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VerifiMind™ PEAS — Wiki

The Trust Layer for the Agentic Web

Multi-model AI validation that breaks through single-model bias and hallucination with structured, diverse perspectives under human direction.

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Overview

VerifiMind™ PEAS (Prompt Engineering Attribution System) is a validation-first methodology for ethical and secure application development. It transforms your vision into validated, ethical, secure applications through systematic multi-model AI orchestration — from concept to deployment, with human-centered wisdom validation.

Powered by the Genesis Prompt Engineering Methodology, VerifiMind PEAS orchestrates a council of specialized AI agents (the X-Z-CS RefleXion Trinity) under human direction to deliver objective, validated results.

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Quick Start

Connect via MCP (Recommended)

Claude Code (Terminal):

claude mcp add -s user verifimind -- npx -y mcp-remote https://verifimind.ysenseai.org/mcp/

VS Code / Cursor (.vscode/mcp.json or .cursor/mcp.json):

{
  "servers": {
    "verifimind": {
      "url": "https://verifimind.ysenseai.org/mcp/",
      "transport": "streamable-http"
    }
  }
}

Claude Desktop (Config file):

{
  "mcpServers": {
    "verifimind": {
      "command": "npx",
      "args": ["-y", "mcp-remote", "https://verifimind.ysenseai.org/mcp/"]
    }
  }
}

Important: Use streamable-http transport and always include the trailing slash /mcp/. Full troubleshooting: MCP Server Troubleshooting Guide

Try the Demo

• Wisdom Canvas: HuggingFace Space
• Health Check: verifimind.ysenseai.org/health

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The Genesis Methodology (5-Step Process)

The Genesis Prompt Engineering Methodology is a systematic 5-step process for multi-model AI validation:

Step	Name	Description
1	Initial Conceptualization	Human defines the problem; AI generates initial concepts
2	Critical Scrutiny	Multiple AI models validate and challenge each other
3	External Validation	Independent AI analysis confirms systematic approach
4	Synthesis	Human orchestrates the final synthesis
5	Iteration	Recursive refinement and continuous improvement

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The X-Z-CS RefleXion Trinity

VerifiMind PEAS uses three specialized AI agents, each with a distinct role:

Agent	Role	Model	Purpose
X (Analyst)	Market Intelligence	Perplexity Sonar	Real-time research, competitive analysis, market validation
Z (Guardian)	Ethics & Safety	Claude	Ethical review, Z-Protocol compliance, veto power
CS (Security)	Security Validator	Gemini 2.5-flash	4-Stage Security Verification Protocol (Genesis v3.1)

CS Agent v3.1: 4-Stage Security Verification Protocol

Every security finding passes through a mandatory pipeline:

1. Detection — Automated scanning identifies potential findings
2. Self-Examination — Every finding argued FOR and AGAINST before escalation
3. Severity Rating — CRITICAL / HIGH / MEDIUM / LOW with confidence scoring
4. Human Review — Human oversight is always the final stage

Full protocol: docs/security/CS_AGENT_VERIFICATION_PROTOCOL.md

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MCP Tools (10 Total)

Core Validation Tools (4)

Tool	Description
consult_agent_x	Market intelligence and competitive analysis
consult_agent_z	Ethical review with Z-Protocol compliance
consult_agent_cs	Security assessment with 4-Stage Protocol
run_full_trinity	Complete X-Z-CS validation pipeline

Template Library Tools (6)

Tool	Description
list_templates	Browse 19 pre-built prompt templates
get_template	Retrieve a specific template
fill_template	Fill template with custom variables
list_template_categories	Browse template categories
search_templates	Search templates by keyword
get_template_usage_examples	Get usage examples

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Verified Metrics

Metric	Value
Verified Consultation Hours	1,190+ (all-time)
Total Users	530+
MCP Integration Rate	85%+
MCP Tools	10 (4 core + 6 template)
Prompt Templates	19 pre-built
Days of Development	87+

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Platforms & Access

Platform	Type	Link	Status
GCP Cloud Run	Production API	verifimind.ysenseai.org	✅ LIVE
Official MCP Registry	Registry	registry.modelcontextprotocol.io	✅ LISTED
Landing Page	Showcase	verifimind.io	✅ LIVE
HuggingFace	Demo	YSenseAI/verifimind-peas	✅ LIVE

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Use Cases

For AI Builders

Validate concepts before building. Identify ethical risks early. Get security recommendations. Avoid costly mistakes.

For Investors

Assess AI startup risks. Evaluate ethical implications. Understand market potential. Due diligence support.

For Researchers

Study AI ethics in practice. Analyze validation patterns. Contribute to methodology. Publish findings.

For Policymakers

Understand AI risks. Inform regulation. Identify harmful patterns. Support responsible AI.

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Publications & Citations

Publication	DOI
Genesis v2.0 White Paper	10.5281/zenodo.17972751
MACP & LEP Protocol	10.5281/zenodo.18504478

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Part of the YSenseAI™ Ecosystem

VerifiMind™ PEAS is the validation engine within the broader YSenseAI™ ecosystem — an ethical AI attribution infrastructure that ensures consent, transparency, and fair compensation in AI-generated content.

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Community

• GitHub Discussions: Ask questions, share ideas
• Issues: Report bugs or request features
• X (Twitter): @creator35lwb

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License

MIT License — See LICENSE for details.

© 2025-2026 YSenseAI™ | VerifiMind™ PEAS