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Adversarial Deliberation

Organization
yogsoth-ai
adversarial-deliberation

Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth, loser-resurrection, and steelman/winner-stress modes are parameters; role execution is runtime-agnostic.

Overview

Publisheryogsoth-ai
Repositoryde-anthropocentric-research-engine
Skill nameadversarial-deliberation
Stars
505
Forks
43
Bundled files
Instructions only
LicenseApache-2.0
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.

  • Self-contained

    Everything the model needs lives in the instructions — no extra files to sync.

  • Open source

    Published by yogsoth-ai on GitHub. Read the source before you install it.

Installation

Install the Adversarial Deliberation 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.
    https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/adversarial-deliberation
  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/yogsoth-ai/de-anthropocentric-research-engine.git /tmp/de-anthropocentric-research-engine
mkdir -p .claude/skills
cp -r /tmp/de-anthropocentric-research-engine/skills/adversarial-deliberation .claude/skills/adversarial-deliberation
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Adversarial Deliberation 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 Adversarial Deliberation 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 Adversarial Deliberation 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-deliberation

Purpose

Run structured attack, defense, cross-examination, and adjudication over a claim, candidate, criterion set, or current winner.

Input contract

yaml
mode_contracts:
  critic-defender-judge: &deliberation_input
    required: [target, claim_or_candidate, criteria]
    optional: [perspectives, escalation_depth]
    constraints: [target_scope_and_criteria_must_be_explicit, evidence_provenance_required]
  courtroom: *deliberation_input
  winner-stress: *deliberation_input
  resurrection-advocacy: *deliberation_input
  criteria-interrogation: *deliberation_input
  stakeholder-objection: *deliberation_input
  counter-thesis: *deliberation_input

Execution protocol

Do not perform called SOP operations inline; each loaded SOP owns its contract and thresholds.

  1. You MUST load skill construct-critique to construct the strongest attack. You MUST load skill construct-defense to construct the strongest defensible case.
  2. You MUST load skill surface-assumptions to expose load-bearing assumptions. You MUST load skill construct-perspective-set to construct alternate perspectives.
  3. You MUST load skill cross-examine to cross-examine each exchange. You MUST load skill assess-sensitivity to perturb load-bearing choices.
  4. You MUST load skill adjudicate-exchange to adjudicate against declared criteria. You MUST load skill calibrate-adversarial-confidence to update calibrated confidence. If the task shifts from balanced exchange to direct attack-surface probing, consider structured-red-team. If an exposed assumption requires focused perturbation, consider assumption-stress-test. If the surviving claim needs a decisive falsification program, falsification-first-audit may be the better next tactic. Deviation: skip defense only when the target is explicitly exploratory; skip sensitivity only when no perturbable input is declared; otherwise retain all steps.

Output contract

yaml
mode_contracts:
  critic-defender-judge: &full_deliberation_output
    produces: [attack_record, defense_record, adjudication, confidence_trace]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
  courtroom: *full_deliberation_output
  winner-stress:
    produces: [attack_record, adjudication, confidence_trace]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
  resurrection-advocacy: &defense_deliberation_output
    produces: [defense_record, adjudication, confidence_trace]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
  criteria-interrogation:
    produces: [attack_record, defense_record, adjudication]
    delta_fields: [findings, evidence_updates, uncertainties, decisions, open_questions]
  stakeholder-objection: *full_deliberation_output
  counter-thesis: *defense_deliberation_output

Thresholds and quality gates

  • A-class debate calibration: declared universe = all rounds and claims; numerator = rounds with evidence-linked confidence updates; batch increment = one completed exchange; stopping reason = confidence stabilizes or unresolved disagreement is explicitly reported; source references = evidence IDs and round IDs; direction/threshold reason = escalate when new evidence changes ranking, terminate only when criteria are adjudicated or a non-resolvable uncertainty is recorded.
  • Every verdict must cite criteria and preserve dissent.

Failure and counterexamples

Do not treat rhetorical fluency as evidence. Mark unresolved when attack and defense share an untested assumption or criteria are absent.

Provenance map

  • resolved: multiagent-debate
  • resolved: critic-defender-judge
  • resolved: courtroom-structured
  • resolved: adversarial-escalation
  • resolved: steel-manning
  • resolved: resurrection-advocacy
  • resolved: winner-stress-testing
  • resolved: adversarial-debate-protocol
  • resolved: assumption-excavation
  • resolved: multi-perspective-attack
  • intermediate: Pass5/adversarial-debate
  • intermediate: Pass5/steelman-validation

Preserved source criteria ledger

sourcesource linekindsource criterion
v4 architecturenode desctextualStructured attack/defense/adjudication; loser-resurrection and steelman/winner-stress are parameters.

Context checkpoint / Delta notes

Append claim, criteria, attack/defense records, dissent, confidence changes, and unresolved questions.

Mode branches

  • critic-defender-judge: attack, defense, adjudication.
  • courtroom: add cross-examination and explicit evidentiary ruling.
  • winner-stress: perturb the current winner and record failure triggers.
  • resurrection-advocacy: re-open a rejected candidate with a steelman.
  • criteria-interrogation: challenge the criteria before ranking.
  • stakeholder-objection: add perspective-specific objections.
  • counter-thesis: require a mechanism-distinct opposing thesis.

Frequently asked questions

What does the Adversarial Deliberation AI skill do?

Run structured attack/defense/adjudication over a claim, candidate, criterion set, or current winner. Perspective, target, escalation depth, loser-resurrection, and steelman/winner-stress modes are parameters; role execution is runtime-agnostic.

Why use Adversarial Deliberation on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/yogsoth-ai/de-anthropocentric-research-engine/tree/main/skills/adversarial-deliberation. TypingMind reads its SKILL.md and installs it as a skill you can enable per chat.

Which AI models can use Adversarial Deliberation?

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 Adversarial Deliberation?

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

Is the Adversarial Deliberation AI skill free?

Yes. It is published on GitHub by yogsoth-ai under the Apache-2.0 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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