Adversarial Review
SkillAI & modelsAdversarial code review using cross-model approach. Spawns reviewers on the opposing model (Claude uses Codex, Codex uses Claude) to challenge work from distinct critical lenses. Produces a synthesized verdict with findings and lead judgment. Triggers: "adversarial review".
Available today. Use it from your connected AI after setup.
No other account needed.
Connect ahel once, and every AI you use reads what you have installed.
Then ask your AI: use the Adversarial Review skill
What this skill tells your AI
The instructions your AI receives, as published by poteto/noodle in .agents/skills/adversarial-review/SKILL.md and read by ahel’s review.
Spawn reviewers on the opposite model to challenge work. Reviewers attack from distinct lenses grounded in brain principles. The deliverable is a synthesized verdict — do NOT make changes.
Hard constraint: Reviewers MUST run via the opposite model's CLI (codex exec or
claude -p). Do NOT use subagents, the Agent tool, or any internal delegation mechanism as
reviewers — those run on your own model, which defeats the purpose.
Step 1 — Load Principles
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file.
These govern reviewer judgments.
Step 2 — Determine Scope and Intent
Identify what to review from context (recent diffs, referenced plans, user message).
Determine the intent — what the author is trying to achieve. This is critical: reviewers challenge whether the work achieves the intent well, not whether the intent is correct. State the intent explicitly before proceeding.
Assess change size:
| Size | Threshold | Reviewers |
|---|---|---|
| Small | < 50 lines, 1-2 files | 1 (Skeptic) |
| Medium | 50-200 lines, 3-5 files | 2 (Skeptic + Architect) |
| Large | 200+ lines or 5+ files | 3 (Skeptic + Architect + Minimalist) |
Read references/reviewer-lenses.md for lens definitions.
Step 3 — Detect Model and Spawn Reviewers
Create a temp directory for reviewer output:
REVIEW_DIR=$(mktemp -d /tmp/adversarial-review.XXXXXX)
Determine which model you are, then spawn reviewers on the opposite:
If you are Claude — spawn Codex reviewers via codex exec:
codex exec --skip-git-repo-check -o "$REVIEW_DIR/skeptic.md" "prompt" 2>/dev/null
Use --profile edit only if the reviewer needs to run tests. Default to read-only.
Run with run_in_background: true, monitor via TaskOutput with block: true, timeout: 600000.
If you are Codex — spawn Claude reviewers via claude CLI:
claude -p "prompt" > "$REVIEW_DIR/skeptic.md" 2>/dev/null
Run with run_in_background: true.
Name each output file after the lens: skeptic.md, architect.md, minimalist.md.
Build each reviewer's prompt using the template in references/reviewer-prompt.md.
Step 4 — Verify and Synthesize Verdict
Before reading reviewer output, log which CLI was used and confirm the output files exist:
echo "reviewer_cli=codex|claude"
ls "$REVIEW_DIR"/*.md
If any output file is missing or empty, note the failure in the verdict — do not silently skip a reviewer.
Read each reviewer's output file from $REVIEW_DIR/. Deduplicate overlapping findings.
Produce a single verdict using the format in references/verdict-format.md.
Step 5 — Render Judgment
After synthesizing the reviewers, apply your own judgment. Using the stated intent and brain principles as your frame, state which findings you would accept and which you would reject — and why. Reviewers are adversarial by design; not every finding warrants action. Call out false positives, overreach, and findings that mistake style for substance.
Append the Lead Judgment section to the verdict (see references/verdict-format.md).
Signals
- GitHub stars
- 270
- Forks
- 15
- Last commit
- Mar 2026
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adversarial-review-poteto- Source
- github.com/poteto/noodle