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.
npx skills add patrickdappollonio/claude-plugins --skill adversarial-reviewgit clone --depth 1 https://github.com/patrickdappollonio/claude-pluginsWrote 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.
[](https://agentmods.dev/skills/patrickdappollonio/claude-plugins/adversarial-review)<a href="https://agentmods.dev/skills/patrickdappollonio/claude-plugins/adversarial-review"><img src="https://agentmods.dev/badge/skills/patrickdappollonio/claude-plugins/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.
<a href="https://agentmods.dev/skills/patrickdappollonio/claude-plugins/adversarial-review"><img src="https://agentmods.dev/badge/skills/patrickdappollonio/claude-plugins/adversarial-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>- NVIDIA SkillSpector warn
SkillSpector: 2 findings, 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 Anti-Refusal · line 211 Skill instructs the agent to omit warnings, disclaimers, or ethical commentary. Stripping safety caveats hides risk from the user and is a common jailbreak preamble.Fix: Remove instructions that suppress warnings, disclaimers, or ethical commentary. Let the agent surface safety-relevant caveats to the user.
- medium Prompt Injection · line 50 Subtle instructions detected that may alter agent decision-making or introduce hidden biases.Fix: Review content for implicit steering or bias. Ensure instructions are explicit and align with the skill's stated purpose.
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.
| Model | Per session | Once invoked |
|---|---|---|
| Fable 5.1 | $0.00223 | $0.16818 |
| Opus 5.5 | $0.00089 | $0.06727 |
| Sonnet 5.5 | $0.00045 | $0.03364 |
| Haiku 4.5 | $0.00022 | $0.01682 |
Grade A, and why
adversarial-review scanned grade A with 1 finding 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 5d 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.
Subtle steeringlowPrompt injection
Instructions that bias recommendations or shape behaviour without the user noticing.
Both come from a reviewer trusting a document instead of reasoning. The brief tells reviewers **what to look at**. It must never tell them **what to conclude**. Downgraded: this mod is about security review, or the phrase is quoted, so it is likely naming the pattern rather than instructing it.
How it starts
The opening of the file, as written. The whole thing — 507 lines — stays where its author put it; the contents beside it link to each section on GitHub.
Adversarial Review
Overview
Run a panel of independent, hostile reviewers against a change. Each reviewer assumes the code is broken and tries to prove it, from a single narrow angle. Because each runs as a fresh subagent, none of them inherit the main session's reasoning or the author's rationalizations — that is the whole point. A change that "looks fine" to the person who wrote it (or to the assistant that helped write it) gets attacked from 17 directions by reviewers who were never told why it should work.
This is the full panel, and it is token-heavy on purpose: 17 reviewers plus two gate agents, each reading the diff and the surrounding code. If the user asked for a smaller, quicker, lighter, or cheaper review, stop and run the adversarial-review-quick skill instead; for up to about 500 lines, or to re-check the fixes from an earlier review, run adversarial-review-fast (one reviewer). Run this full panel when the user asks for a thorough or complete review, or when no full review has run on substantial work — over 2000 changed lines, more than one subsystem, or a database schema, sign-in, concurrency, or outside-service change. A plan is sized by what it would build: when the change under review is a plan document, count the code its tickets would touch once built, never the plan's own lines — diagrams, examples, and questions make a plan long without making the change large.
Core principle: the orchestrator gathers the change and the brief once, hands each reviewer the raw change, the brief, and a charter, collects findings, a separate verifier confirms each finding is real, and a separate validator confirms each proposed fix actually works — before anything reaches the user.
Companion files — read them first
question-format.md sits next to this file and defines the one shape every end-of-review question takes: the template, what goes in each part, the word cap, the alternatives, and the one-per-message rule. review-snapshot.md sits beside it and defines how to record the state this review looked at, so a later adversarial-review-fast run can review only what changed since. Read both in full the first time this skill runs in a session, before step 1; read review-snapshot.md again at step 1 and question-format.md again at the STOP step. The summaries in those steps are reminders of text you have already read, never a substitute for it.
What ships with it
2 files 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.
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.
- 5d ago Changed · +2 lines 339b6c515834
- 6d ago Changed 9d4e8f577fa6
- 9d ago Changed e41f0934fef8
- 17d ago Changed f59cd97ca5f8
- 18d ago Changed · +5 lines ecf48d070cf7
- 21d ago Changed · +25 tokens per session ca65934135c5
- 28d ago Changed · +4 lines fa882fdd1936
- 1mo ago Changed 04e4d94f0a48
- 1mo ago Changed · +2 lines 955f4d02086e
- 1mo ago First seen · 494 lines · 198 tokens per session scan A 969b7c741079
adversarial-review is a skill published in the GitHub repository patrickdappollonio/claude-plugins (9 stars, last pushed yesterday), licensed MIT. It adds 223 tokens to every session and 16,818 once invoked, about $0.0009 per session on Opus 5.5. A static security scan graded it A with 1 finding (subtle steering). No closer match exists in the catalogue, so it is treated as the original; first seen 2026-08-31.
adversarial-review compared with skills from other repositories
| Mod | Type | Stars | Last push | Licence | Scan | Tokens | Works with |
|---|---|---|---|---|---|---|---|
| adversarial-reviewpatrickdappollonio/claude-plugins | Skill | 9 | yesterday | MIT | A | 223 | Claude Code |
| autoreviewopenclaw/clawhub | Skill | 9.5k | yesterday | MIT | A | 17 | Claude Code, Codex |
| agent-teams-simplify-and-hardenLeoYeAI/openclaw-master-skills | Skill | 2.2k | 6d ago | MIT | A | 115 | Claude Code, Codex |
| omh-code-reviewrlaope/oh-my-hermes | Skill | 5.2k | yesterday | MIT | A | 48 | Claude Code, Codex |
| revdiff-planumputun/revdiff | Skill | 945 | yesterday | MIT | A | 84 | Claude Code |
| explore-unknownsdzhng/skills | Skill | 1.4k | 2d ago | MIT | A | 162 | Claude Code, Codex |
| re0-mergeLilMGenius/paperthin | Skill | 1.4k | 4d ago | MIT | A | 70 | Claude Code |