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Omh Code Review

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rlaope
omh-code-review

[omh] Hermes Code Review workflow: bug-first review with evidence. Use when the user says: code-review, review, audit, find bugs, release gate, claim audit, evidence audit, README claim.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-code-review
Stars
2.7K
Forks
194
Bundled files
3
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.

  • 3 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 Code Review 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-code-review .claude/skills/omh-code-review
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Code Review 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 Code Review 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 Code Review 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.

Code Review

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

Why This Exists

code-review exists to make review bug-first and evidence-grounded: findings must cite concrete files, diffs, commands, or artifacts before any summary or fix proposal.

Do Not Use When

  • The user asks to implement the fix rather than review existing code or claims.
  • There is no diff, file set, claim, artifact, or expected behavior to review.
  • The request is broad product critique, strategy, or planning rather than code or evidence review.

Examples

Good example:

  • Prompt: $code-review review this PR for install/update UX regressions and missing tests.
  • Expected behavior: Lead with ranked findings, cite concrete evidence, then list open questions and test gaps.
  • Why: The task is explicitly review-shaped and has a behavioral risk surface.

Bad example:

  • Prompt: $code-review add the missing setup flag and commit it.
  • Expected behavior: Route implementation to a selected executor/runtime after review findings are established.
  • Why: Review can identify the issue, but code mutation is a separate execution step.

Completion Checklist

  • Findings come first and are ranked by severity before summary or praise.
  • Every finding cites file, diff, command output, artifact, or expected behavior evidence.
  • Both axes appear in the report: correctness/risk findings, and a spec-axis verdict naming its Claim source or the not_assessed reason.
  • No-issue reviews still name residual risk, missing tests, and independent review evidence if unavailable.
  • The closing carries the checked-and-clean list and the could-not-assess list, each naming its surfaces.
  • Fix implementation, architecture follow-up, and CI/merge claims stay separate from the review result.

Recovery Notes

  • If no diff, file set, PR, or artifact is available, inspect the requested target or ask one target question before reviewing.
  • If tests fail or are missing, cite the exact command gap and do not approve the change as verified.
  • If independent review evidence is unavailable, say so directly instead of implying a second reviewer passed it.
  • To dispatch a reviewer rather than write the findings yourself, load omh-code-review/references/review-dispatch.md; it carries the base-SHA rule and the implementer status contract.
  • When findings arrive for work you own, load omh-code-review/references/review-response.md before changing anything.
  • For maintainability judgement calls, load omh-code-review/references/smell-baseline.md; it names the twelve baseline smells with their fixes and the repo-standards-override rule.

Use When

Use for review-shaped requests; findings come first and must cite concrete evidence.

Strong routing signals: `code-review`, `$code-review`, `review`, `audit`, `find bugs`, `release gate`, `claim audit`, `evidence audit`, `README claim`, `what actually happened`, `code review`, `review gate`, `コードレビュー`, `バグを見つけて`, `実際に何をしたか`, `리뷰`, `코드 리뷰`, `리뷰까지`, `릴리즈 전`, `실제 코드와 맞는가`, `실제로 뭐 했는지`, `검증된 결과`, `代码评审`, `代码审查`, `找出缺陷`

Catalog Metadata

Category: review Phase: critique Quality tier: finding-evidence-gated Reasoning demand: standard

Quality bar:

  • Lead with ranked findings grounded in file, diff, command, or artifact evidence.
  • Separate review findings from fix implementation; fixes become executor work.
  • For Hermes-owned coding work, inspect hermes_coding_harness/v1 and require review evidence before upgrading the reviewer lane.
  • Say clearly when no actionable issue is found and name remaining test gaps.
  • Report each finding with priority (P0-P3), confidence, evidence, path, and line_range, then close with one verdict of ship or no_ship plus its own confidence; a finding without a path and line range is an open question, not a finding.
  • REVIEW.md in the reviewed repository defines what blocks: map its blocking definitions onto P0/P1 and let a no_ship verdict follow from that file rather than from reviewer preference. When the repository has no such file, say which blocking definition was used instead.
  • Review on two axes and report them side by side, never re-ranked against each other: the correctness/risk axis judges the code as it is, and the spec axis judges the diff against the dispatch's Claim and Requirements pointer. A clean diff that does not do what was asked is a spec-axis finding; when no Claim or spec pointer was supplied, report the spec axis as not_assessed with that reason instead of staying silent.
  • Judge maintainability findings against the named baseline in omh-code-review/references/smell-baseline.md: a baseline smell is a judgement call to argue from evidence, never an automatic finding, and the reviewed repository's own standards override the baseline wherever they conflict.
  • Close with two lists beside the verdict: what was checked and found clean, and what could not be assessed with the reason. An absent finding is evidence only when the closing says the surface was actually checked.

Required inputs:

  • diff or files
  • expected behavior
  • test evidence
  • the dispatch Claim and Requirements pointer (issue, plan, or spec section) when intent is reviewable

Expected outputs:

  • ranked findings per axis
  • spec-axis verdict or a named not-assessed reason
  • open questions
  • test gaps
  • checked-and-clean and could-not-assess lists

Artifact expectations:

  • critic run record when review evidence is captured

Safety rules:

  • Findings come before summaries.
  • Cite concrete evidence for every finding.
  • Say clearly when no issue is found.

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 Code Review AI skill do?

[omh] Hermes Code Review workflow: bug-first review with evidence. Use when the user says: code-review, review, audit, find bugs, release gate, claim audit, evidence audit, README claim.

Why use Omh Code Review on TypingMind?

Because you install it once and use it with any model. Omh Code Review 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 Code Review in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-code-review. 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 Code Review?

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 Code Review?

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

Is the Omh Code Review 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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