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Omh Adversarial Consensus

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rlaope
omh-adversarial-consensus

[omh] Hermes Adversarial Consensus workflow: independent perspectives attack a proposal, then distill into a bundle a separate planner consumes. Use when the user says: adversarial-consensus, adversarial planning, adversarial plan review, red team this plan, red-team this plan, red team the proposal, multi-perspective review, multiple perspectives.

Overview

Publisherrlaope
Repositoryoh-my-hermes
Skill nameomh-adversarial-consensus
Stars
2.7K
Forks
194
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 Adversarial Consensus 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-adversarial-consensus .claude/skills/omh-adversarial-consensus
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Omh Adversarial Consensus 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 Adversarial Consensus 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 Adversarial Consensus 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.

Adversarial Consensus

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

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-interview first.
  • 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 ralplan as 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.

Completion Checklist

  • The roster is named with 3-5 distinct angles, and no two seats argue the same one.
  • Round-one findings were produced blind, and any perspective that could not be kept blind is named as a broken-independence caveat instead of being presented as independent.
  • Every cross-attack objection targets another perspective's finding, and no perspective defended itself in that round.
  • Every objection carries exactly one verdict — defended, refined, or conceded — and conceded findings are struck, not softened.
  • The bundle contains only Hard Constraints, Decisions, Risks, Open Questions, every line traces to a surviving finding, and nothing new was added at distillation.
  • The closing message states that the bundle is input, names the follow-on planning workflow, and claims no plan, acceptance, implementation, or verification evidence.

Recovery Notes

  • If the proposal under review cannot be stated in one paragraph, route back to deep-interview before opening round one.
  • If independence was broken — a perspective saw another's findings, or the same seat produced two angles — say so, re-run that perspective on a restated problem, and mark the round's independence as caveated rather than silently continuing.
  • If a round produces no objections at all, treat that as a roster defect rather than consensus: state which angle is missing and add or replace a seat before distilling.
  • If distillation would need a fifth bucket, the extra content is a plan trying to escape; move it to the planner handoff instead of widening the bucket set.

Use When

Use when a proposal, plan, or direction needs independent perspectives to attack it before a plan is written, and the distilled result is meant as input to planning rather than as the plan.

Strong routing signals: `adversarial-consensus`, `$adversarial-consensus`, `adversarial planning`, `adversarial plan review`, `red team this plan`, `red-team this plan`, `red team the proposal`, `multi-perspective review`, `multiple perspectives`, `independent perspectives`, `attack this proposal`, `poke holes in this`, `hyperplan`, `敵対的レビュー`, `多角的レビュー`, `レッドチームレビュー`, `この計画に反論`, `穴を探して`, `적대적 검토`, `다관점 검토`, `여러 관점에서 검토`, `레드팀 검토`, `이 계획 반박`, `허점 찾아`, `对抗式评审`, `多视角评审`, `红队评审`, `反驳这个方案`, `找出漏洞`

Catalog Metadata

Category: planning Phase: adversarial-consensus Quality tier: reviewed-plan-gated Reasoning demand: standard

Quality bar:

  • Name the roster before round one: 3-5 perspectives, each with a stated angle that no other seat covers. The suggested roster is skeptic, validator, researcher, architect, creative; substitute a domain seat when the problem needs one, but two seats arguing the same angle is a duplicate, not a perspective.
  • Run the rounds in order — independent findings; cross-attack; defend, refine, or concede — and state which round is active in every message, because the independence rule and the no-self-defense rule only mean anything relative to the current round. Load references/consensus-protocol.md for the per-round procedure, the per-seat angle table, and the failure modes that make a run look adversarial while producing agreement.
  • Round one is blind: each perspective produces findings without seeing any other perspective's output, and each finding names its evidence or labels itself an assumption.
  • Round two attacks only: every perspective attacks other perspectives' findings and never defends or restates its own. A perspective with no objection to any other seat says so explicitly rather than filling the round with agreement.
  • Round three answers each objection with exactly one verdict — defend with evidence, refine the finding, or concede it — and a conceded finding is struck from the record instead of being softened.
  • The lead distills only. Nothing new enters at distillation: every line in the bundle traces to a surviving finding, and it goes into one of Hard Constraints, Decisions, Risks, Open Questions — never into a fifth bucket, a recommendation, a sequence of steps, or a task list.
  • End with the mandatory handoff: state that the bundle is INPUT to planning, name the follow-on planning workflow (ralplan for a reviewed plan, plan when the shape is already agreed), and stop. Treating the bundle as the plan is the anti-pattern this workflow exists to prevent.
  • Keep round transitions and perspective outputs as declarations: a stated round change is not evidence that the round happened, and a distilled bundle is not plan acceptance, implementation, review, CI, or merge evidence.

Required inputs:

  • the proposal, plan draft, or direction under review
  • the decision the review must inform
  • known constraints and non-negotiables
  • the perspective roster and why each angle is distinct

Expected outputs:

  • per-perspective independent findings
  • cross-attack objections attributed to their author
  • defend, refine, or concede verdict per objection
  • distilled bundle in the fixed buckets Hard Constraints, Decisions, Risks, Open Questions
  • mandatory planner handoff naming the follow-on planning workflow

Artifact expectations:

  • Record the distilled bundle in a durable host-owned or repository file so the planner consumes an artifact, not scrollback.

Safety rules:

  • Do not write the plan here. This workflow produces the input a planner consumes, never the plan itself.
  • Do not let a perspective read another perspective's findings before its own are recorded; a perspective that saw the others is not an independent objection.
  • Do not let a perspective defend its own findings during the cross-attack round; that round attacks other perspectives only.
  • Do not add, rename, or drop a distillation bucket; the closed set is Hard Constraints, Decisions, Risks, Open Questions.
  • Do not invent evidence on behalf of a perspective; an unsupported objection is recorded as an Open Question, not as a Hard Constraint.
  • Do not report a round transition, a perspective's output, or the distilled bundle as executed, reviewed, or accepted work; every phase output is a declaration until the user or a wrapper observes it.

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 Adversarial Consensus AI skill do?

[omh] Hermes Adversarial Consensus workflow: independent perspectives attack a proposal, then distill into a bundle a separate planner consumes. Use when the user says: adversarial-consensus, adversarial planning, adversarial plan review, red team this plan, red-team this plan, red team the proposal, multi-perspective review, multiple perspectives.

Why use Omh Adversarial Consensus on TypingMind?

Because you install it once and use it with any model. Omh Adversarial Consensus 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 Adversarial Consensus in TypingMind?

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/rlaope/oh-my-hermes/tree/main/agent-skills/omh-adversarial-consensus. 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 Adversarial Consensus?

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 Adversarial Consensus?

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

Is the Omh Adversarial Consensus 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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