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Omh Failure Signal Audit

CommunityPopular
rlaope
omh-failure-signal-audit

[omh] Failure Signal Audit workflow: find swallowed errors, unsafe fallbacks, hidden UI/runtime failures, and missing propagation before they become false green status. Use when the user says: failure-signal-audit, failure signal audit, silent failure, silent failures, silent failure hunter, swallowed error, swallowed errors, empty catch.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-failure-signal-audit
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 Failure Signal Audit 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-failure-signal-audit .claude/skills/omh-failure-signal-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Failure Signal Audit 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 Failure Signal Audit 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 Failure Signal Audit 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.

Failure Signal Audit

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

Why This Exists

failure-signal-audit 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: failure-signal-audit check this frontend and agent trace for swallowed errors, false green status, and dangerous fallbacks.
  • Expected behavior: Produce prepare_failure_signal_audit 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: failure-signal-audit silently patch every catch block and claim the system is reliable now.
  • 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

  • Audit scope, source surfaces, and evidence types are named.
  • Swallowed errors, dangerous fallbacks, propagation gaps, and false-green claims are reported as separate finding types.
  • Each finding names location or evidence ref, severity, user/operator impact, and a smallest safe remediation route.
  • No remediation, runtime repair, verification, CI, merge, or future reliability claim is made without observed follow-up evidence.

Recovery Notes

  • If no code/trace/runtime evidence is supplied, prepare the audit plan and request the smallest source surface to inspect.
  • If the user wants live service SLO or incident review, route to reliability-review.
  • If the user wants rendered browser proof, route frontend visual evidence to visual-qa before PASS.

Use When

Use when Hermes should audit code, frontend/browser behavior, agent traces, or runtime reports for failures that were swallowed, downgraded, hidden by fallbacks, or reported as green without enough evidence.

Strong routing signals: `failure-signal-audit`, `failure signal audit`, `silent failure`, `silent failures`, `silent failure hunter`, `swallowed error`, `swallowed errors`, `empty catch`, `ignored exception`, `hidden failure`, `hidden failures`, `dangerous fallback`, `bad fallback`, `fallback hides errors`, `missing error propagation`, `error propagation`, `console errors ignored`, `network failures ignored`, `false green`, `false pass`, `무음 실패`, `조용한 실패`, `숨은 실패`, `삼킨 에러`, `에러 삼킴`, `위험한 fallback`, `위험한 폴백`, `폴백이 에러 숨김`, `실패 신호 감사`, `실패 신호`

Catalog Metadata

Category: review Phase: failure-signal-audit Quality tier: workflow-surface-gated Reasoning demand: standard

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 masked-failure and intended-fallback as competing hypotheses for each suspect site, each with observed evidence for and against, until one reading is discriminated.
  • Order evidence probes cheapest-discriminating-first: read the handler and its callers, then logs and traces, before demanding expensive reruns or instrumentation.
  • When a check went green without an observed fix, bisect from the last run that surfaced the failure to the first that swallowed it before naming the masking change.
  • Attribute a masked failure to a specific handler or fallback only with revert-verify evidence (the signal observed reappearing without it), or mark causation unproven.
  • Route remediation only against a reproduced failing signal; a remediation handoff without a reproduced failure first is a guess.

Required inputs:

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

Expected outputs:

  • failure_signal_audit_plan/v1
  • silent_failure_finding/v1 when observed
  • fallback_risk_matrix/v1
  • propagation_gap_map/v1
  • false_green_status_review/v1
  • remediation_handoff/v1 when needed

Artifact expectations:

  • failure_signal_audit_plan/v1 with source boundary, surfaces, evidence types, and stop condition
  • silent_failure_finding/v1 only from observed code, trace, console, network, test, or runtime evidence
  • fallback_risk_matrix/v1 separating safe fallback, user-visible degraded mode, masked failure, and destructive fallback
  • propagation_gap_map/v1 for missing context, lost stack, ignored async rejection, empty catch, null/empty default, or log-only handling
  • false_green_status_review/v1 comparing PASS/green claims against observed checks and missing signals
  • remediation_handoff/v1 only after findings are accepted and the selected owner is explicit

Artifact contracts:

This label denotes the machine-enforcement level, not a skill quality score and not an observed evidence state.

  • contract_id: failure_signal_audit_plan/v1; enforcement_level: guidance_only; consumer_id: none

Safety rules:

  • A failure signal audit is not remediation, code modification, runtime repair, console/network pass, incident closure, verification, review, CI, merge-readiness, merge, or proof that hidden failures no longer exist.
  • 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 Failure Signal Audit AI skill do?

[omh] Failure Signal Audit workflow: find swallowed errors, unsafe fallbacks, hidden UI/runtime failures, and missing propagation before they become false green status. Use when the user says: failure-signal-audit, failure signal audit, silent failure, silent failures, silent failure hunter, swallowed error, swallowed errors, empty catch.

Why use Omh Failure Signal Audit on TypingMind?

Because you install it once and use it with any model. Omh Failure Signal Audit 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 Failure Signal Audit in TypingMind?

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

Which AI models can use Omh Failure Signal Audit?

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 Failure Signal Audit?

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

Is the Omh Failure Signal Audit 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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