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ln-310-multi-agent-validator

Run parallel Codex and Gemini reviews to validate Stories, Plans, or Context.

Updated Mar 14, 2026
Search Tags:#workflow#planning#validation#multi-agent#code-quality#AI-review
One-click install
npx skills add https://github.com/0xtaoist/taogate --skill ln-310-multi-agent-validator
Or copy as Structured Prompt for Agent▼
Please help me install this Agent Skill.
Skill: ln-310-multi-agent-validator
Source: https://github.com/0xtaoist/taogate/tree/main/ln-310-multi-agent-validator
Command: npx skills add https://github.com/0xtaoist/taogate --skill ln-310-multi-agent-validator

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

Validates Stories, Plans, and Context through parallel multi-agent review (Codex + Gemini) to surface findings, corrections, and GO/NO-GO decisions before moving work forward.

Core Features & Use Cases

  • Parallel agent orchestration: Run Codex and Gemini reviews in the background to accelerate validation workflows.
  • Unified audit & fix planning: Merge agent findings into a single audit, apply approved fixes, and surface clear next steps.
  • Mode versatility: Validate backlog Stories (mode=story), Plans (mode=plan_review), or arbitrary Context (mode=context) with consistent results.
  • Traceable output: Produce a review summary, risk signals, and a defensible GO/NO-GO verdict for leadership decisions.

Quick Start

Provide a Story ID or Plan/Context input to initiate a parallel multi-agent validation workflow.

Frequently Asked Questions about ln-310-multi-agent-validator

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

FAQPage Schema
How does multi-agent validation work for reviewing project plans?▼

Multi-agent validation runs parallel AI reviews using Codex and Gemini to audit plans, merge findings, and deliver a GO/NO-GO verdict with actionable next steps.

How do I validate user stories before starting development?▼

Validate user stories by providing a Story ID to initiate parallel multi-agent reviews, which surfaces corrections and produces a defensible decision before development begins.

What is the best way to run parallel AI code reviews for go/no-go decisions?▼

Running parallel AI code reviews with Codex and Gemini merges agent feedback into a unified audit, generating risk signals and a clear go/no-go verdict for leadership.

Can I use this multi-agent validator to review arbitrary context?▼

Yes, you can use mode=context to validate arbitrary context, applying the same parallel multi-agent review and cross-review memory to produce a unified audit.

Does the multi-agent validator require specific dependencies or components?▼

No, the multi-agent validator operates with no dependencies or components, relying on MCP references and context-aware prompts to orchestrate parallel agent reviews.

What are the limitations of using parallel agents for code quality review?▼

Parallel agent code quality reviews depend on MCP references and cross-review memory, meaning validation accuracy relies on the provided context quality rather than autonomous code execution.

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