Adversarial Review
/install adversarial-review
Adversarial Review
Structured multi-agent review loop. Catches what a single agent misses.
Session store: ~/.openclaw/workspace/reviews/
Process: Init session → spawn Opus reviewers → collect redlines → position on each → produce v2 → deliver
Complexity Self-Assessment
Run this check whenever you produce a substantial document. Score 1 point per signal present. If score ≥ 3, offer the review loop without being asked.
| # | Signal | Points |
|---|---|---|
| 1 | Has multiple interdependent components (failure in one affects others) | 1 |
| 2 | Involves schema changes, migrations, or index design | 1 |
| 3 | Irreversible or expensive to undo (data loss, structural rework) | 1 |
| 4 | Affects production systems, stored data, or external services | 1 |
| 5 | Introduces new abstractions, taxonomies, or data models | 1 |
| 6 | Has a defined sequence of steps where order matters | 1 |
| 7 | Contains security, access control, or permission logic | 1 |
| 8 | Will be acted on by code or agents without further human review | 1 |
| 9 | Document is longer than ~500 lines or covers 3+ distinct systems | 1 |
| 10 | Scott said "let's build this" or "implement this" at any point in the conversation | 1 |
Score 0–2 → skip. Simple doc, don't add noise.
Score 3–6 → offer. "This scores [N]/10 on complexity. Want me to run the review team on it before we act?"
Score 7–10 → strongly recommend. Don't just offer — make the case. "This scores [N]/10 on complexity — multiple interdependent systems, production consequences, hard to reverse. I'd strongly recommend running the review team before we act on this. Today's taxonomy strategy was a 10/10 and the review caught 14 issues including multiple production-breaking bugs."
Quick Reference
| Step | Action |
|---|---|
| 0. Init session | scripts/new-review.sh \x3Cslug> \x3Cpath-to-doc> |
| 1. Choose reviewers | Read references/review-types.md for the right bundle |
| 2. Spawn reviewers | sessions_spawn with model=anthropic/claude-opus-4-6, mode=run — all in parallel |
| 3. Wait | Reviewers auto-announce. Do NOT poll. |
| 4. Save raw output | Write each reviewer result to redlines/reviewer-{role}.md |
| 5. Synthesize | scripts/synthesize.sh \x3Csession-dir> → writes redlines/combined.md |
| 6. Position | AGREE / DISAGREE / MODIFY on every redline → write positions.md |
| 7. Produce v2 | Write output/{slug}-v2.md incorporating accepted changes + rejected appendix |
| 8. Deliver | scripts/cp-output.sh \x3Csession-name> \x3Cdestination> |
Session Directory Structure
~/.openclaw/workspace/reviews/{YYYY-MM-DD}-{slug}/
├── input/
│ └── {original-filename} ← copy of doc being reviewed
├── redlines/
│ ├── reviewer-{role}.md ← raw output per reviewer
│ └── combined.md ← synthesize.sh output (sorted by severity)
├── positions.md ← farsight agree/disagree log
└── output/
└── {slug}-v2.md ← final document
Review Types
| Document Type | Reviewer A | Reviewer B |
|---|---|---|
| Architecture / strategy | Theory & data modeling | Implementation & systems |
| Pipeline / workflow | Sequencing & dependencies | Failure modes & ops |
| Schema / migration | SQL correctness & constraints | Performance & indexes |
| Security design | Threat modeling | Implementation gaps |
| Marketing / positioning | Message clarity & truth | Competitive exposure |
| API / interface design | Consistency & contracts | Consumer experience |
For full persona prompt templates → read references/reviewer-personas.md
For pre-configured bundles → read references/review-types.md
Spawning Reviewers
Spawn ALL reviewers simultaneously — parallel, not sequential. Independent reviewers find different issues.
Model Selection
| Doc Score | Default Model | Rationale |
|---|---|---|
| 7–10 | anthropic/claude-opus-4-6 |
Deep reasoning required; subtle architectural flaws need Opus |
| 3–6 | anthropic/claude-sonnet-4-6 |
Worth trying; structured prompts may close the gap |
A/B testing note: If Sonnet misses a CRITICAL issue that Opus would have caught on a 3–6 doc, upgrade that doc type to Opus permanently. Track findings in references/model-notes.md as patterns emerge.
Key parameters for every reviewer spawn:
model: anthropic/claude-opus-4-6 ← or sonnet for 3-6 scored docs
mode: run
runTimeoutSeconds: 300
label: reviewer-{role}
The task field contains the full reviewer prompt from references/reviewer-personas.md plus the document content to review.
Positioning Rules
For EVERY redline, take an explicit position. No skipping.
| Position | When | Requirement |
|---|---|---|
| AGREE | Critique is correct, change should be made | State what changes |
| DISAGREE | Original design is defensible | Must provide rationale — not just dismissal |
| MODIFY | Issue is real, suggested resolution is wrong | Propose your alternative |
All CRITICAL redlines default to AGREE unless strongly defensible.
At least 1 DISAGREE expected — if zero, you may be rubber-stamping.
Write positions to positions.md in the session directory.
v2 Requirements
- Revision table at the top (what changed and why)
- All AGREE + MODIFY changes incorporated
- Rejected redlines documented in an appendix ("considered and rejected")
- Version bumped, date updated
- Saved to
output/{slug}-v2.md
Quality Bar
A good review session produces:
- ≥2 CRITICAL issues (if zero, reviewers weren't adversarial enough — re-spawn with harder prompt)
- ≥1 DISAGREE from farsight (if zero, consider whether the doc was genuinely perfect or just unchallenged)
- A v2 meaningfully different from v1
Redline Format
**[REDLINE-{TYPE}-{NNN}]** {Section reference}
**Claim:** What the document says
**Challenge:** The specific objection or gap
**Severity:** CRITICAL | MAJOR | MINOR
**Suggested Resolution:** What should change
Full spec → read references/redline-format.md
- 确保已安装 OpenClaw(本地或 Docker 部署)
- 在对话框中输入安装命令:
/install adversarial-review - 安装完成后,直接呼叫该 Skill 的名称或使用
/adversarial-review触发 - 根据 Skill 的参数说明提供必要输入,即可获得结构化输出
Adversarial Review 是什么?
Run a structured adversarial multi-agent review loop on any significant document. Spawns parallel Opus reviewers with different critical lenses, collects str... 它是一个面向 Claude Code / OpenClaw 的 AI Agent Skill 插件,目前累计下载 135 次。
如何安装 Adversarial Review?
在 OpenClaw 或 Claude Code 对话框中运行命令「/install adversarial-review」即可一键安装,无需额外配置。
Adversarial Review 是免费的吗?
是的,Adversarial Review 完全免费,采用 MIT-0 许可证,可自由下载、安装和使用。
Adversarial Review 支持哪些平台?
Adversarial Review 跨平台运行,可在任意部署了 OpenClaw / Claude Code 的环境中使用(cross-platform)。
谁开发了 Adversarial Review?
由 Scott Jensen(@scott3j)开发并维护,当前版本 v1.0.0。