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-jev-done-checkgit 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-jev-done-check)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-jev-done-check"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-jev-done-check/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-jev-done-check"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-jev-done-check.svg" alt="Reviewed on agentmods" width="80" height="20"></a>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 | $0.00066 | $0.00641 |
| Opus 5.5 | $0.00026 | $0.00256 |
| Sonnet 5.5 | $0.00013 | $0.00128 |
| Haiku 4.5 | $0.00007 | $0.00064 |
Grade A, and why
omh-jev-done-check 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 3d 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 — 45 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Jev Done Check
Required: each completion claim, observed evidence, and the goal. Output: objections per claim, or no_objection.
Read skill_view(name='omh-routing', file_path='references/jev-rail.md') before the first ask: consent, what leaves the machine, limits, and non-answer statuses. Prepared OMH routing is not execution or approval. Shared product, routing, compatibility, and evidence rules: skill_view(name='omh-routing', file_path='references/skill-common-rail.md').
Contract: references/full-contract.md. Preset: references/preset.md.
Procedure
- Name what leaves the machine: each claim, an excerpt of observed output, and the goal.
- Call
omh_jev_askwithpreset: done_check/v1andstate{claim, evidence_excerpt, goal}, one ask per claim, at most 6. - Report each objection with its claim and rule; fix or withdraw an objected claim before reporting completion.
Completion Checklist
- Preserve workflow intent and stop conditions; load the full contract before claiming completion.
- Reply in the user's own words and the host's own voice: its SOUL.md 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 stop condition or a decision the user owns ends the turn, offer the next action as a question rather than declaring what will not be done.
- Report the tool's status, ask_id, served model, and cost source; a non-answer is
not_observedwithrule: not_answered:<status>, never Jev's answer. - Record observed delegation results; use Hermes-native subagent/delegation features when available: native subagents -> Hermes delegation when available, otherwise sequential lanes.
Recovery Notes
- Tool missing,
consent_not_observed, or refused: follow the rail's recovery section; never simulate an answer.
What ships with it
2 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.
- 3d ago Changed · +3 lines 4206353be654
- 16d ago Changed bab41b50fcf2
- 21d ago Changed · +7 tokens per session c5f36199599d
- 23d ago First seen · 42 lines · 59 tokens per session scan A 0baa50f61ad4
omh-jev-done-check is a skill published in the GitHub repository rlaope/oh-my-hermes (3,392 stars, last pushed today), licensed MIT. It adds 66 tokens to every session and 641 once invoked, about $0.0003 per session on Opus 5.5. 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 2026-09-24.
Other skills, from other repositories
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.
improve-codebase-architecture
Review module boundaries, interface depth and high-leverage refactors with evidence.
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.
doubt-driven-development
Review high-risk or uncertain engineering decisions adversarially when independent verification can expose costly assumptions before implementation.