ag-adversario

ag-adversario is a skill for Claude Code from andregusman-raiz/a-gusman-claude. It costs 93 tokens per session (3,349 once invoked), scanned A, original, MIT.

A review step that tries to break a software design before it is built, using checks for inputs, timing, dependencies, state, and assumptions.

In plain words
What is it for?
It is for reviewing a specification or product requirements document, then sending the review to an independent OpenAI Codex judge until both reviewers approve it.
Why use it?
It exposes edge cases and weak assumptions early, before they become code or failures in production.

Skill for Claude Code

Written for Claude Code: allowed-tools in frontmatter. Also seen: model in frontmatter; mentions Codex.

Good fit It is for reviewing a specification or product requirements document, then sending the review to an independent OpenAI Codex judge until both reviewers approve it.

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Install with agentmods
npx agentmods add skills/andregusman-raiz/a-gusman-claude/ag-adversario
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 andregusman-raiz/a-gusman-claude --skill ag-adversario
Clone the repo
git clone --depth 1 https://github.com/andregusman-raiz/a-gusman-claude

Made for: Claude Code.

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 ag-adversario

README.md
[![agentmods](https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-adversario/github.svg)](https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-adversario)
Your own site
<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-adversario"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-adversario/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 ag-adversario

Your own site · 80×15
<a href="https://agentmods.dev/skills/andregusman-raiz/a-gusman-claude/ag-adversario"><img src="https://agentmods.dev/badge/skills/andregusman-raiz/a-gusman-claude/ag-adversario.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 93 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 3,349 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. Third-party audits
  • NVIDIA SkillSpector warn 7 Sept 2026
SkillSpector: 1 finding, up to medium

These are SkillSpector’s own severities. On a checked sample its high-severity flags on skills were ~96% false positives — a documented command, a public API, a “never do X” rule — so we show them as a caution to read, not a verdict. Why →

  • medium Excessive Agency · line 4
    Skill selects an external model or provider that may use a different account or billing plan than the operator expects. Undisclosed model switches can cause unexpected cost or quota consumption.
    Fix: Remove the model/provider override or disclose it prominently and require explicit operator approval before invoking an external coding CLI or billed model.
How audits are shown
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 $0.00093 $0.03349
Opus 5.5 $0.00037 $0.01340
Sonnet 5.5 $0.00019 $0.00670
Haiku 4.5 $0.00009 $0.00335

Measured 2mo ago against content hash ed7cc619a7b0, method: parsed. Prices are Anthropic first-party input rates as of 2026-10-07, from the pricing page.

Security

Grade A, and why

ag-adversario 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 2mo 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.

skills/ag-adversario/SKILL.md · 276 lines

How it starts

The opening of the file, as written. The whole thing — 276 lines — stays where its author put it; the contents beside it link to each section on GitHub.

ag-adversario — Adversarial Review

Quem voce e

Voce e um engenheiro senior adversarial. Seu unico trabalho e TENTAR QUEBRAR o design antes que ele vire codigo. Voce pensa como um atacante, um usuario malicioso, um sistema externo instavel, e uma rede que cai no pior momento possivel.

Voce NAO sugere melhorias. Voce encontra FALHAS.

Doutrina: dois atos

O design so passa quando DOIS modelos de providers diferentes assinam embaixo:

  • Ato 1 (Fases 1-4) — VOCE (Claude) quebra o design pelas 5 lentes. Veredicto preliminar.
  • Ato 2 — um juiz INDEPENDENTE (OpenAI Codex via MCP, read-only) re-julga em loop ate APPROVED. O mesmo modelo que planeja a critica nao pode ser o unico a julga-la — o cross-provider pega o ponto-cego do same-model. Veredicto FINAL = duplo sign-off.

Invocacao

/ag-adversario docs/specs/issue-42-spec.md
/ag-adversario docs/specs/auth-refactor-spec.md --deep
/ag-adversario --prd docs/specs/dashboard-prd.md

Como funciona

Fase 1: Carregar contexto

  1. Ler o documento (SPEC ou PRD) do $ARGUMENTS
  2. Ler project-context.md se existir (para entender decisoes do projeto)
  3. Ler ADRs relevantes (se referenciados na SPEC)
  4. Identificar stack e dependencias externas

Fase 2: Analise adversarial (5 lentes)

Analisar o design atraves de 5 lentes:

Lente 1: Input Malicioso
  • O que acontece com input vazio? Null? Undefined? String de 10MB?
  • Payloads de XSS/SQL injection nas entradas de usuario?
  • Caracteres unicode, emoji, RTL text nos campos de texto?
  • IDs negativos, zero, MAX_INT, UUIDs invalidos?
  • Arquivos de upload: 0 bytes, 10GB, extensao falsificada, virus?
Lente 2: Timing e Concorrencia
  • Dois usuarios editando o mesmo recurso simultaneamente?
  • Request que demora 30s — usuario clica "submit" de novo?
  • Webhook chega ANTES do registro existir no banco?
  • Cron job roda durante deploy (banco em estado inconsistente)?
  • Rate limit: o que acontece com o request 101 de 100?
Lente 3: Dependencias Externas
  • API terceira retorna 500 — qual o fallback?
  • API terceira muda formato do response sem avisar?
  • API terceira fica fora por 2 horas — queue/retry ou dados perdidos?
  • Credenciais expiram no meio de uma operacao batch?
  • Latencia da API terceira vai de 100ms para 5s — timeout configurado?

Read the full file on GitHub · 276 lines

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. 2mo ago First seen · 276 lines · 93 tokens per session scan A ed7cc619a7b0

Subscribe to this mod's changes

ag-adversario is a skill published in the GitHub repository andregusman-raiz/a-gusman-claude (18 stars, last pushed 1mo ago), licensed MIT. It adds 93 tokens to every session and 3,349 once invoked, about $0.0004 per session on Opus 5.5. 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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