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-adversarial-consensusgit 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-adversarial-consensus)<a href="https://agentmods.dev/skills/rlaope/oh-my-hermes/omh-adversarial-consensus"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-adversarial-consensus/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-adversarial-consensus"><img src="https://agentmods.dev/badge/skills/rlaope/oh-my-hermes/omh-adversarial-consensus.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 | $0.00077 | $0.02234 |
| Opus 5 | $0.00039 | $0.01117 |
| Sonnet 5 | $0.00015 | $0.00447 |
| Haiku 4.5 | $0.00008 | $0.00223 |
Grade A, and why
omh-adversarial-consensus 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 — 138 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Consensus
This is a Hermes-native adversarial-consensus workflow skill.
Why This Exists
adversarial-consensus exists because agreement reached by perspectives that read each other is not review — it is convergence. Independent findings, an attack round nobody is allowed to defend against, and a distillation that may only subtract produce objections a single planning pass never surfaces, and the mandatory handoff keeps that bundle from being mistaken for the plan.
Do Not Use When
- The user wants the plan itself, with options, acceptance criteria, and verification commands; use
ralplan, which this workflow feeds. - The request is still too ambiguous to state the proposal being attacked; use
deep-interviewfirst. - The user wants completed code reviewed for defects rather than a proposal attacked before it is built; use
code-review. - The user wants hostile runtime scenarios against a built change; use
ultraqa. - One perspective would do: a small local change with no contested decision does not earn three rounds.
Examples
Good example:
- Prompt: $adversarial-consensus we plan to move session state into Redis before the launch — attack it from every angle before I write the plan.
- Expected behavior: Name the roster and their distinct angles, take blind findings from each, run one attack-only round, resolve each objection to defend/refine/concede, distill only into the four buckets, and hand the bundle to
ralplanas planning input. - Why: The decision is contested and pre-plan, which is exactly where independent objections are worth more than one planner's confidence.
Bad example:
- Prompt: $adversarial-consensus give me the migration plan with the steps and the rollout order.
- Expected behavior: Produce the distilled bundle and hand it to
ralplan; the steps and rollout order are the planner's output, not this workflow's. - Why: The bundle is INPUT to planning. Emitting a plan here skips the reviewed-plan gate and turns the buckets into a task list.
What ships with it
1 file 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 d4d70f40b576
- 8d ago Changed 1afbc6d64535
- 12d ago First seen · 138 lines · 77 tokens per session scan A 3a216cc1fa8f
omh-adversarial-consensus is a skill published in the GitHub repository rlaope/oh-my-hermes (1,677 stars, last pushed today), licensed MIT. It adds 77 tokens to every session and 2,234 once invoked, about $0.0004 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.
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