adversarial-review

adversarial-review is a skill for Claude Code, Codex from event4u-app/agent-config. It costs 47 tokens per session (1,727 once invoked), scanned A, original, MIT.

A critical-review guide for deliberately challenging a plan, design, proposed fix, or completed piece of work.

In plain words
What is it for?
For stress-testing non-trivial designs, architecture choices, code changes, and other work when an explicit adversarial review is requested.
Why use it?
It helps expose weak assumptions, missing edge cases, and likely failures before a decision is finalized.

Skill for Claude CodeCodex

Written for no agent in particular: nothing here depends on one. Also seen: mentions subagents.

Good fit For stress-testing non-trivial designs, architecture choices, code changes, and other work when an explicit adversarial review is requested.

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Install with agentmods
npx agentmods add skills/event4u-app/agent-config/adversarial-review
Install

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.

Any agent
npx skills add event4u-app/agent-config --skill adversarial-review
Clone the repo
git clone --depth 1 https://github.com/event4u-app/agent-config

Made for: Claude Code, Codex.

Wrote 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.

agentmods badge for adversarial-review

README.md
[![agentmods](https://agentmods.dev/badge/skills/event4u-app/agent-config/adversarial-review/github.svg)](https://agentmods.dev/skills/event4u-app/agent-config/adversarial-review)
Your own site
<a href="https://agentmods.dev/skills/event4u-app/agent-config/adversarial-review"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/adversarial-review/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.

agentmods 80×15 button for adversarial-review

Your own site · 80×15
<a href="https://agentmods.dev/skills/event4u-app/agent-config/adversarial-review"><img src="https://agentmods.dev/badge/skills/event4u-app/agent-config/adversarial-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 47 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 1,727 The whole file, excluding the scripts and references it only reads on demand.
Security scan A 0 findings. A grade says what 26 rules found in the file — not that it is safe.
Origin original No closer match found in the catalogue.
Token cost

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.

ModelPer sessionOnce invoked
Fable 5.1 $0.00047 $0.01727
Opus 5 $0.00023 $0.00864
Sonnet 5 $0.00009 $0.00345
Haiku 4.5 $0.00005 $0.00173

Measured 2d ago against content hash 99f8332c43aa, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-10, from the pricing page.

Security

Grade A, and why

adversarial-review 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 2d 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.

src/skills/adversarial-review/SKILL.md · 182 lines

How it starts

The opening of the file, as written. The whole thing — 182 lines — stays where its author put it; the contents beside it link to each section on GitHub.

Adversarial Review

When to use

Use this skill when:

  • You've completed a plan, design, or proposed fix and are about to present it.
  • The change is non-trivial (affects multiple files, changes behavior, touches critical paths).
  • You're about to recommend an architecture or design decision.
  • The user submits finished work (draft, post, naming decision, design proposal) and asks for an honest critical take — "what do you actually think?", "be brutal", "was hältst du wirklich davon". The flow is the same Attack-Defend-Revise loop, but on the user's artifact rather than the agent's plan.

Do NOT use when:

  • The task is trivial (renaming, formatting, simple config change).
  • The user explicitly asked for a quick/rough draft.
  • You're exploring options, not committing to one yet.

Inbound delegation: decision-record § Weighted-matrix mode delegates a scoped attack here after the weighted sums ("attack the winner, using the losing options' strongest criteria") — treat the matrix + sensitivity block as the artifact under review.

Procedure: Adversarial review

  1. Inspect the artifact — Read the plan, diff, or draft you are about to critique; note its scope, assumptions, and the explicit asks before attacking.
  2. Attack — Run Step 1 below as the grumpy senior engineer.
  3. Defend — Run Step 2 as the balanced engineer; classify each criticism as must-fix / defer / reject.
  4. Revise — Run Step 3 to fold valid fixes back in, then report every criticism with its disposition. Deciding which trade-offs matter is the user's pass, not this step's.

Step 1: Attack (Grumpy Senior Engineer)

Assume your plan/fix is flawed. Ask yourself:

  • What's the weakest assumption?
  • Where will this break under load, at scale, or with edge cases?
  • What did I ignore or hand-wave?
  • Is this over-engineered for the actual problem?
  • Would a simpler approach work just as well?
  • What will the next developer curse me for?

Step 2: Defend (Balanced Engineer)

Read the full file on GitHub · 182 lines

Files

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.

Changes

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.

  1. 2d ago Changed · -8 lines 99f8332c43aa
  2. 10d ago First seen · 190 lines · 47 tokens per session scan A 79942cff875f

Subscribe to this mod's changes

adversarial-review is a skill published in the GitHub repository event4u-app/agent-config (10 stars, last pushed today), licensed MIT. It adds 47 tokens to every session and 1,727 once invoked, about $0.0002 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 2026-08-31.