adversarial-reviewer

adversarial-reviewer is a skill for Claude Code from jackfranklin/dotfiles. It costs 41 tokens per session (487 once invoked), scanned A, original, MIT.

An adversarial review guide for examining planned or proposed code changes for security, correctness, unusual inputs, timing problems, and boundary cases.

In plain words
What is it for?
Use it when planning changes or reviewing parsers, serializers, user-input handling, state transitions, and code involving sanitization, injection, encoding, races, or stale data.
Why use it?
It challenges happy-path assumptions and helps find failures before they reach production.

Skill for Claude Code

Written for Claude Code: disable-model-invocation in frontmatter.

Good fit Use it when planning changes or reviewing parsers, serializers, user-input handling, state transitions, and code involving sanitization, injection, encoding, races, or stale data.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/jackfranklin/dotfiles/adversarial-reviewer
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 jackfranklin/dotfiles --skill adversarial-reviewer
Clone the repo
git clone --depth 1 https://github.com/jackfranklin/dotfiles

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 adversarial-reviewer

README.md
[![agentmods](https://agentmods.dev/badge/skills/jackfranklin/dotfiles/adversarial-reviewer.svg)](https://agentmods.dev/skills/jackfranklin/dotfiles/adversarial-reviewer)
Your own site
<a href="https://agentmods.dev/skills/jackfranklin/dotfiles/adversarial-reviewer"><img src="https://agentmods.dev/badge/skills/jackfranklin/dotfiles/adversarial-reviewer.svg" alt="Measured on agentmods" height="20"></a>
Per session 41 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 487 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 high

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 →

  • high Memory Poisoning · line 27
    Skill manipulates agent memory, state, or stored context. Memory corruption can alter personality, override safety rules, or cause unpredictable behavior.
    Fix: Protect agent memory and state from modification by untrusted content. Use read-only memory for critical instructions and validate all state changes.
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 $0.00041 $0.00487
Opus 5 $0.00020 $0.00244
Sonnet 5 $0.00008 $0.00097
Haiku 4.5 $0.00004 $0.00049

Measured 8d ago against content hash 4383f57559d0, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-08, from the pricing page.

Security

Grade A, and why

adversarial-reviewer 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 8d 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.

claude/skills/adversarial-reviewer/SKILL.md · 42 lines

What it actually says

Adversarial Audit

Overview

Perform a rigorous, adversarial review of proposed logic or changes to prevent happy-path bias. Identify security vulnerabilities, parsing anomalies, state desyncs, and boundary errors before writing code.

When to Use

Use when:

  • Planning a code change or drafting an implementation plan.
  • Reviewing code diffs for security, correctness, and edge cases.
  • Writing parsers, serializers, UI components handling user input, or state transitions.

Core Pattern

Audit the changes against these four hazard vectors:

  1. Input & Sanitization

    • Control Characters: Are special syntactical characters (e.g. *, _, \, ` in markdown; <, >, & in HTML) escaped or sanitized?
    • Injection: Can scripts, event handlers, or harmful protocol schemes (javascript:) be injected?
    • Encoding: How are Unicode characters, surrogate pairs, or invalid octets handled?
  2. State & Concurrency

    • UI & App Desync: Can fast user interactions (e.g. double clicks) trigger duplicate requests or corrupt state?
    • Race Conditions: How does the system behave if async responses return out of order?
    • Caching: Is stale cache cleared or invalidated?
  3. Boundary Values

    • Inputs: Handle null, undefined, empty strings, extremely large payloads, or deeply nested structures safely.
    • Errors: Ensure timeouts, network dropouts, or permission rejections fail gracefully instead of crashing or leaking data.
  4. Resource Lifecycle

    • Leaks: Clean up active event listeners, timers, file handles, or network sockets when the component unmounts.

Common Mistakes

  • Hacky regular expressions: Using naive regex for HTML/markdown escaping or sanitization instead of standard libraries/well-tested parsers.
  • Silent failure: Swallowing errors without logging or notifying the user.
  • Happy-path testing: Writing unit tests that only cover valid inputs.
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. 8d ago First seen · 42 lines · 41 tokens per session scan A 4383f57559d0

Subscribe to this mod's changes

adversarial-reviewer is a skill published in the GitHub repository jackfranklin/dotfiles (254 stars, last pushed 6d ago), licensed MIT. It adds 41 tokens to every session and 487 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 2026-08-30.