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Omh Internal Audit

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rlaope
omh-internal-audit

[omh] Testing an internal control -- SOX, ICFR or ITGC: define the population and the sample, name the evidence that proves each item, re-perform the control, and grade any deficiency from stated likelihood, magnitude and compensating-control criteria, never by assertion. Use when the user says: internal-audit, internal audit, internal control, internal controls, internal control audit, control testing, test of controls, tests of controls.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-internal-audit
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 Internal 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.
    https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-internal-audit
  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-internal-audit .claude/skills/omh-internal-audit
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Internal 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 Internal 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 Internal 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.

Internal Audit

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

Why This Exists

internal-audit exists because control-testing methodology had no owner: finance-analysis reports figures and names control exceptions, tech-debt-audit audits code, and production-audit audits a release, while population, sampling, re-performance, and deficiency severity had nothing that derived a grade from criteria.

First Steps

  • Ask what the control is meant to prevent or detect, and over which population and period it operates.
  • Ask for the sampling guidance and the materiality threshold before sizing any sample or grading anything.

Do Not Use When

  • The ask is the month's figures, a budget variance, or the close itself; use omh-finance-analysis.
  • The ask is reading a contract or a regulation for legal risk; use omh-legal-compliance-review.
  • The ask is whether a service is ready to launch; use omh-production-audit.
  • The ask is ranking a codebase's debt; use omh-tech-debt-audit.

Examples

Good example:

  • Prompt: we need to test the quarterly access review control for sox and grade what we find
  • Expected behavior: Define the population of quarterly reviews for the period and check its completeness, size the sample from the quarterly frequency, request the signed review and the removal tickets per item, re-perform the comparison of access lists, and grade any deviation from stated likelihood, magnitude and compensating controls.
  • Why: A grade that is not derived from criteria cannot be defended to an external auditor.

Bad example:

  • Prompt: the owner says the control worked, just mark it effective
  • Expected behavior: Refuse to conclude on inquiry alone: sample the population, obtain the evidence, and re-perform before any conclusion.
  • Why: An owner's statement is the weakest evidence a control test can hold.

Completion Checklist

  • The control, its population, and the completeness check are stated.
  • The sample size is derived from stated frequency, confidence, and tolerable rate, and the selection can be redrawn.
  • Each sample item names the evidence obtained, not described.
  • Each item's re-performance result cites its evidence.
  • The severity grade shows every criterion, or is withheld with the missing one named, and OMH signed off nothing.

Recovery Notes

  • If the population cannot be shown complete, stop and name the completeness test before any sampling.
  • If materiality or the compensating controls are not stated, report the deviations and withhold the grade.

When to Use

Use when an internal control is being tested or a control failure graded: SOX or ICFR testing, IT general controls, a control owner's evidence, a sample of transactions, re-performing a reconciliation or an approval, or deciding whether a deficiency is a significant deficiency or a material weakness. The output is the control and its population, the sample design, the evidence per item, the re-performance record, and a severity grade derived from stated criteria; OMH reads no ledger and tests nothing itself.

Strong routing signals: `internal-audit`, `internal audit`, `internal control`, `internal controls`, `internal control audit`, `control testing`, `test of controls`, `tests of controls`, `sox`, `sox 404`, `sox testing`, `sox control`, `icfr`, `itgc`, `itgcs`, `material weakness`, `significant deficiency`, `control deficiency`, `deficiency severity`, `re-performance`, `reperformance`, `reperform`, `reperform the control`, `audit sampling`, `attribute sampling`, `control population`, `audit evidence`, `audit workpaper`, `segregation of duties`

Catalog Metadata

Category: review Phase: internal-audit Quality tier: criteria-derived-severity Reasoning demand: standard

Quality bar:

  • Define the population and check its completeness before designing the sample.
  • Load references/control-audit-method.md for the sample-size table, the evidence hierarchy, and the severity decision table instead of recalling them.
  • Re-perform the control independently; do not re-read the owner's conclusion.
  • Show every criterion beside the severity grade so a reviewer can re-derive it.
  • Keep planned, sampled, evidenced, re-performed, and graded as separate states for every item.

Required inputs:

  • the control: its objective, owner, frequency, and the risk or assertion it addresses
  • the population it operates over: the period, the source system, and how completeness was checked
  • the firm's or team's sampling guidance, or the confidence and tolerable deviation rate to use
  • the materiality threshold and the compensating controls that exist
  • the evidence each sample item actually produced, as observed documents, logs, or approvals

Expected outputs:

  • control_under_test/v1
  • sample_design/v1
  • evidence_request/v1
  • reperformance_record/v1
  • deficiency_severity_grade/v1

Artifact expectations:

  • control_under_test/v1 names the control's objective, owner, frequency, and risk, and the population with its period, source, and completeness check
  • sample_design/v1 states the sampling method and derives the sample size from the control's frequency and the stated confidence and tolerable deviation rate, with a selection record that lets someone draw the same items again
  • evidence_request/v1 names, per sample item, the document, log, or approval that proves the control operated and who provides it, and separates evidence obtained from evidence described
  • reperformance_record/v1 records, per sample item, the independent re-performance of the control and its result -- operated, deviation, or not testable -- with the evidence reference
  • deficiency_severity_grade/v1 derives control deficiency, significant deficiency, or material weakness from stated likelihood, magnitude against stated materiality, and compensating controls, shows each criterion's value and source, and withholds the grade when a criterion is missing

Safety rules:

  • Derive every severity grade from stated criteria -- likelihood, magnitude against a stated materiality, and compensating controls; with a criterion missing, withhold the grade rather than assert one.
  • Check the population's completeness before sampling from it; a sample from an incomplete population says nothing about the items left out.
  • Record evidence obtained, not evidence described; a control owner's explanation is inquiry, not proof the control operated.
  • Never drop a deviation found in the sample because it was explained; record it and evaluate it against the tolerable rate.
  • OMH reads no ledger, tests no control, and signs off nothing; every population, sample, and result comes from observed output or is marked unverified.

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 Internal Audit AI skill do?

[omh] Testing an internal control -- SOX, ICFR or ITGC: define the population and the sample, name the evidence that proves each item, re-perform the control, and grade any deficiency from stated likelihood, magnitude and compensating-control criteria, never by assertion. Use when the user says: internal-audit, internal audit, internal control, internal controls, internal control audit, control testing, test of controls, tests of controls.

Why use Omh Internal Audit on TypingMind?

Because you install it once and use it with any model. Omh Internal 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 Internal 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-internal-audit. 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 Internal 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 Internal Audit?

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

Is the Omh Internal 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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