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Omh Agent Debug

CommunityPopular
rlaope
omh-agent-debug

[omh] Agent Debug workflow: capture a stuck, looping, drifting, or repeatedly failing agent run, diagnose the likely failure pattern, and prepare the smallest safe recovery action. Use when the user says: agent-debug, agent debug, agent debugging, agent introspection, agent self-debug, self-debug, self debugging, looping agent.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-agent-debug
Stars
2.7K
Forks
194
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 rlaope on GitHub. Read the source before you install it.

Installation

Install the Omh Agent Debug 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-agent-debug .claude/skills/omh-agent-debug
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Agent Debug 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 Agent Debug 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 Agent Debug 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.

Agent Debug

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

Why This Exists

agent-debug exists so Hermes users can ask for this workflow in chat and receive a structured, evidence-bounded OMH operating surface instead of ad hoc narration.

Do Not Use When

  • The request is already handled by a narrower explicit skill with stronger evidence.
  • The user asks OMH to secretly run external platforms, connectors, schedulers, file exports, or runtime agents.
  • The only safe answer is to ask for missing authority, credentials, target, or observed evidence first.

Examples

Good example:

  • Prompt: agent-debug capture why this agent is looping on the same tool and prepare the smallest safe recovery action.
  • Expected behavior: Produce prepare_agent_debug with required context, wrapper actions, and not-evidence boundaries.
  • Why: The prompt names a real workflow surface that Hermes can orchestrate without hiding execution.

Bad example:

  • Prompt: agent-debug silently reset the executor, patch the environment, and claim the future loop is fixed.
  • Expected behavior: Report the missing observed evidence or authority instead of claiming the external step happened.
  • Why: Prepared OMH guidance is not platform, runtime, connector, file, memory, or delivery evidence.

Completion Checklist

  • Failure state, intended goal, recent tool sequence, and context pressure are captured.
  • Diagnosis distinguishes repeated command/tool loops, context drift, environment mismatch, service errors, and wrong-hypothesis tests.
  • Recovery action is contained, reversible, and does not claim implementation, verification, CI, merge, or future-loop fixes.

Recovery Notes

  • If the request is install/setup health, route to doctor.
  • If the request is a manager status or throughput review, route to agent-ops-review.
  • If the request is a durable self-improvement record after diagnosis, route to workflow-learning.

Use When

Use when an agent run is stuck, looping on tools, burning tokens without progress, drifting from the objective, losing context, or failing on recoverable environment/tool assumptions.

Strong routing signals: `agent-debug`, `agent debug`, `agent debugging`, `agent introspection`, `agent self-debug`, `self-debug`, `self debugging`, `looping agent`, `agent loop failure`, `agent run stuck`, `agent failure capture`, `tool retry loop`, `repeated tool calls`, `context drift`, `prompt drift`, `token burn`, `에이전트 디버그`, `에이전트 실패`, `에이전트 반복 실패`, `반복 실패`, `도구 반복`, `컨텍스트 드리프트`, `토큰 낭비`

Catalog Metadata

Category: operations Phase: agent-debug Quality tier: workflow-surface-gated Reasoning demand: light

Quality bar:

  • Name the user-facing workflow objective, required context, next action, and stop condition.
  • Separate prepared guidance from observed platform, runtime, connector, file, memory, or delivery evidence.
  • Expose missing tools, credentials, targets, or observations as user-visible gaps.
  • Hold at least two competing failure hypotheses at once, each with observed evidence for and against; a diagnosis that never named a rival hypothesis is a guess.
  • Order probes cheapest-discriminating-first: run the cheapest check that splits the surviving hypotheses before any expensive capture, rerun, or restart.
  • When a run that used to work now fails, bisect from last-known-good to first-bad change (prompt, config, tool, model, or environment) instead of debugging the newest symptom.
  • Name a cause only after revert-verify: remove the suspect change and observe the failure disappear, or state that causation is unproven.
  • Reproduce the failure before preparing any recovery action; a fix without a reproduced failure first is a guess.

Required inputs:

  • user request
  • target context
  • delivery or status expectation
  • known missing evidence

Expected outputs:

  • agent_debug_report/v1
  • agent_failure_capture/v1
  • agent_failure_pattern_hypothesis/v1
  • contained_recovery_action/v1

Artifact expectations:

  • agent_debug_report/v1 with failure pattern, recent tool sequence, goal/context pressure, environment assumptions, recovery action, and evidence status
  • agent_failure_capture/v1 separating observed errors and tool loops from inferred root-cause hypotheses
  • contained_recovery_action/v1 with the smallest safe next action and explicit escalation boundary

Safety rules:

  • An agent debug report is not executor reset, hidden state mutation, tool repair, implementation, verification, CI, merge-readiness, merge, or proof that future loops are fixed. Record only observed failure evidence, diagnosis hypotheses, contained recovery actions, and remaining blockers.
  • Do not claim connector, gateway, runtime, file generation, memory mutation, or host automation evidence from prepared guidance.

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.

Frequently asked questions

What does the Omh Agent Debug AI skill do?

[omh] Agent Debug workflow: capture a stuck, looping, drifting, or repeatedly failing agent run, diagnose the likely failure pattern, and prepare the smallest safe recovery action. Use when the user says: agent-debug, agent debug, agent debugging, agent introspection, agent self-debug, self-debug, self debugging, looping agent.

Why use Omh Agent Debug on TypingMind?

Because you install it once and use it with any model. Omh Agent Debug 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 Agent Debug in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-agent-debug. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Omh Agent Debug?

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 Agent Debug?

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

Is the Omh Agent Debug 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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