Xpersona

Crawler Summary

cross-model-review answer-first brief

Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- name: cross-model-review description: Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- Cross-Model Review Adversarial review workflow: the author model never reviews its own work. A different model catches blind spots, naming mismatch Capability contract not published. No trust telemetry is available yet. Last updated 2/24/2026.

Freshness

Last checked 2/24/2026

Best For

cross-model-review is best for read workflows where OpenClaw compatibility matters.

Not Ideal For

Contract metadata is missing or unavailable for deterministic execution.

Evidence Sources Checked

editorial-content, GITHUB OPENCLEW, runtime-metrics, public facts pack

CardFactsSnapshotContractTrust
Claim this agent
Agent DossierGitHubSafety: 89/100

cross-model-review

Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- name: cross-model-review description: Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- Cross-Model Review Adversarial review workflow: the author model never reviews its own work. A different model catches blind spots, naming mismatch

OpenClawself-declared

Public facts

4

Change events

1

Artifacts

0

Freshness

Feb 24, 2026

Verifiededitorial-contentNo verified compatibility signals

Capability contract not published. No trust telemetry is available yet. Last updated 2/24/2026.

Trust evidence available

Trust score

Unknown

Compatibility

OpenClaw

Freshness

Feb 24, 2026

Vendor

Rogermzy

Artifacts

0

Benchmarks

0

Last release

Unpublished

Executive Summary

Key links, install path, and a quick operational read before the deeper crawl record.

Verifiededitorial-content

Summary

Capability contract not published. No trust telemetry is available yet. Last updated 2/24/2026.

View Source

Setup snapshot

git clone https://github.com/rogermzy/cross-model-review.git
  1. 1

    Setup complexity is LOW. This package is likely designed for quick installation with minimal external side-effects.

  2. 2

    Final validation: Expose the agent to a mock request payload inside a sandbox and trace the network egress before allowing access to real customer data.

Evidence Ledger

Everything public we have scraped or crawled about this agent, grouped by evidence type with provenance.

Verifiededitorial-content
Vendor (1)

Vendor

Rogermzy

profilemedium
Observed Feb 24, 2026Source linkProvenance
Compatibility (1)

Protocol compatibility

OpenClaw

contractmedium
Observed Feb 24, 2026Source linkProvenance
Security (1)

Handshake status

UNKNOWN

trustmedium
Observed unknownSource linkProvenance
Integration (1)

Crawlable docs

6 indexed pages on the official domain

search_documentmedium
Observed Apr 15, 2026Source linkProvenance

Release & Crawl Timeline

Merged public release, docs, artifact, benchmark, pricing, and trust refresh events.

Self-declaredagent-index

Docs Update

Docs refreshed: Sign in to GitHub · GitHub

search_documentmedium

Fresh crawlable documentation was indexed for the official domain.

Observed Apr 15, 2026

Artifacts Archive

Extracted files, examples, snippets, parameters, dependencies, permissions, and artifact metadata.

Self-declaredGITHUB OPENCLEW

Extracted files

0

Examples

3

Snippets

0

Languages

typescript

Parameters

Executable Examples

text

Review [FILE_PATH] in the context of the full codebase. Check:

1. Data model: well-designed? Missing fields? Redundancies?
2. API design: makes sense for frontend/consumer needs?
3. Conflicts with existing code/schema?
4. Integration: realistic given current architecture?
5. UX/DX issues?
6. What's missing or could go wrong?

Read relevant context files: [LIST_CONTEXT_FILES]

Output structured review:
- LGTM: what's good
- CONCERNS: numbered, with file references
- SUGGESTIONS: numbered, with file references
- MISSING: what's not covered

bash

# PTY required for Codex CLI
codex exec "Review [file] in context of codebase. [prompt]. Read: [files]. Output: LGTM / CONCERNS / SUGGESTIONS."

python

sessions_spawn(
  task="Review [file] against [context files]. Output: LGTM / CONCERNS / SUGGESTIONS / MISSING.",
  label="cross-review"
)

Docs & README

Full documentation captured from public sources, including the complete README when available.

Self-declaredGITHUB OPENCLEW

Docs source

GITHUB OPENCLEW

Editorial quality

ready

Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- name: cross-model-review description: Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers. --- Cross-Model Review Adversarial review workflow: the author model never reviews its own work. A different model catches blind spots, naming mismatch

Full README

name: cross-model-review description: Cross-model adversarial review for plans, code, and architecture. After one model creates a plan or writes code, a different model reviews it for blind spots, then feedback is incorporated. Prevents single-model echo chambers.

Cross-Model Review

Adversarial review workflow: the author model never reviews its own work. A different model catches blind spots, naming mismatches, missing edge cases, and architectural flaws.

When to Use

  • After creating architecture plans, feature specs, or migration plans
  • After writing significant code changes (>100 lines)
  • Before executing irreversible changes (DB migrations, deployments)
  • When the user explicitly asks for a review
  • Auto-trigger: Any document with "PLAN" or "ARCHITECTURE" in the filename

Reviewer Assignment

| Author | Reviewer | |--------|----------| | Claude Opus (main session) | Codex CLI (codex exec) | | Codex CLI (coding-agent) | Claude Opus (via sessions_spawn) | | Claude Sonnet (sub-agent) | Codex CLI (codex exec) |

Rule: whoever wrote it does NOT review it.

Review Prompt Template

Review [FILE_PATH] in the context of the full codebase. Check:

1. Data model: well-designed? Missing fields? Redundancies?
2. API design: makes sense for frontend/consumer needs?
3. Conflicts with existing code/schema?
4. Integration: realistic given current architecture?
5. UX/DX issues?
6. What's missing or could go wrong?

Read relevant context files: [LIST_CONTEXT_FILES]

Output structured review:
- LGTM: what's good
- CONCERNS: numbered, with file references
- SUGGESTIONS: numbered, with file references
- MISSING: what's not covered

Execution

Claude authored → Codex reviews

# PTY required for Codex CLI
codex exec "Review [file] in context of codebase. [prompt]. Read: [files]. Output: LGTM / CONCERNS / SUGGESTIONS."
  • pty: true (required for Codex)
  • background: true if expecting >30s
  • timeout: 180
  • workdir: project root so Codex can read context files

Codex authored → Claude reviews

sessions_spawn(
  task="Review [file] against [context files]. Output: LGTM / CONCERNS / SUGGESTIONS / MISSING.",
  label="cross-review"
)

After Review

  1. Summarize findings to user (concern count + key issues, brief)
  2. If user approves → incorporate ALL feedback into the document
  3. Commit with message: "v{N} — incorporate {reviewer} review feedback"
  4. If concerns are severe (data loss, security, breaking changes) → flag to user before proceeding

What Makes a Good Review

  • Specific: "Status uses 'completed' but engine.py uses 'complete'" beats "naming could be better"
  • Actionable: Every concern includes a suggested fix
  • File-referenced: Points to exact files/lines affected
  • Proportionate: Don't bikeshed variable names when architecture is wrong

Contract & API

Machine endpoints, protocol fit, contract coverage, invocation examples, and guardrails for agent-to-agent use.

MissingGITHUB OPENCLEW

Endpoints

Dossier APISnapshot APIContract APITrust API

Contract coverage

Status

missing

Auth

None

Streaming

No

Data region

Unspecified

Protocol support

OpenClaw: self-declared

Requires: none

Forbidden: none

Guardrails

Operational confidence: low

No positive guardrails captured.
Invocation examples
curl -s "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/snapshot"
curl -s "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/contract"
curl -s "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/trust"

Reliability & Benchmarks

Trust and runtime signals, benchmark suites, failure patterns, and practical risk constraints.

Missingruntime-metrics

Trust signals

Handshake

UNKNOWN

Confidence

unknown

Attempts 30d

unknown

Fallback rate

unknown

Runtime metrics

Observed P50

unknown

Observed P95

unknown

Rate limit

unknown

Estimated cost

unknown

Do not use if

Contract metadata is missing or unavailable for deterministic execution.
No benchmark suites or observed failure patterns are available.

Media & Demo

Every public screenshot, visual asset, demo link, and owner-provided destination tied to this agent.

Missingno-media
No screenshots, media assets, or demo links are available.

Related Agents

Neighboring agents from the same protocol and source ecosystem for comparison and shortlist building.

Self-declaredprotocol-neighbors
GITHUB_REPOSactivepieces

Rank

70

AI Agents & MCPs & AI Workflow Automation • (~400 MCP servers for AI agents) • AI Automation / AI Agent with MCPs • AI Workflows & AI Agents • MCPs for AI Agents

Traction

No public download signal

Freshness

Updated 2d ago

OPENCLAW
GITHUB_REPOScherry-studio

Rank

70

AI productivity studio with smart chat, autonomous agents, and 300+ assistants. Unified access to frontier LLMs

Traction

No public download signal

Freshness

Updated 5d ago

MCPOPENCLAW
GITHUB_REPOSAionUi

Rank

70

Free, local, open-source 24/7 Cowork app and OpenClaw for Gemini CLI, Claude Code, Codex, OpenCode, Qwen Code, Goose CLI, Auggie, and more | 🌟 Star if you like it!

Traction

No public download signal

Freshness

Updated 6d ago

MCPOPENCLAW
GITHUB_REPOSCopilotKit

Rank

70

The Frontend for Agents & Generative UI. React + Angular

Traction

No public download signal

Freshness

Updated 23d ago

OPENCLAW
Agent hubMore from this sourceOpenClaw agents
Machine Appendix

Contract JSON

{
  "contractStatus": "missing",
  "authModes": [],
  "requires": [],
  "forbidden": [],
  "supportsMcp": false,
  "supportsA2a": false,
  "supportsStreaming": false,
  "inputSchemaRef": null,
  "outputSchemaRef": null,
  "dataRegion": null,
  "contractUpdatedAt": null,
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Invocation Guide

{
  "preferredApi": {
    "snapshotUrl": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/snapshot",
    "contractUrl": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/contract",
    "trustUrl": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/trust"
  },
  "curlExamples": [
    "curl -s \"https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/snapshot\"",
    "curl -s \"https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/contract\"",
    "curl -s \"https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/trust\""
  ],
  "jsonRequestTemplate": {
    "query": "summarize this repo",
    "constraints": {
      "maxLatencyMs": 2000,
      "protocolPreference": [
        "OPENCLEW"
      ]
    }
  },
  "jsonResponseTemplate": {
    "ok": true,
    "result": {
      "summary": "...",
      "confidence": 0.9
    },
    "meta": {
      "source": "GITHUB_OPENCLEW",
      "generatedAt": "2026-04-16T23:43:05.565Z"
    }
  },
  "retryPolicy": {
    "maxAttempts": 3,
    "backoffMs": [
      500,
      1500,
      3500
    ],
    "retryableConditions": [
      "HTTP_429",
      "HTTP_503",
      "NETWORK_TIMEOUT"
    ]
  }
}

Trust JSON

{
  "status": "unavailable",
  "handshakeStatus": "UNKNOWN",
  "verificationFreshnessHours": null,
  "reputationScore": null,
  "p95LatencyMs": null,
  "successRate30d": null,
  "fallbackRate": null,
  "attempts30d": null,
  "trustUpdatedAt": null,
  "trustConfidence": "unknown",
  "sourceUpdatedAt": null,
  "freshnessSeconds": null
}

Capability Matrix

{
  "rows": [
    {
      "key": "OPENCLEW",
      "type": "protocol",
      "support": "unknown",
      "confidenceSource": "profile",
      "notes": "Listed on profile"
    },
    {
      "key": "read",
      "type": "capability",
      "support": "supported",
      "confidenceSource": "profile",
      "notes": "Declared in agent profile metadata"
    }
  ],
  "flattenedTokens": "protocol:OPENCLEW|unknown|profile capability:read|supported|profile"
}

Facts JSON

[
  {
    "factKey": "docs_crawl",
    "category": "integration",
    "label": "Crawlable docs",
    "value": "6 indexed pages on the official domain",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  },
  {
    "factKey": "vendor",
    "category": "vendor",
    "label": "Vendor",
    "value": "Rogermzy",
    "href": "https://github.com/rogermzy/cross-model-review",
    "sourceUrl": "https://github.com/rogermzy/cross-model-review",
    "sourceType": "profile",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:44:03.136Z",
    "isPublic": true
  },
  {
    "factKey": "protocols",
    "category": "compatibility",
    "label": "Protocol compatibility",
    "value": "OpenClaw",
    "href": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/contract",
    "sourceUrl": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/contract",
    "sourceType": "contract",
    "confidence": "medium",
    "observedAt": "2026-02-24T19:44:03.136Z",
    "isPublic": true
  },
  {
    "factKey": "handshake_status",
    "category": "security",
    "label": "Handshake status",
    "value": "UNKNOWN",
    "href": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/trust",
    "sourceUrl": "https://xpersona.co/api/v1/agents/rogermzy-cross-model-review/trust",
    "sourceType": "trust",
    "confidence": "medium",
    "observedAt": null,
    "isPublic": true
  }
]

Change Events JSON

[
  {
    "eventType": "docs_update",
    "title": "Docs refreshed: Sign in to GitHub · GitHub",
    "description": "Fresh crawlable documentation was indexed for the official domain.",
    "href": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceUrl": "https://github.com/login?return_to=https%3A%2F%2Fgithub.com%2Fopenclaw%2Fskills%2Ftree%2Fmain%2Fskills%2Fasleep123%2Fcaldav-calendar",
    "sourceType": "search_document",
    "confidence": "medium",
    "observedAt": "2026-04-15T05:03:46.393Z",
    "isPublic": true
  }
]

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