adversarial-review

adversarial-review is a skill for Claude Code from daniyalahmed21/skillforge. It costs 30 tokens per session (237 once invoked), scanned A, original, MIT.

A review workflow that sends a staged code change to two independent reviewers before fixes are applied. A staged change is a Git change selected for the next commit, and a diff is the exact list of edits.

In plain words
What is it for?
Use it to review staged diffs, combine the reviewers' findings, choose how fixes should be applied, and repeat the review when logic changes.
Why use it?
It can uncover bugs and risks before a non-trivial change is committed or merged. Independent reviews reduce the chance that one reviewer's missed issue goes unnoticed.

Skill for Claude Code

Written for Claude Code: shipped in a Claude Code plugin. Also seen: mentions subagents.

Part of the adversarial-review plugin — 1 skill, 2 agents shipped together

Good fit Use it to review staged diffs, combine the reviewers' findings, choose how fixes should be applied, and repeat the review when logic changes.

Compare 6 skills from other repositories ↓
Install with agentmods
npx agentmods add skills/daniyalahmed21/skillforge/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 daniyalahmed21/skillforge --skill adversarial-review
Clone the repo
git clone --depth 1 https://github.com/daniyalahmed21/skillforge

Made for: Claude Code.

Or install adversarial-review, the plugin that ships this one along with the rest of its 1 skill, 2 agents.

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/daniyalahmed21/skillforge/adversarial-review/github.svg)](https://agentmods.dev/skills/daniyalahmed21/skillforge/adversarial-review)
Your own site
<a href="https://agentmods.dev/skills/daniyalahmed21/skillforge/adversarial-review"><img src="https://agentmods.dev/badge/skills/daniyalahmed21/skillforge/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/daniyalahmed21/skillforge/adversarial-review"><img src="https://agentmods.dev/badge/skills/daniyalahmed21/skillforge/adversarial-review.svg" alt="Reviewed on agentmods" width="80" height="20"></a>
Per session 30 Skills are progressive disclosure: only the name and description are preloaded; the body loads when the skill is used.
When invoked 237 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 $0.00030 $0.00237
Opus 5 $0.00015 $0.00118
Sonnet 5 $0.00006 $0.00047
Haiku 4.5 $0.00003 $0.00024

Measured 9d ago against content hash 592a3df7828f, method: parsed. Prices are Anthropic first-party input rates as of 2026-09-09, 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 9d 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.

adversarial-review/skills/adversarial-review/SKILL.md · 18 lines

What it actually says

  1. Capture the change: git diff --staged > /tmp/review.diff. If nothing is staged, stop and tell the user to stage the change first.
  2. Spawn TWO independent adversarial-reviewer subagents in parallel on that diff. Give each ONLY the diff path plus, if this is a refactor/port, the original source as ground truth. Do NOT pass either reviewer your own reasoning or the other reviewer's output.
  3. Collect both bug lists and dedupe them into one list.
  4. Present the deduped list to the user and ask whether to apply fixes yourself (main session) or delegate to the fixer subagent.
  5. After fixes are applied, if any fix touched logic, re-stage and loop back to step 1 on the new diff. Otherwise report done.

Rules: reviewers never edit; the fixer never re-reviews. Keep the roles separate.

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. 9d ago First seen · 18 lines · 30 tokens per session scan A 592a3df7828f

Subscribe to this mod's changes

adversarial-review is a skill published in the GitHub repository daniyalahmed21/skillforge (6 stars, last pushed 2mo ago), licensed MIT. It adds 30 tokens to every session and 237 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.

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