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Omh Native Debugging

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rlaope
omh-native-debugging

[omh] Hermes native-debugging workflow: prepare hypothesis-driven debugging of native binaries and instruct the executor to drive a DAP debugger instead of printf. Use when the user says: native-debugging, native debugging, native binary, segfault, segmentation fault, core dump, stack corruption, memory corruption.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-native-debugging
Stars
2.7K
Forks
194
Bundled files
1
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.

  • 1 bundled files

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

  • Open source

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

Installation

Install the Omh Native 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/rlaope/oh-my-hermes.git /tmp/oh-my-hermes
mkdir -p .claude/skills
cp -r /tmp/oh-my-hermes/agent-skills/omh-native-debugging .claude/skills/omh-native-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Native 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 Omh Native 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 Omh Native 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.

Native Debugging

This is an OMH native-debugging workflow skill, projected for Agent Skills hosts (Claude Code, Codex, Cursor, opencode, OpenClaw, pi).

Why This Exists

native-debugging closes OMH's zero-coverage low-level domain by preparing a hypothesis-driven, DAP-first debugging plan for native binaries, while OMH itself continues to execute nothing.

Do Not Use When

  • The failure is a build or CI failure rather than a runtime fault in a binary; use build-failure-triage.
  • The subject is an agent or workflow misbehaving rather than a native binary; use agent-debug.
  • The change is Rust source work whose risk is unsafe or UB discipline; use rust.
  • The request is to judge whether a fix is verified rather than to find the fault; use verification-gate.

Examples

Good example:

  • Prompt: This binary segfaults on the third request; help me debug it.
  • Expected behavior: Prepare native_fault_statement/v1, three competing hypotheses with distinguishing observations, and a debugger_session_plan/v1 naming the DAP adapter, breakpoints, and values to read.
  • Why: The request is a runtime fault in a native binary where the plan, not the guess, is what OMH can prepare.

Bad example:

  • Prompt: Add some printfs and tell me it is fixed once the crash stops.
  • Expected behavior: Name the DAP-driven observation plan, and keep reproduction, root cause, and fix as separate not_observed states.
  • Why: A disappearing symptom is not a root cause, and printf-via-rebuild is the fallback rather than the method.

Completion Checklist

  • The fault is stated as an observed symptom with a reproduction command, separate from any assumed cause.
  • At least three hypotheses span distinct axes and each carries its refuting observation.
  • The debugger session plan names the DAP adapter, breakpoints, watchpoints, threads, frames, and values to read.
  • The handoff says the executor drives the debugger and OMH executes nothing.
  • Reproduction, debugger output, root cause, and fix are reported as separate observed or not_observed states.

Recovery Notes

  • If the fault does not reproduce, make reproduction the first hypothesis and plan the observation that would establish it, rather than debugging a fault no one can trigger.
  • If no debug adapter or symbols are available, say so, plan the coarser evidence path, and keep root cause unclaimed instead of upgrading a guess.

Use When

Use when Hermes should prepare low-level debugging of a native binary, crash, or memory fault: competing hypotheses, the distinguishing observation for each, and a DAP-driven evidence plan for the executor.

Strong routing signals: `native-debugging`, `native debugging`, `native binary`, `segfault`, `segmentation fault`, `core dump`, `stack corruption`, `memory corruption`, `heap corruption`, `use after free`, `null pointer dereference`, `stripped binary`, `disassembly`, `lldb`, `gdb`, `dap debugger`, `breakpoint`, `watchpoint`, `backtrace`, `セグメンテーション違反`, `コアダンプ`, `メモリ破壊`, `ヒープ破壊`, `解放後使用`, `逆アセンブル`, `네이티브 디버깅`, `세그폴트`, `코어 덤프`, `메모리 손상`, `역어셈블`, `중단점`, `段错误`, `核心转储`, `内存破坏`, `释放后使用`, `反汇编`

Catalog Metadata

Category: verification Phase: native-debugging Quality tier: native-debug-evidence-gated Reasoning demand: standard

Quality bar:

  • State the fault as an observed symptom with its reproduction command before naming any cause.
  • Load references/native-debug-loop.md and follow its hypothesis, observation, and escalation order rather than improvising a search.
  • Write at least three hypotheses on distinct axes, each with the single observation that would refute it and the exact place to read that observation.
  • Plan the debugger session concretely: adapter, breakpoints, watchpoints, threads, frames, and the values read at each stop — the executor should not have to invent the session.
  • Prefer debugger-observed state over added print statements; a rebuild-and-print loop is the fallback, not the method.
  • Keep reproduction, debugger output, root cause, and fix as separate observed states.

Required inputs:

  • the binary, crash signature, or fault symptom
  • whether source and debug symbols are available
  • platform, architecture, and the reproduction command
  • how reliably the fault reproduces
  • existing crash logs, core dumps, or sanitizer output
  • observed debugger evidence for any resolution claim

Expected outputs:

  • native_fault_statement/v1
  • hypothesis_set/v1 with at least three competing hypotheses
  • distinguishing_observation_plan/v1
  • debugger_session_plan/v1
  • native_debug_handoff/v1
  • observed_debugger_evidence/v1 when observed

Artifact expectations:

  • native_fault_statement/v1 separates the observed symptom from the assumed cause and names the reproduction command
  • hypothesis_set/v1 spans distinct axes — caller-side misuse, callee invariant, memory lifetime, concurrency, build/runtime mismatch — not three phrasings of one guess
  • distinguishing_observation_plan/v1 pairs each hypothesis with the one observation that refutes it, and where to read it
  • debugger_session_plan/v1 names the adapter (lldb or gdb via DAP), the breakpoints and watchpoints, the frames and threads to inspect, and the values to read at each stop
  • native_debug_handoff/v1 states that the executor drives the debugger and OMH executes nothing
  • breakpoint hits, memory and register reads, backtraces, and confirmed reproductions only when observed

Safety rules:

  • Do not claim a reproduction, a breakpoint hit, a read value, a root cause, or a fix from a prepared debugging plan.
  • Instruct the executor to drive a DAP debug adapter — lldb-dap, codelldb, or a gdb adapter — with breakpoints, stepping, and thread and frame inspection, and to reach for print-and-rebuild only when no adapter is available.
  • Require at least three hypotheses on distinct axes before any observation is planned; a single hypothesis makes every reading confirmatory.
  • Never treat a symptom's disappearance as a root cause; an unexplained fix is an open fault.
  • Treat attaching to, patching, or bypassing protections on a binary the user does not own or operate as out of scope.
  • Do not execute binaries, debuggers, or any command from OMH core.

Runtime Evidence

Use the current host's own tools and subagent/task mechanism when available; otherwise run the same lanes sequentially or name the unavailable capability. A prepared plan, handoff, checklist, or skill installation is not execution, review, CI, merge-readiness, or merge evidence. Report actual tool results or not_observed / not_available; never invent dispatch or host accounting. Treat supplied context as advisory, not proof of hidden memory reads or writes. State scope, constraints, verification, and the stop condition before work. Supporting paths are relative to this skill directory; sibling skill paths are relative to its parent. Resolve them from the host-provided skill base directory ({baseDir} on hosts that provide it), never a hardcoded install location. A named workflow not installed here is unavailable, not permission to emulate its host-specific capabilities. Verify through the real surface before done.

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 Omh Native Debugging AI skill do?

[omh] Hermes native-debugging workflow: prepare hypothesis-driven debugging of native binaries and instruct the executor to drive a DAP debugger instead of printf. Use when the user says: native-debugging, native debugging, native binary, segfault, segmentation fault, core dump, stack corruption, memory corruption.

Why use Omh Native Debugging on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-native-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 Omh Native 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 Omh Native Debugging?

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

Is the Omh Native Debugging AI skill free?

Yes. It is published on GitHub by rlaope 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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