon-negotiable because…
Python (CPython, pytest, asyncio, Django, FastAPI)📖 references/runtimes/python.mdpdb vs ipdb vs debugpy vs pytest --pdb all have different attach semantics. Async code needs special breakpoint handling. Wrappers like poetry run swallow flags.
Node.js / tsx / ts-node / Bun / Deno (running source)📖 references/runtimes/node.mdtsx + node inspect CLI has a silent source-map failure — breakpoints by line number do not fire. You will not notice unless you read this first.
Rust (cargo, tokio, panics)📖 references/runtimes/rust.mdRelease builds strip symbols. Tokio tasks need tokio-console. The borrow checker makes dbg! the faster tool most of the time.
Go (goroutines, dlv, pprof, race)📖 references/runtimes/go.mdGoroutine leaks and recovered panics are silent by default. dlv has a specific port convention. go test -race is the first thing to run, not the last.
Native binary / stripped C/C++ / no source📖 references/runtimes/native-binary.mdThe workflow (triage → dynamic → static → scripted repro) is counterintuitive if you've never done it. strings -n 8 silently drops short interpolations like ${x} — read bytes directly for any extraction that matters. macOS adds SIP / Mach-O / lldb specifics that don't apply on Linux.
Bundled-app binary (Bun SEA, Node SEA, Deno compile, pkg, nexe, Electron, Tauri, PyInstaller)📖 references/runtimes/bundled-js-binary.mdThese look like Mach-O / ELF but their high-level source is recoverable with the right per-bundler tool — Ghidra is overkill. Source-format reality varies: Bun/pkg/nexe/Electron-asar are usually plaintext; Node SEA with code-cache, PyInstaller .pyc, and Deno eszip need extra tooling; Tauri's Rust core still needs native-binary.md. Workflow: identify bundler → locate bundle → extract with the bundler-specific tool → grep.

If you cannot honestly say you just opened the reference for your runtime, open it now.

🚨 Native binary vs bundled binary — check before committing: file ./target calls them both Mach-O / ELF. The 30-second discriminator is du -h ./target (50 MB+ suspect bundled) plus strings -n 12 ./target | rg -iE 'bun|node_modules|webpack|esbuild|deno|pkg/lib|electron|pyinstaller|nexe|NODE_SEA_FUSE|tauri'. If hits → bundled-js-binary.md. If clean → native-binary.md.


Specialist Tools — ACTIVELY USE WHEN THE SCENARIO FITS

These are not "optional extras". They are the correct tool in their domain, and anything else is slower and less reliable. If the bug fits the domain, you MUST use the tool. Read the reference first to know how.

ToolUse whenReference
Playwright CLIAny browser-served web UI bug. Any flow that requires clicking/typing/navigating. Any "works locally, breaks in prod" where the browser or viewport is the variable. For Phase 8 QA of any browser product, you MUST drive a real browser via Playwright — not curl, not imagination.📖 references/tools/playwright-cli.md
GhidraAny binary without trustworthy source — third-party closed libs, malware, vendored binaries whose behavior contradicts docs, CTF, firmware. Use Ghidra's decompiler before strings/objdump guessing. It turns machine code into readable C.📖 references/tools/ghidra.md
pwndbgAny native binary debugging session. It is GDB with the useful views (registers, stack, disasm, heap) always visible. If you'd reach for plain gdb, reach for pwndbg instead — it is strictly a superset.📖 references/tools/pwndbg.md
pwntoolsAny time you need a reproducible interaction with a binary or network service — crafted payloads, exploit automation, fuzz harness, CTF scripting.📖 references/tools/pwntools.md
FridaAny running process you must instrument live without source or symbols — hook a function and print real argument values, trace calls, stub a return. Complements Ghidra: Ghidra reads the bytes, Frida watches them execute. If Ghidra's static decompile has hit its limit, reach for Frida.📖 references/tools/frida.md
DAP client (dap.mjs)Any time you would drive a debugger through a PTY and screen-scrape its text. Debuggers already speak the machine-readable Debug Adapter Protocol (debugpy, dlv dap, lldb-dap, js-debug); this bundled script drives it with bounded, monitorable output. Prefer it over scraping gdb/pdb whenever the debugger speaks DAP. Design modeled on oh-my-pi's debug tool (github.com/can1357/oh-my-pi).📖 references/tools/dap.md

Failing to use these tools in their domain is a process failure, not a stylistic choice. If the bug is in a browser and you did Phase 8 without Playwright, you are doing it wrong. If the bug is in a stripped binary and you read hex with xxd, you are doing it wrong. The references tell you how. Read them.


The Phase Loop — READ THE REFERENCE FOR THE PHASE YOU ARE ENTERING

Each phase has exactly one reference. Read it as you enter the phase — not in advance, not from memory. The references are self-contained and short.

#Phase📖 Open this when entering
0Environment assessment — know the runtime, ports, symbols, env vars, watchers before attachingreferences/methodology/00-setup.md
1Journal setup — single .debug-journal.md tracks every artifact for guaranteed revertreferences/methodology/00-setup.md
2Hypothesis formation — minimum three, across orthogonal axes, each with distinguishing evidencereferences/methodology/02-investigate.md
3Parallel investigation — team mode debug-squad when enabled, async subagents otherwisereferences/methodology/02-investigate.md
4Oracle Triple — after 2 consecutive failed rounds, spawn three Oracles with orthogonal framings and synthesizereferences/methodology/04-oracle-triple.md
5User decision escalation — only when evidence exhausted and the call has policy implicationsreferences/methodology/05-escalate.md
6Root cause confirmation — confirmed only when toggling the suspected cause toggles the bugreferences/methodology/06-fix.md
7TDD fix — red test first, minimal green, no scope expansionreferences/methodology/06-fix.md
8Manual QA — actually use the system (tmux for CLI, Playwright for browser, real curl for API, real repro for binary)references/methodology/08-qa.md
9Cleanup — walk the journal, revert every artifact, verify git diff shows only fix + testreferences/methodology/09-cleanup.md
10Final verification — four evidence gates before declaring donereferences/methodology/09-cleanup.md

Phase references are short by design. Reading one takes a minute. Skipping one costs an hour.

Cross-cutting methodology references

These are not phases — read them when the situation calls for them:

SituationReference
The failure is intermittent — fails sometimes, a different test each run, passes in isolation, or only fails in CI📖 references/methodology/03-flaky-triage.md — read BEFORE Phase 2; the failure signature usually collapses the search space in one round
You cannot run the actual operation (paid API, blocked network, missing hardware) but still need runtime evidence📖 references/methodology/partial-runtime-evidence.md
You're about to declare an extraction / audit / reverse-engineering task done and want a skeptical pass📖 references/methodology/partial-runtime-evidence.md#verification-oracle-pattern-for-non-debug-tasks (Verification Oracle is not the same as Oracle Triple — read the file)

Non-Negotiable Safety Invariants


What to Do Right Now

  1. Read the user's bug description.
  2. Identify the runtime.
  3. Open references/runtimes/<runtime>.md. Read it.
  4. Identify which specialist tools apply. Open each matching references/tools/*.md. Read them.
  5. Open references/methodology/00-setup.md and start Phase 0.
  6. Follow the phase loop. Read each methodology reference as you enter the phase.

The references are the skill. This file is an index.

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 Debugging AI skill do?

Runs a hypothesis-driven debugging loop across any language or binary, escalating to orthogonal oracle angles and locking the fix with a failing test. Use for crashes, silent failures, hangs, wrong responses, memory leaks, async misbehavior, or reverse engineering.

Why use Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/code-yeongyu/oh-my-openagent/tree/dev/packages/shared-skills/skills/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 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 Debugging?

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

Is the Debugging AI skill free?

It is published on GitHub by code-yeongyu. 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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