cross-model-plan-review

source	shipped
name	cross-model-plan-review
description	Use when a plan or high-risk approach needs independent review from another model or CLI
type	colony
domains	["review","cross-model","quality"]
agent_roles	["architect","auditor"]
workflow_triggers	["plan","continue"]
task_keywords	["cross-model","second opinion","peer review","plan review","external review"]
priority	normal
version	1.0

Cross Model Plan Review

Purpose

No single AI sees every blind spot. This skill dispatches colony plans to external AI CLIs (Claude, GPT, Gemini, etc.) for independent review, then converges their feedback into a unified assessment. Different models catch different issues.

When to Use

• User says "get a second opinion" or "cross-review this plan"
• Before executing a complex or high-risk phase
• Architect wants validation of a technical approach
• Quality gate before milestone execution

Instructions

1. Plan Packaging

Package the plan for external review:
  1. Extract PLAN.md content
  2. Include relevant PROJECT.md context
  3. Include applicable ADRs and specs
  4. Strip colony-internal references (pheromones, etc.)
  5. Add review prompt with evaluation criteria

2. Review Dispatch

Send to available AI CLIs:
  
  For each available CLI:
    1. Format plan in that CLI's preferred input style
    2. Include evaluation rubric:
       - Completeness: Are all requirements addressed?
       - Risks: What could go wrong?
       - Alternatives: Better approaches exist?
       - Dependencies: Missing dependencies?
       - Testing: Adequate verification plan?
    3. Request structured feedback format
    4. Set response expectations (severity, specificity)

3. Feedback Collection

Each reviewer returns:
{
  "reviewer": "{model_name}",
  "concerns": [
    {
      "severity": "HIGH|MEDIUM|LOW",
      "category": "completeness|risk|alternative|dependency|testing",
      "description": "{what the concern is}",
      "suggestion": "{how to address it}"
    }
  ],
  "overall_assessment": "APPROVE|CONDITIONAL|REJECT",
  "summary": "{brief overall opinion}"
}

4. Convergence Analysis

Merge feedback from all reviewers:
  1. Identify consensus concerns (raised by 2+ reviewers)
  2. Identify unique concerns (raised by only one reviewer)
  3. Rank by severity and consensus weight
  4. Generate conflict report where reviewers disagree

  Convergence report:
  - UNANIMOUS: All reviewers agree -> high confidence
  - MAJORITY: 2+ of 3 agree -> moderate confidence
  - SPLIT: No clear agreement -> needs user judgment

5. Review Report

 CROSS-MODEL REVIEW -- {plan_name}
   Reviewers: {list of models}
   
   Consensus Concerns ({count}):
    [HIGH] {concern} -- agreed by {reviewer_list}
    [MED]  {concern} -- agreed by {reviewer_list}
   
   Unique Concerns ({count}):
    [MED]  {concern} -- only from {model}
   
   Disagreements ({count}):
    {topic}: {model_A} says X, {model_B} says Y
   
   Overall: {APPROVE|CONDITIONAL|REJECT}
   Recommendation: {action based on convergence}

6. Re-Planning Loop

If HIGH concerns exist:
  1. Surface concerns to user
  2. User chooses: address concerns or proceed
  3. If address: re-plan affected sections
  4. Re-submit for review (max 3 cycles)
  5. Stop when no HIGH concerns remain

Key Patterns

• Independent reviews: Reviewers don't see each other's feedback until convergence.
• Convergence over averaging: Consensus concerns are more reliable than any single opinion.
• Disagreement is data: Where models disagree reveals genuine ambiguity.
• Limited cycles: Re-review caps at 3 to prevent infinite refinement.

Output Format

 REVIEW | {plan_name} | {reviewer_count} reviewers
   Consensus: {unanimous|majority|split}
   HIGH concerns: {count} | MED: {count} | LOW: {count}
   Verdict: {APPROVE|CONDITIONAL|REJECT}

Examples

Clean review:

"3 reviewers (Claude, GPT-4, Gemini). All APPROVE. 2 MEDIUM concerns raised by 2/3 reviewers. Addressing: adding error handling for edge case in wave 3."

Split review:

"3 reviewers. APPROVE/CONDITIONAL/REJECT. Major disagreement: Claude recommends microservices, GPT recommends monolith. Surfacing to user for architecture decision."