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Adversarial Review

by Scott Jensen · GitHub ↗ · v1.0.0 · MIT-0
cross-platform ⚠ suspicious
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Install in OpenClaw
/install adversarial-review
Description
Run a structured adversarial multi-agent review loop on any significant document. Spawns parallel Opus reviewers with different critical lenses, collects str...
README (SKILL.md)

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

Usage Guidance
This skill appears to be what it says (a structured review workflow) but there are a few things to check before installing or using it widely: - Missing runtime dependency: synthesize.sh writes and runs a temporary Node.js script and requires the node binary, but the skill does not declare node as a required binary. Ensure node is available on the host or modify the script to use an available runtime. - Model invocation assumption: SKILL.md expects the agent to spawn reviewers with named models (e.g., anthropic/claude-opus-4-6). Confirm your platform provides access to those models or that you are comfortable with the platform's model invocation behavior; otherwise reviewer spawning will fail or behave differently. - Local storage: the skill will copy reviewed documents into ~/.openclaw/workspace/reviews and persist reviewer outputs, positions, and final v2 documents. If you handle sensitive documents, consider storage location, encryption, or cleanup policies. - Self-triggering behavior: the skill instructs agents to run a self-assessment automatically when producing substantial documents. Decide whether you want that behavior enabled by default — if you don't, avoid loading/activating the skill persistently or ensure the agent's skill-eligibility rules prevent automatic runs. If you accept these conditions, the skill is functionally coherent. If you need to trust it with highly sensitive documents, review and, if necessary, modify the scripts (or change the session path) and confirm model access/permissions first.
Capability Analysis
Type: OpenClaw Skill Name: adversarial-review Version: 1.0.0 The bundle contains a critical shell injection vulnerability in `scripts/cp-output.sh` due to the use of `eval` on the `$DESTINATION` argument, which allows arbitrary command execution if the path is influenced by an attacker. Additionally, `SKILL.md` contains 'self-triggering' instructions designed to override the agent's behavior and force the review loop on all substantial documents, a form of prompt injection. While the stated purpose of adversarial document review is legitimate, the combination of high-risk shell patterns and behavioral overrides warrants caution.
Capability Assessment
Purpose & Capability
The name/description match the included files: reviewer persona templates, review-type bundles, and shell/node helper scripts for session init, synthesis, and copying output. However, SKILL.md instructs spawning reviewers using specific high-capability models (e.g., anthropic/claude-opus-4-6) while the skill declares no required environment variables or credentials for model/API access. That may be fine if the hosting platform provides model access, but it is an implicit requirement that isn't documented. Also the skill includes runtime scripts that expect a node runtime (synthesize.sh creates and runs a temporary Node script) but the skill declares no required binaries; this is a clear undeclared dependency.
Instruction Scope
Instructions are detailed and constrained to the review workflow: create a session dir under ~/.openclaw/workspace/reviews, copy the input doc there, spawn reviewers (via sessions_spawn with explicit model/params), write per-reviewer redlines, synthesize combined results, record positions, and produce a v2. This is consistent with the stated purpose. Two things to note: (1) the skill instructs agents to self-trigger the Complexity Self-Assessment whenever producing substantial documents — that grants the skill broad, frequent usage if the host agent honors it; (2) the workflow reads and writes user documents into a home-directory workspace (~/.openclaw), so it will store local copies of any reviewed documents.
Install Mechanism
There is no install spec (instruction-only), which is low-risk normally. However, synthesize.sh dynamically writes and executes a Node.js script (calls node). The skill does not declare 'node' (or npm) as a required binary. If node is not present on the host, synthesis will fail. The lack of declared runtime/binary requirements is an inconsistency that could lead to runtime errors or surprising behavior; the skill also writes temporary files under /tmp and persistent files under the user's home directory (expected for a session store).
Credentials
The skill requests no environment variables, no credentials, and no config paths beyond creating and using ~/.openclaw/workspace/reviews. It does not attempt to read or exfiltrate other system credentials. The lack of any requested API keys is consistent if the platform supplies model invocation capability; if not, model spawning steps may fail silently or require additional platform-level credentials.
Persistence & Privilege
always:false and disable-model-invocation:false (defaults) — the skill is not forced into every agent run, but SKILL.md explicitly urges SELF-TRIGGERING behavior (instructs the agent to run the complexity self-assessment whenever producing substantial documents). That is a behavioral scope request (not a platform-level always:true), and it could lead to frequent automatic usage if the agent honors it. The skill creates and persists session data under ~/.openclaw/workspace/reviews which is expected for its function.
How to Use
  1. Make sure OpenClaw is installed (local or Docker)
  2. Run the install command in chat: /install adversarial-review
  3. After installation, invoke the skill by name or use /adversarial-review
  4. Provide required inputs per the skill's parameter spec and get structured output
Version History
v1.0.0
- Initial release of adversarial-review skill. - Enables structured, multi-agent review loops for significant documents with automated complexity self-assessment. - Spawns parallel Opus reviewers with diverse critical lenses, collects structured redlines, and guides agreement/disagreement and v2 document production. - Provides clear review process, positioning rules, and quality expectations. - Includes comprehensive reviewer type selection and session directory guidelines for workflow transparency.
Metadata
Slug adversarial-review
Version 1.0.0
License MIT-0
All-time Installs 2
Active Installs 2
Total Versions 1
Frequently Asked Questions

What is Adversarial Review?

Run a structured adversarial multi-agent review loop on any significant document. Spawns parallel Opus reviewers with different critical lenses, collects str... It is an AI Agent Skill for Claude Code / OpenClaw, with 135 downloads so far.

How do I install Adversarial Review?

Run "/install adversarial-review" in the OpenClaw or Claude Code chat to install it in one step — no extra setup required.

Is Adversarial Review free?

Yes, Adversarial Review is completely free, licensed under MIT-0. You can download, install and use it at no cost.

Which platforms does Adversarial Review support?

Adversarial Review is cross-platform and runs anywhere OpenClaw / Claude Code is available (cross-platform).

Who created Adversarial Review?

It is built and maintained by Scott Jensen (@scott3j); the current version is v1.0.0.

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