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By mistakeknot
Cross-AI peer review — quick (Claude↔Codex), deep (Oracle), council (multi-model), mine (disagreement extraction).
npx claudepluginhub mistakeknot/interagency-marketplace --plugin interpeerCross-AI peer review for Claude Code.
Single-model review catches a lot, but models have blind spots: and they tend to have the same blind spots consistently. interpeer sends your code or documents to a different model for a second opinion, with four escalation modes depending on how much scrutiny you need.
Quick (qinterpeer): sends to Codex CLI for fast Claude↔GPT feedback. Takes seconds, catches surface-level disagreements.
Deep (interpeer): sends to Oracle (ChatGPT 5.2 Pro) with prompt optimization and large context support (~200k tokens). Takes minutes, catches architectural and design issues.
Council (winterpeer): full LLM Council with multi-model consensus. Slow, but useful for critical decisions where you want genuine multi-perspective analysis.
Mine (splinterpeer): the interesting one. Instead of just getting a second opinion, this mode extracts the disagreements between models and converts them into actionable artifacts: tests for disputed behavior, specs for ambiguous requirements, and questions for genuinely unclear design choices. Models disagree about interesting things.
First, add the interagency marketplace (one-time setup):
/plugin marketplace add mistakeknot/interagency-marketplace
Then install the plugin:
/plugin install interpeer
Requires Codex CLI for quick mode and Oracle CLI for deep mode.
/interpeer
Auto-detects which AI tools are available and offers the appropriate modes.
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Based on adoption, maintenance, documentation, and repository signals. Not a security audit or endorsement.
Configurable multi-model code review, plan review, and general review with consensus convergence
Symmetric two-AI peer review using OpenAI Codex CLI. Both AIs review independently in a blind pass, then debate per-issue with terminal states until convergence. Catches significantly more issues than single-pass validation.
Multi-agent adversarial review panel — 4-6 AI reviewers debate your code/plans, then a judge delivers a structured verdict with epistemic labels. Bundles plan-review-integrator for applying review findings back into implementation plans.
Use when you want a delegated second opinion or implementation from GPT (Codex), Gemini, Grok (xAI), or OpenRouter (config-driven, 400+ models) - seven expert subagents (Architect, Plan Reviewer, Scope Analyst, Code Reviewer, Security Analyst, Researcher, Debugger) and bundled ask-gpt/ask-gemini/ask-grok/ask-openrouter/ask-all/consensus commands, advisory (read-only) or implementation (write; Grok and OpenRouter are advisory-only).
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