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Orchestrating Adversarial Reviews

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telagod
orchestrating-adversarial-reviews

Multi-agent adversarial-verification orchestration for high-confidence conclusions. Fan-out finders, then verify every finding through a three-prism panel (exploitability / correctness / refutation) that defaults to disbelief, gate fixes behind load-bearing proof tests that catch agents who falsely claim "done/fixed", and roll out behind a build-first exit-code guard. Use when a fan-out task must produce trustworthy results — security audit, code review, research synthesis, migration — and a single agent's self-report cannot be trusted. Composes with securing-systems (what to look for) and shipping-changes (change closed loop); orchestration engine is the Workflow tool.

Overview

Publishertelagod
Repositorycode-abyss
Skill nameorchestrating-adversarial-reviews
Stars
242
Forks
33
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 telagod on GitHub. Read the source before you install it.

Installation

Install the Orchestrating Adversarial Reviews 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/telagod/code-abyss/tree/main/skills/orchestrating-adversarial-reviews
  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/telagod/code-abyss.git /tmp/code-abyss
mkdir -p .claude/skills
cp -r /tmp/code-abyss/skills/orchestrating-adversarial-reviews .claude/skills/orchestrating-adversarial-reviews
Restart Claude Code after copying so it picks up the new skill.

Use it in TypingMind

Enable Orchestrating Adversarial Reviews 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 Orchestrating Adversarial Reviews 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 Orchestrating Adversarial Reviews 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.

对抗验证编排 · orchestrating-adversarial-reviews

单个 agent 会谎报"已修复 / 全覆盖 / 没问题"。结论的可信度不来自"谁说的",来自"扛过几次推翻"。 本 skill 是编排骨架:fan-out 发现 → 三棱镜对抗验证 → 证明性 guard → 守卫式上线。 信级:运行时行为 / 证明测试 > 多 agent 多数裁决 > 单 agent 自报(永远 [unverified])。

核心信条

  1. 不信单 agent 自报。 finder 会噪音误报,implementer 会谎称"已修复/全覆盖"。每个高价值结论必须被独立 agent 用不同视角尝试推翻。
  2. 可证伪 > 可声称。 修复必须配一个"退回漏洞代码就 FAIL、修好才 PASS"的 load-bearing 证明测试。没有证明测试的"已修复"等于没修。
  3. 失败方向要对。 守卫链里任何一步的退出码都不能被管道遮住;破坏性动作前置可逆检查。

何时使用

场景用理由
授权安全审计 / 加固闭环✅首个范例,见 workflow
大面积代码审查(多维度、需高可信)✅dimensions → find → 对抗验证
研究综合 / 事实核查(结论要扛得住)✅多源 fan-out + 证伪棱镜
大规模迁移 / 重构(site 发现 + 逐项验证)✅pipeline 逐项独立 + 证明测试

何时不使用

  • ❌ 单文件、低风险、机械改动——直接做完跑测试,别套编排(参见 shipping-changes 的"何时不使用")。
  • ❌ 用户没有 opt-in 多-agent 编排 / 没开 ultracode——Workflow 会 fan-out 几十个 agent 烧大量 token,必须显式授权。
  • ❌ 只需要"找什么洞"的知识——那是 securing-systems / analyzing-security,本 skill 不重写知识,只编排。

编排骨架(三相)

Recon (fan-out)     每维一个 finder, 并行深读, schema 出结构化 findings
   |                pipeline 而非 barrier: 维度A的发现可在维度B还在找时就进验证
   v
Verify (三棱镜)     每条 finding 派 N 个 verifier, 各执一镜, 默认怀疑
   |                可利用性 / 正确性 / 证伪猎杀 —— 票数 >= 多数 才保留
   v
Synthesize / Ship   合成定级报告; 若是修复任务 -> 证明测试 guard -> build-first 上线

对应 Workflow 工具的 pipeline(items, findStage, verifyStage)(默认无栅栏,墙钟最短)。仅当"下一阶段需全部上一阶段结果"(去重 / 早退 / 跨条比较)才用 parallel 栅栏。

四大护栏(实战血泪,按重要度)

  1. 三棱镜对抗验证(防 finder 噪音)——每条发现派视角各异的 verifier(可利用性 / 正确性 / 证伪猎杀),默认怀疑,多数票 confirmed 才保留。
  2. 证明性测试 guard / load-bearing(防 implementer 谎报)——修复配真行为测试,且测试本身能"反向证伪"(退回漏洞版必 FAIL)。本 skill 的命门。
  3. build-first + 退出码 guard(防误删 / 半成品上线)——先构建验证新件再动旧件;cmd | tail 会吞掉 cmd 的退出码,判码用 cmd > log 2>&1; rc=$?。
  4. 语境校准降噪——fan-out 前钉死威胁模型 / 评判语境,否则收一堆无效发现。

每条护栏的细节、PoC 判据、反向证伪实操、安全审计 worked example,见 references/workflow.md。

反模式

  • 信单 agent "已修复 / 没问题 / 全覆盖"——必过对抗验证 + 证明测试。
  • up --build 2>&1 | tail && rm <old>——管道吞码,build 失败仍删旧件。
  • N 个同质 verifier 复读同一判断——要视角多样,否则冗余不抗错。
  • 多 agent 同一工作区并行写同批文件——冲突;要么单 implementer,要么 isolation: worktree。
  • 用户未 opt-in 时擅自 fan-out 大舰队——先确认或估算成本。

参见

  • securing-systems —— 攻防知识总路由(找什么洞)。
  • shipping-changes —— 单上下文变更闭环脊柱。
  • cultivating-skills —— 本 skill 的孵化器 / 安全脊柱 / 升级漏斗。
  • Workflow 工具 —— 编排引擎(pipeline / parallel / schema / budget)。

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 Orchestrating Adversarial Reviews AI skill do?

Multi-agent adversarial-verification orchestration for high-confidence conclusions. Fan-out finders, then verify every finding through a three-prism panel (exploitability / correctness / refutation) that defaults to disbelief, gate fixes behind load-bearing proof tests that catch agents who falsely claim "done/fixed", and roll out behind a build-first exit-code guard. Use when a fan-out task must produce trustworthy results — security audit, code review, research synthesis, migration — and a single agent's self-report cannot be trusted. Composes with securing-systems (what to look for) and...

Why use Orchestrating Adversarial Reviews on TypingMind?

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

Open Plugins → Skills → Install from GitHub in TypingMind and paste https://github.com/telagod/code-abyss/tree/main/skills/orchestrating-adversarial-reviews. 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 Orchestrating Adversarial Reviews?

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 Orchestrating Adversarial Reviews?

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

Is the Orchestrating Adversarial Reviews AI skill free?

Yes. It is published on GitHub by telagod 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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