Adversarial code review using opposite-model reviewers to challenge work from distinct critical lenses.
Works with
Spawns 1–3 reviewers (Skeptic, Architect, Minimalist) on the opposing model's CLI to avoid same-model bias and ensure genuine adversarial critique
Reviewers attack based on brain principles and assigned lenses; produces a synthesized verdict without auto-applying changes
Scales reviewer count by change size: 1 reviewer for <50 lines, 2 for 50–200 lines, 3 for 200+ lines or 5+
AI-first code editor with Composer
Before installing skills in Cursor, ensure your development environment meets these requirements:
node --versionadversarial-reviewExecute the skills CLI command in your project's root directory to begin installation:
Fetches adversarial-review from poteto/noodle and configures it for Cursor.
The CLI shows a list of agents. Use arrow keys and space to select Cursor:
Confirm successful installation by checking the skill directory location:
Restart Cursor to activate adversarial-review. Access via /adversarial-review in your agent's command palette.
We perform automated surface-level scans (Gen AI Scanner, Socket, Snyk) during installation. These checks detect common vulnerabilities but do not guarantee complete security. Always review skill source code and verify the publisher's reputation before production use.
Skills execute code in your environment. Always review source, verify the publisher, and test in isolation before production.
Submit your Claude Code skill and start earning
Create detailed user stories, acceptance criteria, and feature specs
Example
Generate user stories for 'password reset feature' with acceptance criteria, edge cases, and test scenarios
Reduce spec writing time by 50%, ensure comprehensive coverage
Research competitors, compare features, identify gaps
Example
Analyze 5 competitor products, create feature comparison matrix, suggest differentiation opportunities
Complete competitive research in 2 hours instead of 2 days
Evaluate features using frameworks (RICE, ICE, Kano) and create prioritized backlogs
Example
Score 20 feature ideas using RICE framework, generate prioritized roadmap with rationale
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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.
Read brain/principles.md. Follow every [[wikilink]] and read each linked principle file.
These govern reviewer judgments.
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.
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" > "$REVIEW_DIR/skeptic.md" 2>/dev/null
Run with run_in_background: true.
Name each output file after the lens: skeptic.md, architect.md, minimalist.md.
Build each reviewer's prompt using the template in references/reviewer-prompt.md.
Before reading reviewer output, log which CLI was used and confirm the output files exist:
echo "reviewer_cli=codex|claude"
ls "$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.
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).
Make data-driven prioritization decisions faster
Draft PRDs, status updates, and stakeholder presentations
Example
Create executive summary of Q3 roadmap, monthly progress report, feature launch announcement
Save 3-5 hours/week on communication overhead
Prerequisites
Time Estimate
30-60 minutes to see productivity improvements
Steps
Common Pitfalls
✓ Do
✗ Don't
💡 Pro Tips
✓ Use when
Use for user story writing, competitive research, roadmap prioritization, stakeholder communication, and PRD drafting. Best for reducing repetitive documentation and research work.
✗ Avoid when
Avoid for strategic product vision (requires deep customer empathy), pricing decisions (needs market and financial expertise), or when face-to-face customer discovery is more valuable than speed.
mattpocock/skills
parcadei/continuous-claude-v3
cursor/plugins
ailabs-393/ai-labs-claude-skills
pproenca/dot-skills
mattpocock/skills
We added adversarial-review from the explainx registry; install was straightforward and the SKILL.md answered most questions upfront.
Keeps context tight: adversarial-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
Keeps context tight: adversarial-review is the kind of skill you can hand to a new teammate without a long onboarding doc.
adversarial-review is among the better-maintained entries we tried; worth keeping pinned for repeat workflows.
adversarial-review reduced setup friction for our internal harness; good balance of opinion and flexibility.
adversarial-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
adversarial-review has been reliable in day-to-day use. Documentation quality is above average for community skills.
Solid pick for teams standardizing on skills: adversarial-review is focused, and the summary matches what you get after install.
Registry listing for adversarial-review matched our evaluation — installs cleanly and behaves as described in the markdown.
Solid pick for teams standardizing on skills: adversarial-review is focused, and the summary matches what you get after install.
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