oh-my-hermes is an operating layer for Hermes Agent that organizes requests into workflows for planning, research, creation, coding handoffs, operations, and project memory. Hermes users run these workflows through the desktop app, CLI, or messenger app, while the catalogue add-ons extend its native capabilities.
Getting it into your agent
One page per mod, every tool's command on it. A separate URL per tool would split the same page into five that compete with each other.
npx skills add rlaope/oh-my-hermes --skill omh-code-reviewgit clone --depth 1 https://github.com/rlaope/oh-my-hermesWrote this? Show the measurements
A badge with what this costs and how it scanned, read live from this page, so it follows the numbers instead of freezing them. Markdown for a README, HTML for a documentation site or a project page.
[](https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-code-review)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-code-review"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-code-review/github.svg" alt="Measured on agentmods" height="20"></a>Or the 80×15 button, for a site that already has a row of RSS and ATOM ones. Only the verdict fits; the numbers stay here.
<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-code-review"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-code-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector pass
What it costs to keep this loaded
Counted locally with the o200k_base tokenizer, which is exact for GPT models; Claude uses its own tokenizer and its counts differ. Treat this as one consistent yardstick across the catalogue rather than a bill. Prices are per million input tokens.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00046 | $0.01697 |
| Opus 5 | $0.00023 | $0.00848 |
| Sonnet 5 | $0.00009 | $0.00339 |
| Haiku 4.5 | $0.00005 | $0.00170 |
Grade A, and why
omh-code-review scanned grade A with 0 findings against 26 rules in 11 categories — prompt injection, anti-refusal, data exfiltration, privilege escalation, supply chain, agent snooping, system-prompt leakage, SSRF and excessive agency — measured 7d ago.
A static scan of the body, not an audit. Every finding is printed with the line that produced it so you can judge whether it matters here. A mod is markdown that instructs an agent; that is exactly why what it instructs is worth reading.
Nothing flagged
None of the 26 patterns this scan looks for appear in this file: no shell pipes, no recursive deletes, no credential paths, no hidden text, no instruction-override or anti-refusal phrasing, no agent-config snooping. That is not a guarantee, it is the absence of the things that are checkable.
How it starts
The opening of the file, as written. The whole thing — 136 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Code Review
This is a Hermes-native code-review workflow skill.
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_assessedreason. - 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.mdbefore 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.
What ships with it
3 files beside SKILL.md in the same directory: the scripts, references and assets a skill reads on demand. Not counted in the per-session cost; read them before you install if any of them is executable.
What this file has done since we first saw it
Hashed on every crawl. A supply-chain change to an agent config is a question of when, not whether, so the history is kept rather than the latest state alone.
- 7d ago Changed · +9 lines 6043c0cbd886
- 10d ago First seen · 127 lines · 46 tokens per session scan A dea52ad6be81
omh-code-review is a skill published in the GitHub repository rlaope/oh-my-hermes (1,605 stars, last pushed today), licensed MIT. It adds 46 tokens to every session and 1,697 once invoked, about $0.0002 per session on Opus 5. A static security scan graded it A with 0 findings. No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-30.
Other skills, from other repositories
reviewing-changes
Use when a review package asks you to review a plan step's change set (todo.startReview): you are the REVIEWER, not the author. How to judge an agent-written diff, file findings with addreviewcomment, and settle with exactly one reviewverdict.
reflecting-findings
Use when a reflection package hands you another agent's review findings to verify (before they become a fix request): you are the REFLECTOR, an independent skeptic. Judge each finding against the real code and settle it with reflectfinding — kept or refuted.
nlpm-audit
Audit SKILL.md, AGENTS.md, prompts, hooks, and plugin manifests for instruction conflicts, quality, broken references, and manifest-to-disk drift.
api-design-reviewer
Use when reviewing API designs for consistency, usability, versioning, error semantics, security, backward compatibility, and developer experience before implementation or release.
pr-review-expert
Review GitHub PRs or GitLab MRs for correctness, security, compatibility, and affected test coverage, with actionable evidence tied to the diff.
gh-address-comments
Use when addressing GitHub PR review comments or issue comments on the current branch with gh CLI, including auth checks, comment triage, edits, verification, and replies.