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Omh App Debugging

CommunityPopular
rlaope
omh-app-debugging

[omh] Application code misbehaves -- a wrong value, a flaky test, a lost update: reproduce it first, form competing hypotheses, discriminate them with the cheapest observation, and only then fix the demonstrated root cause. Use when the user says: app-debugging, app debugging, application debugging, root cause, root-cause, find the root cause, root cause analysis, flaky test.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-app-debugging
Stars
3.6K
Forks
269
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 App 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.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-app-debugging
  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-app-debugging .claude/skills/omh-app-debugging
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh App 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 App 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 App 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.

App Debugging

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

Why This Exists

app-debugging exists because a wrong result in application code had no owner: native-debugging covers native binaries, build-failure-triage covers red builds, and agent-debug covers agent misbehaviour, so the most common debugging request dispatched straight to an execution lane that skipped the diagnosis.

First Steps

  • Ask for, or plan, the one command that shows the wrong behaviour, and record its observed output before naming a cause.
  • Refuse to prepare a fix while the reproduction reads not_observed; plan the observation that would establish it instead.

Do Not Use When

  • The fault is a crash, memory corruption, or core dump in a compiled native binary that needs a debugger session; use omh-native-debugging.
  • The build, compile, or CI job fails the same way on every run; use omh-build-failure-triage.
  • The subject is an agent or workflow run that misbehaved rather than the application code; use omh-agent-debug.
  • The cause is already demonstrated and the request is to judge whether the fix is proven; use omh-verification-gate.

Examples

Good example:

  • Prompt: a test fails one run in five in CI, how do I find out why
  • Expected behavior: Prepare reproduction_record/v1 with the loop that measures the failure rate, competing_hypotheses/v1 across ordering, shared state, timing, and environment, and the cheapest observation that splits them; no fix yet.
  • Why: The failure is intermittent, so the first deliverable is a measured reproduction rather than a patch.

Bad example:

  • Prompt: add a sleep before the assertion so the flaky test passes
  • Expected behavior: Record the sleep as a symptom mask, keep root cause open, and plan the observation that names the race.
  • Why: A timing change that hides the failure leaves the fault in place and removes the reproduction.

Completion Checklist

  • The reproduction names its command, observed output, expected output, and hit rate, and reads observed before any fix is prepared.
  • At least three hypotheses on distinct axes were written before the first observation was chosen.
  • Each observation records which hypotheses its result eliminated.
  • The root cause cites the demonstrating observation and the observation that ruled out each rival.
  • The fix handoff carries the reproduction as a regression test that fails before and passes after.

Recovery Notes

  • If the fault does not reproduce, make reproduction the first hypothesis and plan the loop, seed, or ordering that would establish it.
  • If every hypothesis is eliminated, record that, widen the axes, and keep root cause unclaimed rather than promoting the last survivor.

When to Use

Use when application code -- a Python, TypeScript, Go, or JVM service, library, or test -- behaves wrongly and the cause is unknown: a wrong value, an intermittent or order-dependent test, a race or lost update, or a bug that moves when observed. The work is a demonstrated root cause: an observed reproduction, competing hypotheses, the cheapest observation that separates them, and only then a fix.

Strong routing signals: `app-debugging`, `app debugging`, `application debugging`, `root cause`, `root-cause`, `find the root cause`, `root cause analysis`, `flaky test`, `flaky tests`, `test is flaky`, `intermittent test failure`, `fails intermittently`, `fails one run in`, `passes locally but fails in ci`, `heisenbug`, `bug disappears`, `disappears when i add a print`, `race condition`, `lost update`, `update is lost`, `wrong return value`, `returns the wrong value`, `reproduce the bug`, `minimal reproduction`

Catalog Metadata

Category: verification Phase: app-root-cause Quality tier: root-cause-evidence-gated Reasoning demand: standard

Quality bar:

  • State the symptom and the expected behaviour separately from any suspected cause.
  • Record the reproduction with its hit rate; an intermittent fault is reproduced when its rate over N runs is measured, not when it happened once.
  • Load references/hypothesis-and-race-method.md for the hypothesis table, flaky-test tactics, and race patterns instead of improvising them.
  • Pick the next observation by cost and by how many hypotheses its result eliminates, and record the eliminations.
  • Keep reproduction, root cause, fix, and verification as separate observed states.
  • Count a fix as failed when an executed run of the same reproduction command still shows the original symptom or shows a new symptom the fix caused rather than unmasked; planned runs and other commands do not count. After the third failed fix, prepare no fourth; re-examine the architecture assumptions every fix shared, per the method reference, and offer the user the next step as a question.

Required inputs:

  • the wrong behaviour as observed, and the behaviour that was expected
  • the command, request, or test that shows it, and how often it shows it
  • language, framework, and what changed recently when known
  • logs, stack traces, or failing assertions already captured
  • observed reproduction and verification evidence for any root-cause or fix claim

Expected outputs:

  • reproduction_record/v1
  • competing_hypotheses/v1 with at least three hypotheses on distinct axes
  • discriminating_observation_plan/v1
  • root_cause_record/v1
  • fix_handoff/v1 blocked until reproduction_record/v1 reads observed
  • observed_fix_verification/v1 when observed

Artifact expectations:

  • reproduction_record/v1 names the exact command, the input, the observed output, the expected output, and the hit rate over N runs; it reads observed or not_observed and nothing between
  • competing_hypotheses/v1 spans distinct axes -- input and state, ordering and timing, environment and build, dependency behaviour -- rather than three phrasings of one guess
  • discriminating_observation_plan/v1 orders the observations cheapest first and names, for each, which hypotheses its result eliminates
  • root_cause_record/v1 cites the observation that demonstrated the cause and the one that ruled out each rival
  • fix_handoff/v1 carries the reproduction as the regression test that must fail before the fix and pass after it

Safety rules:

  • No fix before an observed reproduction: fix_handoff/v1 stays blocked while reproduction_record/v1 reads not_observed, and a proposed change without one is a guess, not a fix.
  • Do not claim a reproduction, a probe result, a root cause, or a passing fix from a prepared plan.
  • Require at least three hypotheses on distinct axes before planning observations; one hypothesis makes every reading confirmatory.
  • Never treat a symptom's disappearance as a root cause; a bug that stops after a print statement, a retry, or a sleep is an open timing fault.
  • Do not execute tests, debuggers, or commands from OMH core; the executor runs them and the record takes only what it observed.

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. Record actual tool results, or not_observed / not_available, in the record; 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. Reply in the user's own words and the host's own voice: the host's persona owns reply language, tone, speech level, and sentence endings, progress updates included (where it sets no language, use the one the user wrote in), and OMH shapes structure and content only; OMH's record terms (surface, lane, wrapper, handoff, evidence boundary, not_observed) stay in records and tool calls, never in the sentence the user reads unless they ask about one; and when a real unresolved user decision or a useful briefing discussion remains, offer one specific, content-linked question. Completed work needs no question; omit unrelated non-action footers and generic offers to continue. 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 App Debugging AI skill do?

[omh] Application code misbehaves -- a wrong value, a flaky test, a lost update: reproduce it first, form competing hypotheses, discriminate them with the cheapest observation, and only then fix the demonstrated root cause. Use when the user says: app-debugging, app debugging, application debugging, root cause, root-cause, find the root cause, root cause analysis, flaky test.

Why use Omh App Debugging on TypingMind?

Because you install it once and use it with any model. Omh App 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 App 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-app-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 App 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 App Debugging?

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

Is the Omh App 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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