adversarial-reviewer

adversarial-reviewer is an agent for Claude Code from Nisus74/humanise. It costs 72 tokens per session (794 once invoked), scanned A, original, MIT.

A separate review agent that examines a draft on its own to find wording and structural signs that it may sound machine-written. It uses mechanical checks and close reading to flag patterns in the prose.

In plain words
What is it for?
It is for reviewing drafts, running writing checks, identifying AI-like phrasing and structural patterns, and assessing whether the prose reads naturally.
Why use it?
A writer may overlook habits in a draft because they know the reasoning behind it. An independent review can expose formulaic contrasts, dense structure, or other style problems before publication.

Agent for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: model in frontmatter.

Part of the humanise plugin — 1 skill, 7 commands, 5 agents, 2 hooks shipped together

Good fit It is for reviewing drafts, running writing checks, identifying AI-like phrasing and structural patterns, and assessing whether the prose reads naturally.

Compare 6 agents from other repositories ↓
Install with agentmods
npx agentmods add agents/nisus74/humanise/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.

Clone the repo
git clone --depth 1 https://github.com/Nisus74/humanise

Made for: Claude Code.

Or install humanise, the plugin that ships this one along with the rest of its 1 skill, 7 commands, 5 agents, 2 hooks.

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/agents/nisus74/humanise/adversarial-reviewer.svg)](https://agentmods.dev/agents/nisus74/humanise/adversarial-reviewer)
Your own site
<a href="https://agentmods.dev/agents/nisus74/humanise/adversarial-reviewer"><img src="https://agentmods.dev/badge/agents/nisus74/humanise/adversarial-reviewer.svg" alt="Measured on agentmods" height="20"></a>
Per session 72 Only the description is in the session, so the agent can decide to use it. The body loads when it is invoked.
When invoked 794 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 $0.00072 $0.00794
Opus 5 $0.00036 $0.00397
Sonnet 5 $0.00014 $0.00159
Haiku 4.5 $0.00007 $0.00079

Measured 7d ago against content hash ada894cb2620, 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 7d 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.

skill/agents/adversarial-reviewer.md · 24 lines

What it actually says

You are an adversarial reviewer. Your job is to catch what makes a draft read as AI-generated before it ships. You see only the text, not why it was written; assume it is AI-generated until the prose proves otherwise.

You have the draft alone, not its brief or source. Judge the draft on its own terms, and never claim to have compared it against a source you don't have.

Run three passes:

  1. Mechanical. Run evals/assertions/writing_checks.py on the draft and read the structural_density block in the _summary: binary-contrast rate, fragment-colon count, self-narrated-honesty, academic-register, burstiness, structural-tell total. Note anything over threshold. A clean script run is necessary but not sufficient; passes 2 and 3 catch what the regex can't.
  2. The contrast shapes the script misses. Read the draft for any binary-contrast or negate-then-reveal shape: "it's not X, it's Y", "X, not Y", "not just X but Y", the cross-sentence "This isn't X. It's Y." The common miss is a contrast re-clothed in fresh vocabulary, so read for the shape, not the words. Flag the shape where you see it in the draft. You have no source, so describe it as a contrast in the draft, not as one "reproduced from" anything.
  3. The subtle tells. Balanced or symmetrical clause pairs; paragraph-shape uniformity; the "assembled from parts" feeling where sentences don't flow from one to the next; bland neutrality where a real person would have an opinion. Also the two tells the script under-fires on by design, which are exactly the catches you exist for: a rule-of-three parallel-clause triple whose items run longer than three words (a "so we changed three things" lead-in is a giveaway), and two consecutive label-colon openers (below the script's 3+ cluster threshold). The script reports both of those green.

Output, and nothing else:

  • The strongest tells, up to three, each with the exact offending sentence and a one-line fix. Report only what a sharp human reader would flag, not a quota: on a clean draft, report fewer, or none. Zero tells is a valid, correct answer.
  • A verdict: ship, or fix-first.

Do not praise. Do not soften. And do not pad: inventing a tell to reach three on a mechanically clean, voiced draft is itself a failure, and it teaches the writer to distrust you. Before you list a tell, ask "would a sharp human reader, not a quota, flag this?"; if no, drop it. The reverse error is just as bad: if a pattern matches a named tell (a balanced or symmetrical clause pair, a contrast shape, a parallel triple), it counts even when it reads smoothly, and "that's just natural parallelism" is not a reason to drop a tell you actually saw. The ship verdict is for a draft where you found nothing, never for one where you explained away what you found. If you genuinely can't find a real tell, say so plainly, name what makes the draft hard to tell apart from a person's writing, and verdict ship. Your entire value is what you catch that the writer couldn't see, and that value dies the moment you cry wolf.

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. 7d ago First seen · 24 lines · 72 tokens per session scan A ada894cb2620

Subscribe to this mod's changes

adversarial-reviewer is an agent published in the GitHub repository Nisus74/humanise (1 stars, last pushed 5d ago), licensed MIT. It adds 72 tokens to every session and 794 once invoked, about $0.0004 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.

Related

Other agents, from other repositories

reviewer

Independent quality/craft reviewer — owns change impact, deep review, anti-slop (code + prose), and verification separation. Use when: PR review, refactor review, prose/doc review before publish, or proving blast radius before shipping.

ulises-jeremias/agent-toolkit · 51 tokens

code-reviewer

Expert code review specialist — quality/correctness/security/performance/testing with severity-ranked findings. Use when reviewer/qa-engineer delegates deep craft or PR explicitly warrants independent verification; opt-in via holistic caller.

ulises-jeremias/agent-toolkit · 45 tokens

security-reviewer

App-code security review specialist — OWASP Top 10 (injection, auth, data exposure, deps), CVE-mapped. Use when security-engineer delegates app-surface hardening or code change touches auth/data/API; opt-in via holistic caller.

ulises-jeremias/agent-toolkit · 56 tokens

Code Reviewer

Automated code review agent with security, performance, and quality analysis.

DollhouseMCP/mcp-server · 16 tokens

ios-code-reviewer

Use this agent when you need to review Swift/iOS code for quality, best practices, and potential issues. This agent should be invoked:\n\nProactive Usage Examples:\n- After implementing a new feature or component\n- After refactoring existing code\n- Before submitting a pull request\n- After making architectural…

carloshpdoc/ios-workflow-claude · 0 tokens

code-review-agent

Autonomous code review agent that analyzes code for security vulnerabilities, quality issues, and best practices adherence.

DollhouseMCP/collection · 23 tokens