adversarial-code-review

Reviews branches and pull requests using fresh-context reviewer subagents with execution-based verification.

9|1|Updated Aug 28, 2026
One-click install
npx skills add https://github.com/RisorseArtificiali/skills --skill adversarial-code-review-risorseartificiali
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: adversarial-code-review
Source: https://github.com/RisorseArtificiali/skills/tree/main/skills/adversarial-code-review
Command: npx skills add https://github.com/RisorseArtificiali/skills --skill adversarial-code-review-risorseartificiali

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? Code reviews done by the same context that wrote the code produce rubber-stamp approvals, and reviews done by reading alone produce plausible-sounding findings that don't reproduce. This Skill fixes both by dispatching fresh-context reviewer subagents that attack the change from distinct lenses, then requiring skeptic subagents to reproduce every finding in an isolated git worktree before it counts. ## Core Features & Use Cases - Lens-based reviewer panel: Dispatches correctness, tests, security, simplification, and spec-compliance reviewers in parallel, each with a verbatim adversarial prompt and no access to the author's reasoning. - Execution-based verification: Every finding must be reproduced by a skeptic subagent with a command and observed output in an isolated git worktree; unreproduced findings are reported as PLAUSIBLE, never promoted. - Bounded fix loop: Re-runs each confirmed finding's reproduction against fixes, re-reviews only the fix diff, and caps re-review at two cycles before escalating. - Use Case: Before merging a feature branch, run this Skill to get a structured verdict (BLOCK / FIX-THEN-MERGE / SHIP) with confirmed findings, reproductions, accepted trade-offs, and a mandatory Not covered section. ## Quick Start Ask the agent to adversarially review the current branch against main before merging.

Frequently Asked Questions about adversarial-code-review

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I review a pull request with AI subagents?▼

Dispatch fresh-context reviewer subagents that receive only the diff, a written contract, and an adversarial prompt, never the author's reasoning. Each reviewer attacks from a distinct lens such as correctness, tests, or security, and findings are verified by execution in a git worktree.

How to verify AI code review findings before acting on them?▼

Send every finding to a skeptic subagent whose job is to refute it by executing the failure scenario in an isolated worktree. A finding is only CONFIRMED with a reproduction command and observed output; otherwise it is reported as PLAUSIBLE or dropped as REFUTED.

When should I not use adversarial code review?▼

Skip it for in-flight design decisions, mechanical changes like renames or formatting, and generated code. For diffs under roughly 50 lines with no risky areas, collapse the panel to one correctness reviewer plus one verifier instead of the full panel.

Does this review process require git worktrees?▼

Yes, reviewers and verifiers run in an isolated git worktree so they can build, run, and test the change. If worktrees are unavailable, it falls back to read-only mode in the current checkout and discloses that verification quality is degraded.

Why do AI code reviews produce false positives?▼

Model reviewers generate plausible-sounding findings that do not reproduce because they only read code without executing it. This process treats reviewer output as hypotheses and requires a skeptic to reproduce each one with a command and output before it counts.

How many fix re-review cycles should a branch go through?▼

At most two. Each cycle re-runs the original finding's reproduction against the fix and re-reviews only the fix diff. A branch still producing confirmed findings after two cycles is escalated to the user rather than ground through a third cycle.