peer-superset

Coordinate two reviewer agents to generate and merge independent reviews into a consensus artifact set.

9|Updated Apr 2, 2026
One-click install
npx skills add https://github.com/dazzaji/interlateral_agents --skill peer-superset
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: peer-superset
Source: https://github.com/dazzaji/interlateral_agents/tree/main/.claude/skills/peer-superset
Command: npx skills add https://github.com/dazzaji/interlateral_agents --skill peer-superset

SYSTEM DOCUMENTATION & REQUIREMENTS

💡 This Skill includes scripts (resource) and references (resource) components.

What problem does it solve?

Two independent reviewers generate original analyses and then merge them into a single consensus superset of findings and revision requests.

Core Features & Use Cases

  • Independent dual critiques and red-team style evaluation
  • Structured synthesis producing a unified set of revision requests
  • Artifact-driven workflow with clear phase boundaries and done markers

Quick Start

Instruct the two agents to independently review the matter, then exchange artifacts to produce a merged consensus superset.

Frequently Asked Questions about peer-superset

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How does two-agent peer review improve documentation consensus?

Two-agent peer review improves documentation consensus by coordinating independent reviewer agents to generate separate original critiques, which are then merged into a unified superset of findings and revision requests.

What is the best way to run independent dual critiques before merging artifact reviews?

The best way to run independent dual critiques is to instruct two agents to review artifacts independently, exchange their original outputs, and then synthesize the results into a single consensus superset under a designated output directory.

Can I use structured peer-review for sprint specs and deployment plans?

Structured peer-review can be applied to sprint specs, runbooks, and deployment plans, generating artifact-driven outputs with clear phase boundaries and standard done markers for any scenario benefiting from dual critique before synthesis.

How do I generate a consensus superset from separate agent reviews?

You generate a consensus superset by having two independent agents produce original reviews, exchange their artifacts, and then combine the separate findings into a single merged set of revision requests.

Does this dual-critique workflow require external dependencies?

This dual-critique workflow requires no external dependencies, operating entirely through phase-based prompts and direct mesh communications to produce artifact-based outputs with standard done markers.

What are the limitations of using independent agent synthesis for artifact management?

A limitation of independent agent synthesis is that it requires clear phase boundaries and strict artifact management to ensure the two original reviews are genuinely independent before the consensus exchange and merging occur.