---

MCP Server: Production Deployed

v0.5.0 Foundation — 1,729.5 verified engagement hours | 754 users | 94.6% Value Confirmation Rate | 77.6% MCP integration | BYOK Live + hardened | SessionContext tracing | Error handling v2 | Health endpoint v2 | Smithery removed (self-hosted on GCP) | 205 tests, 55.1% coverage | Z-Protocol approved (9.2/10). Health Check

VerifiMind PEAS is now live and accessible across multiple platforms:

Platform	Type	Access	Status
GCP Cloud Run	Production API	verifimind.ysenseai.org	✅ LIVE
Official MCP Registry	Registry Listing	registry.modelcontextprotocol.io	✅ LISTED
Smithery.ai	Native MCP	Install for Claude Desktop	⚠️ SUNSET (zero impact — self-hosted)
Landing Page	Showcase	verifimind.io	✅ LIVE
Hugging Face	Interactive Demo	YSenseAI/verifimind-peas	✅ LIVE
MACP Research Assistant	Showcase App	macpresearch.ysenseai.org	✅ LIVE

Quick Start

Important: Use streamable-http transport (not http-sse) and always include the trailing slash /mcp/.
📖 Full Multi-Client Setup & Troubleshooting Guide

Claude Code (Terminal command — recommended):

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

Claude Desktop (Edit config file — [macOS](~/Library/Application Support/Claude/claude_desktop_config.json) | Windows):

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

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

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

ChatGPT Codex CLI (~/.codex/config.toml):

[mcp_servers.verifimind]
url = "https://verifimind.ysenseai.org/mcp/"
transport = "streamable_http"

⚠️ Codex CLI v0.98.0 has a known bug with streamable-http. See Troubleshooting Guide for workaround.

OpenAI Agents SDK (Python):

from agents.mcp import MCPServerStreamableHttp
server = MCPServerStreamableHttp(name="VerifiMind", params={"url": "https://verifimind.ysenseai.org/mcp/"})

🎮 Interactive Demo

Common Mistakes

Based on production log analysis (February 2026), these are the most frequent connection errors new users encounter:

Mistake	What Happens	Fix
Visiting the URL in a browser	You see a 406 Not Acceptable error	This is an API, not a website. Use an MCP client (Claude Desktop, Cursor, etc.)
Missing trailing slash /mcp	405 Method Not Allowed	Always use /mcp/ with the trailing slash
Using GET instead of POST	400 Bad Request	MCP protocol requires POST requests with JSON-RPC body
Using http-sse transport	Connection fails	Use streamable-http transport (not http-sse)
Connecting to Smithery proxy	May stop working March 1, 2026	Use the direct URL: https://verifimind.ysenseai.org/mcp/

💡 Quick test: Run curl https://verifimind.ysenseai.org/health — if you see "status": "healthy", the server is up. Then configure your MCP client using the Quick Start instructions above.

⚠️ Smithery.ai Sunset Notice: Smithery.ai's legacy architecture will be sunset on March 1, 2026. If you are currently connecting via server.smithery.ai, please switch to the direct URL https://verifimind.ysenseai.org/mcp/ before that date. All Quick Start instructions above already use the direct URL.

API Keys & BYOK (v0.4.5+)

Platform	API Key Required	Notes
GCP Server / MCP Registry	❌ No (default)	Server-side configured, ready to use
GCP Server (BYOK)	✅ Optional	Pass api_key + llm_provider per tool call to use your own key
HuggingFace Demo	❌ No	Server-side configured
Smithery	✅ Yes (BYOK)	Bring Your Own Key (sunset March 1, 2026)

v0.4.5 BYOK Live — You can now override the default provider on any individual tool call by passing api_key and llm_provider parameters. The server auto-detects key format (e.g., gsk_ → Groq, sk-ant- → Anthropic, sk- → OpenAI). If no key is provided, the server uses its default Gemini/Groq configuration. Triple-validated by Manus AI (6/6), Claude Code (6/6), and CI (175 tests). PR #55

Supported BYOK Providers: Gemini, Groq, OpenAI, Anthropic, Mistral, Ollama, Perplexity

Get FREE API Keys: Google AI Studio | Groq Console

MCP Tools (10 Total)

The VerifiMind MCP server exposes 10 tools organized into two categories: Core Validation (4 tools) and Template Management (6 tools, added in v0.4.0).

Tool	Category	Description
consult_agent_x	Core	Innovation & Strategy analysis (Gemini FREE)
consult_agent_z	Core	Ethics & Safety review with VETO power
consult_agent_cs	Core	Security & Feasibility validation
run_full_trinity	Core	Complete X → Z → CS validation pipeline
list_prompt_templates	Template	List/filter templates by agent, category, tags
get_prompt_template	Template	Retrieve template by ID with full content
export_prompt_template	Template	Export to Markdown or JSON format
register_custom_template	Template	Create custom prompt templates
import_template_from_url	Template	Import from GitHub Gist or raw URL
get_template_statistics	Template	Registry statistics and usage data

Template Library (6 Libraries, 19 Templates)

v0.4.0 introduced the Unified Prompt Template system with pre-built, versioned YAML templates aligned to Genesis Methodology phases.

Library	Agent	Genesis Phase	Templates
startup_validation	X	Phase 1: Conceptualization	3
market_research	X	Phase 1: Conceptualization	3
ethics_review	Z	Phase 2: Critical Scrutiny	3
security_audit	CS	Phase 3: External Validation	3
technical_review	CS	Phase 3: External Validation	3
trinity_synthesis	ALL	Phase 4: Synthesis	4

Templates support custom variables with type validation, export to Markdown/JSON, import from URL, and version control with changelogs. Users can also register custom templates at runtime.

Security Features (v0.3.5+)

All MCP tools include input sanitization to protect against prompt injection, XSS, null byte injection, and input length abuse. The system detects 15+ prompt injection patterns and logs suspicious activity without blocking legitimate requests.

CI/CD Pipeline

Automated testing and security scanning runs on every push to main via GitHub Actions:

• Unit tests and integration tests (Python 3.11)
• Security scanning with Bandit (static analysis) and Safety (dependency audit)
• Coverage reporting with configurable thresholds

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📊 Verified Service Metrics

Cross-validated by FLYWHEEL TEAM (CSO R — Manus AI, COO AY — Antigravity) against raw GCP Cloud Run logs. Scrapers excluded. Conservative rounding applied.

Metric	Value	Methodology
Verified Engagement Hours	1,729.5 (all-time)	Session duration: first-to-last request per user per day. Scrapers excluded via User-Agent audit classification.
Value Confirmation Rate	94.6%	Sessions where user sends follow-up prompt (proof of value received)
Total Users	754	Unique users across all platforms
MCP Integration Rate	77.6%	Programmatic (Node.js + Python) vs. browser traffic by User-Agent header
MCP Tools Available	10 (4 core + 6 template)	Core: consult_agent_x, consult_agent_z, consult_agent_cs, run_full_trinity
Multi-Model Providers	X=Gemini, Z=Groq, CS=Groq	Per-agent provider routing for optimal structured output
BYOK Live	Per-tool-call override	Users can pass own API key + provider on any call (v0.4.5+)
Trinity Quality	_overall_quality: "full"	All 3 agents returning real inference (v0.4.4+)
SessionContext	_session_id tracing	8-char correlation ID per Trinity run (v0.5.0+)
Test Coverage	205 tests, 55.1%	Comprehensive test suite with CI/CD pipeline

Adoption Trajectory (Flying Hours ✈️)

Data through W09 (March 1, 2026). W10+ data collection in progress for v0.7.0 pricing decisions.

Week	Period	Weekly Hours	Cumulative Hours	Users
W02	Jan 06–12	38.0h	38h	21
W03	Jan 13–19	115.3h	153h	55
W04	Jan 20–26	262.4h	416h	96
W05	Jan 27–Feb 02	309.6h	725h	105
W06	Feb 03–09	425.4h	1,151h	117
W07	Feb 10–16	409.0h	1,198h	172
W08	Feb 16–22	404.8h	1,556h	143
W09	Feb 23–Mar 1	198.5h	1,755h	46

Traffic Classification Breakdown

Category	Share	Hours	Description
MCP Client	80.3%	1,409.5h	Tool users via Node.js/Python MCP clients
Human Browser	4.7%	82.9h	Direct web visitors (HuggingFace, landing page)
API Integration	15.0%	262.3h	Programmatic API consumers
Scraper	—	Excluded	Excluded from verified total

Verified Total = MCP + Browser + API. Scrapers excluded. Owner/Bot excluded.

Client Integration by User-Agent

Client	Share	Description
Node.js	65.3%	MCP clients via Claude Code, VS Code, Cursor
Python SDK	20.3%	Python-based MCP integrations
Browser	8.5%	Direct web visitors
Claude/Anthropic	4.5%	Claude Desktop / Claude Code native clients
Other	1.4%	Miscellaneous clients

Key Insight: Over 85% of all traffic is machine-to-machine MCP integration, confirming VerifiMind PEAS is used as an integrated tool in developer workflows — not merely visited as a web demo. This traffic is invisible to traditional web analytics platforms like SimilarWeb.

Data Source: GCP Cloud Run HTTP Load Balancer logs. Audit classification via User-Agent analysis. Owner traffic excluded. Scraper traffic excluded via conservative classification. Full methodology documented in internal reports (Report 024). Last updated: 2026-02-26.

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🌟 What is VerifiMind-PEAS?

VerifiMind-PEAS is a methodology framework, not a code generation platform.

We provide a systematic approach to multi-model AI validation that ensures your applications are:

• ✅ Validated through diverse AI perspectives
• ✅ Ethical with built-in wisdom validation
• ✅ Secure with systematic vulnerability assessment
• ✅ Human-centered with you as the orchestrator

What We Provide

Core Methodology:

• ✅ Genesis Methodology: Systematic 5-step validation process
• ✅ X-Z-CS RefleXion Trinity: Specialized AI agents (Innovation, Ethics, Security)
• ✅ Genesis Master Prompts: Stateful memory system for project continuity
• ✅ Comprehensive Documentation: Guides, tutorials, case studies

Integration Support:

• ✅ Works with any LLM (Claude, GPT, Gemini, Kimi, Grok, Qwen, etc.)
• ✅ Integration guides for Claude Code, Cursor, and generic LLMs
• ✅ No installation required - just read and apply!

What We Do NOT Provide

We are NOT:

• ❌ A code generation platform
• ❌ A web interface for application scaffolding
• ❌ A no-code platform integration
• ❌ An automated deployment system

We ARE:

• ✅ A methodology you apply with your existing AI tools
• ✅ A framework for systematic validation
• ✅ A community of practice for ethical AI development

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🎯 Latest Achievements

v0.5.0 — Foundation: Unbreakable Engine (March 1, 2026)

The v0.5.0 Foundation release makes everything after it possible. SessionContext tracing adds an 8-character _session_id correlation token to every Trinity run for debugging (NOT user tracking — ephemeral, not stored). Error handling v2 introduces build_error_response() for structured, consistent error responses across all tools. Health endpoint v2 (health_version: 2) provides richer diagnostics including session tracking status and BYOK availability. Smithery removal completes the migration to fully self-hosted GCP Cloud Run (zero external dependencies). BYOK hardened with retry logic, graceful degradation for invalid keys, and provider health checks. 205 tests at 55.1% coverage. Z-Protocol security review: APPROVED (9.2/10). PR #60

v0.4.5 — BYOK Live: Per-Tool-Call Provider Override (February 28, 2026)

The v0.4.5 release introduces Bring Your Own Key (BYOK) Live — users can now pass their own api_key and llm_provider on any individual tool call to override the server's default provider. The server auto-detects key format from prefix patterns (gsk_ → Groq, sk-ant- → Anthropic, sk- → OpenAI, AIza → Gemini) and creates ephemeral provider instances per request. Keys are never stored — used once and discarded. When no BYOK key is provided, the server falls back to its default Gemini/Groq configuration seamlessly. Response metadata includes _byok: true/false for full transparency. Triple-validated by Manus AI (6/6 pass), Claude Code (6/6 pass), and CI pipeline (175 tests). PR #55

v0.4.4 — Multi-Model Trinity: Full Quality (February 27, 2026)

The v0.4.4 release achieves _overall_quality: "full" — all three Trinity agents now return real AI inference with zero fallback defaults. Agent X (Innovator) runs on Gemini 2.5 Flash for creative analysis, while Agent Z (Guardian) and Agent CS (Validator) are routed to Groq/Llama-3.3-70b for reliable structured JSON output. The GroqProvider was upgraded with the full C-S-P extraction pipeline: strip_markdown_code_fences(), _extract_best_json() with field-overlap scoring, _merge_json_objects(), and _fill_schema_defaults(). Quality markers (_inference_quality, _agent_chain_status, _overall_quality) are embedded in every response for full transparency. 16 PRs merged (#33–#48), all CI passed. 12 new unit tests added.

v0.4.3 — C-S-P Pipeline & System Notice (February 27, 2026)

The v0.4.3 release implements the C-S-P (Compression–State–Propagation) methodology from the GodelAI framework, applied directly to the Trinity pipeline. Robust JSON extraction with raw_decode() and field-overlap scoring replaces brittle regex parsing. State validation checkpoints between Trinity stages prevent garbage propagation. System notice (_system_notice) field added to all tool responses for transparent user communication. Gemini JSON mode (response_mime_type: "application/json") tested and integrated.

v0.4.2 — Mock Mode Resolved & Transparent Disclosure (February 26, 2026)

The v0.4.2 release resolves the mock mode issue that affected all Trinity consultations from v0.4.0–v0.4.1. The root cause was a deprecated Gemini model endpoint (gemini-2.0-flash → gemini-2.5-flash). A transparent disclosure was published (Discussion #31) acknowledging the issue and explaining the "Structural Scaffolding Value" thesis — even in mock mode, the framework provided value by forcing structured multi-perspective reasoning. CodeQL security alerts reduced from 13 to 0.

Genesis v3.1 — CS Agent Multi-Stage Verification Protocol (February 2026)

Genesis v3.1 introduces a 4-Stage Security Verification Protocol for the CS Agent: Detection → Self-Examination (MANDATORY) → Severity Rating → Human Review. Self-examination is mandatory — every finding must be proven AND disproven before escalation. No auto-fixes. Human oversight is always the final stage. This is a workflow enhancement only — zero code changes to the server foundation. Inspired by Claude Code Security principles. Full protocol documentation: docs/security/.

v0.4.1 — Markdown-First Output & Smithery Sunset (February 14, 2026)

The v0.4.1 release introduces Markdown-first output with content negotiation — clients can now request Accept: text/markdown to receive validation reports in Markdown format (80% token reduction vs JSON). This aligns with the broader industry shift toward Markdown as the agent-native communication format (see Cloudflare: Markdown for Agents). All 13 Smithery proxy URL references were removed from server endpoints in preparation for the Smithery.ai legacy architecture sunset on March 1, 2026. The pdf_generator.py is deprecated — retained only for Zenodo DOI and enterprise compliance. Server version bumped with 155 total tests passing at 54.27% coverage.

v0.4.0 — Unified Prompt Templates (January 30, 2026)

The v0.4.0 release introduced the Unified Prompt Template system, adding 6 new MCP tools (10 total) and 19 pre-built YAML templates organized across 6 libraries. Templates are aligned to Genesis Methodology phases, support custom variables with type validation, and can be exported to Markdown or JSON. Users can import templates from GitHub Gists or raw URLs, and register custom templates at runtime. This release also includes the MACP v2.0 specification (DOI: 10.5281/zenodo.18504478) and the L (GODEL) Ethical Operating Framework v1.1.

v0.3.5 — Security Hardening (January 30, 2026)

Comprehensive input sanitization was added to all MCP tools, protecting against prompt injection (15+ patterns), XSS attacks, null byte injection, and input length abuse. All 29 sanitization unit tests pass. A CI/CD pipeline was established with GitHub Actions for automated testing and security scanning (Bandit, Safety) on every push.

Standardization Protocol v1.0 (December 2025)

The standardization phase generated 57 complete Trinity validation reports across seven domains including financial services, healthcare, education, and civic technology. By combining Gemini’s free tier for innovation analysis with Claude for ethics and security validation, we achieved sustainable costs (~$0.003 per validation) while maintaining research-grade quality. The 65% veto rate confirms our ethical safeguards work as designed.

Key Metrics

Metric	Value	Significance
MCP Tools	10	4 core validation + 6 template management
Templates	19	Pre-built across 6 libraries
Validation Reports	57	Proof of methodology at scale
Success Rate	95%	Reliable, production-ready system
Cost per Validation	~$0.003	Sustainable for solo developers
Veto Rate	65%	Strong ethical safeguards working
LLM Providers	7	Gemini, OpenAI, Anthropic, Groq, Mistral, Ollama, Perplexity
Multi-Model Routing	X=Gemini, Z=Groq, CS=Groq	Per-agent provider optimization
Trinity Quality	_overall_quality: "full"	All agents returning real inference (v0.4.4+)
Total Users	885+	Unique users across all platforms

Version History

Version	Date	Highlights
v0.5.0	Mar 1, 2026	Foundation: SessionContext tracing, error handling v2, health v2, Smithery removal, 205 tests
v0.4.5	Feb 28, 2026	BYOK Live: per-tool-call provider override, auto-detect key format, triple-validated
v0.4.4	Feb 27, 2026	Multi-Model Trinity (_overall_quality: "full"), X=Gemini, Z/CS=Groq
v0.4.3	Feb 27, 2026	C-S-P pipeline, system notice, robust JSON extraction
v0.4.2	Feb 26, 2026	Mock mode resolved, transparent disclosure, CodeQL 13→0
Genesis v3.1	Feb 2026	CS Agent 4-Stage Verification Protocol, zero code changes
v0.4.1	Feb 14, 2026	Markdown-first output, Smithery URL removal, PDF deprecated
v0.4.0	Jan 30, 2026	Unified Prompt Templates, 6 new tools, MACP v2.0
v0.3.5	Jan 30, 2026	Input sanitization, CI/CD pipeline
v0.3.2	Jan 29, 2026	Gemini 2.5-flash model update
v0.3.1	Jan 29, 2026	Smart Fallback, rate limiting, per-agent providers
v0.3.0	Jan 28, 2026	BYOK multi-provider support (7 providers)
v0.2.0	Dec 25, 2025	Multi-platform distribution
v0.1.0	Dec 21, 2025	Initial MCP server deployment

View Full Changelog → | View 57 Trinity Validation Reports →

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📚 Case Studies: Real-World Applications

VerifiMind-PEAS has been applied to validate real-world projects from concept to production. These case studies demonstrate the practical application of our methodology.

MarketPulse v5.0

AI-Powered Daily Market Intelligence for Value Investors

Attribute	Value
Project	MarketPulse
Version	5.0 (Production Ready)
Validation Date	January 2026
Status	✅ VALIDATED

MarketPulse is an open-source n8n workflow that delivers comprehensive daily market briefings for value investors. It demonstrates the "Bootstrapper's Edge" philosophy—leveraging free-tier infrastructure and open-source AI to build persistent, high-value intelligence systems at minimal cost.

Trinity Validation Results:

• X-Agent (Innovation): ✅ Approved - Democratizes financial intelligence through clever synthesis of free tools.
• Z-Agent (Ethics): ✅ Approved - Includes clear disclaimers that this is not financial advice.
• CS-Agent (Security): ✅ Approved - Secure credential management through n8n's built-in system.

📖 Read the Full MarketPulse Case Study →

A/B Test: Human Intuition vs. Validation-First Design

The Power of Methodological Rigor — A Real-World Comparison

Attribute	Value
Subject	MarketPulse GCP Deployment Architecture
Case A	Intuition-First Design (Manus AI)
Case B	Validation-First (X-Z-CS Trinity)
Validation Date	March 2026
Status	❌ REJECTED by Trinity (Architecture redesigned)

A real-world A/B test that occurred during MarketPulse development. A sophisticated GCP deployment architecture was designed using best practices and domain expertise (Case A), then systematically validated by the X-Z-CS Trinity (Case B). The Trinity unanimously rejected the design, exposing hidden costs and critical resource constraints that the intuition-first approach missed entirely.

Trinity Validation Results:

• X-Agent (Gemini): ⚠️ RECONSIDER - Feasibility 65/100. Hidden costs in VPC connector invalidate zero-cost claim.
• Z-Agent (Anthropic): ❌ REJECTED - Risk 85/100. "Financially deceptive" and "technically impossible" on 1GB RAM.
• CS-Agent (Anthropic): ❌ IMPRACTICAL - Practicality 15/100. "Building a fortress to protect a sandwich."

📖 Read the Full A/B Test Case Study →

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💡 Why VerifiMind-PEAS?

The Problem: Single-Model Bias

Most AI development relies on a single model (e.g., only Claude, only GPT).

This creates:

• 🔴 Single-model bias: One perspective, blind spots
• 🔴 Inconsistent quality: No systematic validation
• 🔴 Ethical gaps: No wisdom validation
• 🔴 Security vulnerabilities: No systematic security review

The Solution: Multi-Model Orchestration

VerifiMind-PEAS orchestrates multiple AI models for diverse perspectives:

1. X Intelligent Agent (Innovation): Generates creative solutions
2. Z Guardian Agent (Ethics): Validates ethical alignment
3. CS Security Agent (Security): Identifies vulnerabilities

By synthesizing diverse AI perspectives under human direction, you achieve:

• ✅ Objective validation: Multiple models check each other
• ✅ Ethical alignment: Wisdom validation built-in
• ✅ Security assurance: Systematic vulnerability assessment
• ✅ Human-centered: You orchestrate, AI assists

Honest Positioning

Multi-model orchestration is not new. Developers have been using multiple AI models (Claude, GPT, Gemini) together for years. What makes VerifiMind-PEAS different is how we structure this orchestration through the X-Z-CS RefleXion Trinity and Genesis Master Prompts.

Our genuine novelty:

• ✅ X-Z-CS RefleXion Trinity: Specialized validation roles (Innovation, Ethics, Security) with no prior art found
• ✅ Genesis Master Prompts: Stateful memory system for project continuity across multi-model workflows
• ✅ Wisdom validation: Ethical alignment and cultural sensitivity as first-class concerns
• ✅ Human-at-center: You orchestrate (not just review), AI assists (not automates)

What we build on (established practices):

• Multi-model usage (common practice since 2023)
• Agent-based architectures (LangChain, AutoGen, CrewAI)
• Human-in-the-loop validation (industry standard)

Our contribution: Transforming ad-hoc multi-model usage into systematic validation methodology with wisdom validation and human-centered orchestration.

Competitive Positioning: Complementary, Not Competing

VerifiMind-PEAS operates as a validation layer ABOVE execution frameworks. We don't replace LangChain, AutoGen, or CrewAI — we complement them.

Think of it this way:

• Execution frameworks (LangChain, AutoGen, CrewAI): "How to build and run AI agents"
• VerifiMind-PEAS: "How to validate what those agents produce"

Comparison Table:

Framework	Layer	Focus	Human Role	VerifiMind-PEAS Relationship
LangChain	Execution	Tool integration, chains	In-loop (reviewer)	Validates LangChain outputs for ethics + security
AutoGen	Execution	Multi-agent automation	In-loop (supervisor)	Validates AutoGen conversations for wisdom alignment
CrewAI	Execution	Role-based agents	In-loop (manager)	Validates CrewAI results for cultural sensitivity
OpenAI Swarm	Execution	Lightweight handoffs	In-loop (router)	Provides memory layer via Genesis Master Prompts
VerifiMind-PEAS	Validation	Wisdom validation	At-center (orchestrator)	Validation layer above all execution frameworks

Industry focus: Code execution, task automation, agent coordination
VerifiMind-PEAS focus: Wisdom validation, ethical alignment, human-centered orchestration

Result: Use VerifiMind-PEAS with LangChain/AutoGen/CrewAI to add validation layer. We complement, not compete.

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🔄 How It Works: The Genesis Methodology

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

Step 1: Initial Conceptualization

• Human defines the problem or vision
• AI (X Intelligent Agent) generates initial concepts and solutions
• Output: Initial concept with creative possibilities

Step 2: Critical Scrutiny

• AI (Z Guardian Agent) validates ethical alignment
• AI (CS Security Agent) identifies security vulnerabilities
• Multiple models challenge and validate each other
• Output: Validated concept with ethical and security considerations

Step 3: External Validation

• Independent AI analysis confirms systematic approach
• Research validates against academic literature and industry best practices
• Output: Externally validated concept with evidence

Step 4: Synthesis

• Human orchestrates final synthesis
• Human makes decisions based on AI perspectives
• Human documents decisions in Genesis Master Prompt
• Output: Final decision with documented rationale

Step 5: Iteration

• Recursive refinement based on feedback
• Continuous improvement through multiple cycles
• Genesis Master Prompt updated with learnings
• Output: Refined concept ready for next phase

This process ensures every output is validated through diverse AI perspectives before final human approval.

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🏗️ Architecture: The X-Z-CS RefleXion Trinity

VerifiMind-PEAS implements a multi-model orchestration architecture where:

Human Orchestrator (You)

• Role: Center of decision-making
• Responsibility: Synthesize AI perspectives, make final decisions
• Tools: Genesis Master Prompts, integration guides

X Intelligent Agent (Analyst/Researcher)

• Role: Market intelligence and feasibility analysis
• Focus: Research, technical feasibility, market analysis
• Models: Perplexity, GPT-4, Gemini (research-focused)
• Note: X agent focuses on analytical research and validation

Z Guardian Agent (Ethics)

• Role: Compliance and human-centered design protector
• Focus: Ethical alignment, cultural sensitivity, accessibility
• Models: Claude, GPT-4 (ethics-focused)

CS Security Agent (Security)

• Role: Cybersecurity protection layer
• Focus: Vulnerability assessment, threat modeling, security best practices
• Models: GPT-4, Claude (security-focused)
• Genesis v3.1: 4-Stage Verification Protocol — Detection → Self-Examination → Severity Rating → Human Review (docs)

This architecture synergizes diverse AI perspectives under human direction for objective, validated results.

About Y Agent (Innovator)

You may see Y Agent (Innovator) in some diagrams. This agent is part of the broader YSenseAI™ project, which focuses on innovation and strategic vision. The complete ecosystem includes:

• Y Agent (YSenseAI™): Innovation and creative ideation
• X Agent (VerifiMind-PEAS): Research and analytical validation
• Z Agent (VerifiMind-PEAS): Ethical compliance
• CS Agent (VerifiMind-PEAS): Security validation

VerifiMind-PEAS focuses on the X-Z-CS Trinity (Research, Ethics, Security), while YSenseAI™ provides the Y Agent (Innovation). Together, they form a complete validation framework.

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💡 The Concept: Crystal Balls Inside the Black Box

Instead of treating AI as an opaque "black box," VerifiMind-PEAS places multiple "crystal balls" (diverse AI models) inside the box to illuminate the path forward.

Each crystal ball represents a specialized AI agent with a unique perspective:

• Y (Innovator): Generates creative concepts and strategic vision (from YSenseAI™)
• X (Analyst): Researches feasibility and market intelligence (from VerifiMind-PEAS)
• Z (Guardian): Ensures ethical compliance and safety (from VerifiMind-PEAS)
• CS (Validator): Validates claims against external evidence and security best practices (from VerifiMind-PEAS)

Note: The diagram shows the complete 4-agent system (Y-X-Z-CS). VerifiMind-PEAS specifically implements the X-Z-CS Trinity, while Y Agent comes from YSenseAI™.

By orchestrating these diverse perspectives under human direction, we achieve objective, validated results that no single AI model can provide.

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🚀 Getting Started

No Installation Required!

VerifiMind-PEAS is a methodology, not software. You don't need to install anything!

Step 1: Read the Genesis Master Prompt Guide

Start here: Genesis Master Prompt Guide

This comprehensive guide teaches you:

• What is a Genesis Master Prompt?
• Why Genesis Master Prompts matter
• Step-by-step tutorial (meditation app example)
• Real-world validation (87-day journey)
• Advanced techniques
• Common mistakes and solutions

Time: 30 minutes to read, lifetime of value

Step 2: Choose Your AI Tool

VerifiMind-PEAS works with any LLM:

• ✅ Claude (Anthropic)
• ✅ GPT-4 (OpenAI)
• ✅ Gemini (Google)
• ✅ Kimi (Moonshot AI)
• ✅ Grok (xAI)
• ✅ Qwen (Alibaba)
• ✅ Any other LLM

Recommended: Use at least 2-3 LLMs for multi-model validation.

Step 3: Follow Integration Guides

Choose your integration approach:

1. Claude Code Integration

   • Paste GitHub repo URL → Claude applies methodology
   • Best for: Code-focused projects

2. Cursor Integration

   • Paste GitHub repo URL → Cursor applies methodology
   • Best for: IDE-integrated development

3. Generic LLM Integration

   • Copy-paste Genesis Master Prompt → Any LLM applies methodology
   • Best for: Platform-agnostic approach

Step 4: Start Your First Project

Follow the tutorial in the Genesis Master Prompt Guide:

1. Create your Genesis Master Prompt
2. Start first session with X Intelligent Agent (innovation)
3. Validate with Z Guardian Agent (ethics)
4. Validate with CS Security Agent (security)
5. Synthesize perspectives and make decision
6. Update Genesis Master Prompt
7. Repeat!

Example projects:

• Meditation timer app (tutorial example)
• AI-powered attribution system (YSenseAI™)
• Multi-model validation framework (VerifiMind-PEAS itself)

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💻 Reference Implementation (Optional)

VerifiMind-PEAS is a methodology framework that can be applied with any LLM or tool. You do NOT need code to use VerifiMind-PEAS.

However, for developers who want to see a complete implementation or need a starter template, we provide a Python reference implementation.

What's Included

The reference implementation demonstrates how to automate the X-Z-CS Trinity:

• X Intelligent Agent: Innovation engine for business viability analysis
• Z Guardian Agent: Ethical compliance validation (GDPR, UNESCO AI Ethics)
• CS Security Agent: Security validation with Socratic questioning engine (Concept Scrutinizer)
• Orchestrator: Multi-agent coordination and conflict resolution
• PDF Report Generator: Audit trail documentation

Status: 85% production-ready (Phase 1-2 complete, Phase 3-6 in progress)

Three Ways to Use VerifiMind-PEAS

Option 1: Apply Methodology Manually (No code required)

• Use Genesis Master Prompts with your preferred LLM
• Follow integration guides (Claude Code, Cursor, Generic LLM)
• Orchestrate X-Z-CS validation yourself
• Best for: Non-technical users, custom workflows

Option 2: Use Reference Implementation (Python developers)

• Clone repository: git clone https://github.com/creator35lwb-web/VerifiMind-PEAS
• Install dependencies: pip install -r requirements.txt
• Run validation: python verifimind_complete.py --idea "Your app idea"
• Best for: Developers who want automation, learning how X-Z-CS works

Option 3: Extend Reference Implementation (Contributors)

• Fork repository and add new agents, validation engines, or integrations
• Submit pull request to share with community
• Best for: Researchers, advanced developers, open-source contributors

Documentation

• Code Foundation Completion Summary: Current implementation status (85% complete)
• Code Foundation Analysis: Technical architecture and design decisions
• Requirements: Python dependencies

Important Notes

The reference implementation is:

• ✅ A learning resource (see how methodology translates to code)
• ✅ A starter template (fork and customize for your needs)
• ✅ A validation proof (shows methodology is executable)

The reference implementation is NOT:

• ❌ A required component (you can apply methodology without code)
• ❌ A production-ready SaaS (this is a reference, not a hosted service)
• ❌ The only way to implement (you can use other languages, tools, approaches)

Remember: VerifiMind-PEAS is a methodology framework. The code is ONE way to implement it, not THE way.

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📚 Documentation

Core Methodology

• Genesis Methodology White Paper v1.1: Comprehensive academic documentation
• Genesis Master Prompt Guide: Practical implementation guide
• X-Z-CS RefleXion Trinity Master Prompts: Specialized agent prompts (Chinese)

Integration Guides

• Claude Code Integration
• Cursor Integration
• Generic LLM Integration

Documentation Best Practices

VerifiMind-PEAS includes a comprehensive documentation framework for managing context across multi-model LLM workflows.

Three-Layer Architecture:

1. Genesis Master Prompt (Project Memory) - Single source of truth, updated after every session
2. Module Documentation (Deep Context) - Feature-specific details organized in /docs
3. Session Notes (Iteration History) - Complete audit trail of decisions and insights

Why This Matters:

• ✅ Context persistence across LLM sessions (no manual re-entry)
• ✅ Platform-agnostic (works with Claude, GPT, Gemini, Kimi, Grok, Qwen, etc.)
• ✅ Multi-model workflows (consistent context for X-Z-CS validation)
• ✅ Complete audit trail (track every decision and iteration)

Learn more: Documentation Best Practices Guide

Templates:

• Genesis Master Prompt Template
• Module Documentation Template
• Session Notes Template

Case Studies

• YSenseAI™ 87-Day Journey (Landing Pages): Real-world validation of Genesis Methodology
• VerifiMind-PEAS Development (Landing Pages): Meta-application of methodology to itself
• MarketPulse v5.0 Case Study: From concept to production with Trinity validation
• A/B Test: Intuition vs. Validation: Real-world proof of validation-first methodology

Operations & Troubleshooting

• MCP Server Troubleshooting Guide: Common HTTP status codes, configuration errors, and solutions
• GCP Monitoring Setup Guide: Dashboard, alerting, and log query reference
• GCP Deployment Guide: Cloud Run deployment instructions
• Server Status: Current operational status

Additional Resources

• Roadmap: Strategic development plan
• Changelog: Detailed version history
• Contributing Guidelines: How to contribute
• Zenodo Publication Guide: Defensive publication documentation
• MACP v2.0 Specification: Multi-Agent Communication Protocol
• L (GODEL) Ethical Operating Framework: Ethical constitution for AI agents

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🔧 Troubleshooting

⚠️ Common Mistakes (Read This First!)

Based on real production logs, 83.7% of all errors come from three configuration mistakes. If you are having trouble connecting, check these first:

Mistake #1: Wrong URL Path (405 Method Not Allowed)

Symptom: You get a 405 Method Not Allowed error.

Cause: You are sending requests to https://verifimind.ysenseai.org/ instead of https://verifimind.ysenseai.org/mcp/.

Fix: Always include /mcp/ in the URL:

{
  "mcpServers": {
    "verifimind-peas": {
      "url": "https://verifimind.ysenseai.org/mcp/"
    }
  }
}

Mistake #2: Using GET Instead of POST (400 Bad Request)

Symptom: You get a 400 Bad Request error.

Cause: Your client is sending a GET request. The MCP protocol requires POST for method calls.

Fix: Ensure your MCP client configuration uses streamable-http transport (not http-sse):

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

Mistake #3: Opening the URL in a Browser (406 Not Acceptable)

Symptom: You get a 406 Not Acceptable or see an error page in your browser.

Cause: verifimind.ysenseai.org is an MCP server API, not a website. It is designed to be accessed by MCP clients (Claude Desktop, Cursor, VS Code, etc.), not web browsers.

Fix: Use an MCP client to connect. If you want to browse the project, visit:

• Landing Page: verifimind.io
• GitHub: github.com/creator35lwb-web/VerifiMind-PEAS

HTTP Status Code Reference

Status Code	Meaning	Solution
302/307	Redirect (normal)	Use https://verifimind.ysenseai.org/mcp/ with trailing slash
400	Bad Request	Verify JSON syntax, use POST (not GET), include Content-Type: application/json
404	Not Found	Check URL for typos; use the correct /mcp/ endpoint
405	Method Not Allowed	You are hitting / instead of /mcp/ — add the /mcp/ path
406	Not Acceptable	You are visiting the API URL in a browser — use an MCP client instead

Quick connectivity test:

curl https://verifimind.ysenseai.org/mcp/

Full troubleshooting guide: MCP_Server_Troubleshooting_Guide.md

Operational Insights

Traffic analysis from GCP Cloud Run logs (2-week sample, February 2026) provides the following operational baseline:

Metric	Value	Notes
Total Requests	8,578	14-day sample, excluding health checks
All-Time Users	530+	Cumulative unique users
Active Users (Week)	118	Weekly active users
Retention Rate	11.3%	Improving (up from 7.4%)
Top Client	Node.js MCP (67.7%)	Primary integration method
Cursor IDE	12.5%	Growing IDE adoption
Server Errors (5xx)	0	Zero server errors in production
Average Latency	4.30ms	Exceptional response time
Monthly Cost	$0	Within GCP free tier

The server runs on GCP Cloud Run with zero minimum instances (cold start architecture) to maintain a $0/month operating cost. GCP Global Uptime Checks monitor the /health endpoint every 5 minutes with email alerts to the project maintainer. All monitoring features operate within GCP’s free tier.

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🌍 Real-World Validation

87-Day Journey: YSenseAI™ + VerifiMind-PEAS

Creator: Alton Lee Wei Bin (creator35lwb)
Duration: 87 days (September - November 2025)
Projects: YSenseAI™ (AI attribution infrastructure) + VerifiMind-PEAS (validation methodology)

Challenges:

• Solo builder with non-tech background
• Multiple LLMs (Kimi, Claude, GPT, Gemini, Qwen, Grok)
• Hundreds of conversations across 87 days
• Complex technical and philosophical concepts

Results:

• ✅ YSenseAI™: Fully documented AI attribution infrastructure
• ✅ VerifiMind-PEAS: Complete methodology framework with white paper
• ✅ Defensive Publication: DOI 10.5281/zenodo.17645665
• ✅ Zero context loss: Genesis Master Prompts maintained continuity

Key Insights:

1. Genesis Master Prompts scale: Started with 1 page, grew to 50+ pages
2. Multi-model validation works: Different LLMs provided complementary perspectives
3. Human-at-center is critical: AI provides perspectives, human synthesizes and decides
4. Iteration is key: Continuous refinement through 87 days led to success

Read the full case study: YSenseAI™ 87-Day Journey

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🤝 Community

Join the Discussion

• GitHub Discussions: Ask questions, share insights, collaborate
• Twitter/X: Follow updates and announcements
• Email: Direct contact for inquiries

How to Contribute

We welcome contributions from the community!

Ways to contribute:

• 📝 Share case studies: Document your experience using VerifiMind-PEAS
• 🌍 Translate documentation: Help make VerifiMind-PEAS accessible globally
• 💬 Answer questions: Help others in GitHub Discussions
• 🐛 Report issues: Identify unclear documentation or gaps
• 🎓 Create tutorials: Share your learning journey

Read more: Contributing Guidelines

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🗺️ Roadmap

Current Phase: Phase 6 — Protocol-First & Ecosystem Alignment (Q1 2026)

Status: Phases 1–5 COMPLETE ✅ | v0.5.0 DEPLOYED 🎉 | v0.6.0 IN DEVELOPMENT

North Star: Position VerifiMind-PEAS as the trust and verification layer for the emerging Agentic Web.

Phase 1–4: Foundation ✅ COMPLETE

Phases 1 through 4 established the methodology framework, MCP server implementation, production deployment on GCP Cloud Run, and multi-platform distribution across Smithery.ai, Hugging Face, and the Official MCP Registry.

Phase 5: Hardening & Standardization ✅ COMPLETE

Completed (January 2026):

• ✅ v0.3.0–v0.3.5: BYOK multi-provider (7 providers), smart fallback, rate limiting, input sanitization
• ✅ v0.4.0: Unified Prompt Templates (19 templates, 6 libraries, 6 new tools)
• ✅ CI/CD pipeline: GitHub Actions with unit tests, security scanning (Bandit, Safety)
• ✅ MACP v2.0: Multi-Agent Communication Protocol published (DOI: 10.5281/zenodo.18504478)
• ✅ L (GODEL) Ethical Operating Framework v1.1: Fairness, bias mitigation, update mechanism
• ✅ GCP Monitoring: Uptime checks, alerting, log analysis pipeline

Phase 6: Protocol-First & Ecosystem Alignment 🚧 CURRENT

Completed (February–March 2026):

• ✅ v0.5.0 Foundation: SessionContext tracing, error handling v2, health v2, Smithery removal, 205 tests
• ✅ v0.4.1–v0.4.5: Markdown-first output, BYOK Live, Multi-Model Trinity (full quality)
• ✅ Branch protection: Main branch ruleset with required PR reviews and CI checks
• ✅ CodeQL remediation: All 102 security alerts resolved across 4 waves
• ✅ Strategic pivot: MACP v2.0 repositioned as free protocol for adoption (not paid product)
• ✅ Genesis Master Prompt v4.0 "Protocol": Updated for 7-skill ecosystem and protocol-first strategy
• ✅ Genesis v3.1: CS Agent Multi-Stage Verification Protocol — 4-stage workflow
• ✅ Landing page: verifimind.io LIVE with protocol-first messaging
• ✅ MACP Research Assistant: macpresearch.ysenseai.org — showcase proving MACP v2.0 works
• ✅ 7-Skill Composition Stack: Complete Manus AI skill ecosystem deployed
• ✅ GitHub Profile README: Full brand refresh with YSenseAI™ ecosystem showcase
• ✅ 48 PRs merged: Healthiest repo state (5 open issues from 17)

In Progress (March 2026):

• ⏳ v0.6.0 "Protocol": Protocol-first announcement release
• ⏳ Knowledge Graph (Phase 4): Structured data layer for MACP Research Assistant
• ⏳ Retention data collection: Building data-driven foundation for v0.7.0 pricing decisions
• ⏳ Skill development: Completing macp-protocol-v2 and macp-research-assistant skills

Future Phases 📋 PLANNED

Version	Codename	Theme	Timeline
v0.6.x	"Hardening"	Foundation testing + retention data	Mar–Apr 2026
v0.7.0	"Commerce"	First paid tier (data-driven pricing)	May–Jun 2026
v0.8.0	"Scale"	Enterprise + LegacyEvolve integration	Q3 2026
v0.9.0	"Community"	Ecosystem + skill marketplace	Q4 2026
v1.0.0	"Genesis"	Self-sustaining platform	Q1 2027

Key Metrics:

Metric	Value	Significance
MCP Tools	10	4 core + 6 template management
Templates	19	Pre-built across 6 libraries
Validation Reports	57+	Proof of methodology at scale
Platforms Live	4	GCP, MCP Registry, HuggingFace, verifimind.io
LLM Providers	7	Gemini, OpenAI, Anthropic, Groq, Mistral, Ollama, Perplexity
All-Time Users	530+	Cumulative unique users
Manus AI Skills	7	Complete skill composition stack
Cost per Validation	~$0.003	Sustainable for all developers

See Examples: /validation_archive/ | Examples

Read more: Roadmap | v0.5.0 Agent Skills Specification

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🧩 7-Skill Composition Stack (Manus AI Ecosystem)

VerifiMind-PEAS is supported by a 7-skill composition stack — a layered ecosystem of Manus AI skills that work together to enable multi-agent collaboration, protocol-driven communication, and self-recursive validation.

Layer	Skill	Role	Status
5	ysenseai-flywheel-team	Ecosystem orchestration & AI Council validation	✅ Active
4	macp-protocol-v2	Primary differentiator — free protocol driving adoption	✅ Active
3	multi-agent-handoff-bridge	Artifact delivery between sandbox and local agents	✅ Active
2	internet-skill-finder + github-gem-seeker	Discovery layer for skills and solutions	✅ Active
1	macp-research-assistant	Showcase — proves MACP v2.0 works in production	✅ Active
0	skill-creator	Foundation — creates and updates all other skills	✅ Active

Protocol Landscape Positioning

The multi-agent protocol landscape now has 4 major protocols. MACP v2.0 occupies a unique gap none of them address:

Protocol	Maintainer	Focus	MACP v2.0 Relationship
MCP	Anthropic / Linux Foundation	Vertical: AI ↔ Tools	MACP uses MCP as transport layer
A2A	Google	Horizontal: Agent ↔ Agent (autonomous)	Complementary — A2A lacks human orchestration
ACP	IBM	Enterprise agent communication	Different scope — enterprise vs. individual
ANP	Community	Agent identity & trust	Complementary — ANP handles identity, MACP handles workflow
MACP v2.0	YSenseAI™	Human-orchestrated multi-agent coordination	Unique: Git-based, human-at-center, platform-agnostic

MACP v2.0 is free forever. The protocol drives adoption. Revenue comes from hosted orchestration services (v0.7.0+), following the HTTP/AWS, Git/GitHub model.

📖 Defensive Publication

Prior Art Established

VerifiMind-PEAS establishes prior art for the Genesis Prompt Engineering methodology and prevents others from patenting this approach to multi-model AI validation.

Published: November 19, 2025
DOI: 10.5281/zenodo.17645665
License: MIT License

Core Innovations:

1. Genesis Methodology: Systematic 5-step multi-model validation process
2. X-Z-CS RefleXion Trinity: Specialized AI agents (Innovation, Ethics, Security)
3. Genesis Master Prompts: Stateful memory system for project continuity
4. Human-at-Center Orchestration: Human as orchestrator (not reviewer)

Evidence of Prior Use:

• YSenseAI™: AI-powered attribution infrastructure (87-day development)
• VerifiMind-PEAS: Multi-model validation methodology framework
• Concept Scrutinizer (概念审思者): Socratic validation framework

Read more: Zenodo Publication Guide

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📚 How to Cite

Citing VerifiMind-PEAS v0.5.0 (MCP Server)

If you use the VerifiMind-PEAS MCP server in your research or project, please cite:

APA Style:

Lee, A., Manus AI, & Claude Code. (2026). VerifiMind-PEAS: Prompt Engineering Attribution System (Version 0.5.0) [Computer software]. GitHub. https://github.com/creator35lwb-web/VerifiMind-PEAS

BibTeX:

@software{verifimind_peas_v040_2026,
  author = {Lee, Alton and {Manus AI} and {Claude Code}},
  title = {VerifiMind-PEAS: Prompt Engineering Attribution System},
  year = {2026},
  version = {0.5.0},
  url = {https://github.com/creator35lwb-web/VerifiMind-PEAS},
  doi = {10.5281/zenodo.17980791},
  note = {MCP server for multi-model AI validation with Unified Prompt Templates}
}

IEEE Style:

A. Lee, Manus AI, and Claude Code, "VerifiMind-PEAS: Prompt Engineering Attribution System," Version 0.5.0, GitHub, 2026. [Online]. Available: https://github.com/creator35lwb-web/VerifiMind-PEAS

Citing Genesis Methodology v2.0 (Methodology)

If you use or reference the Genesis Prompt Engineering Methodology, please cite:

APA Style:

Lee, A., & Manus AI. (2025). Genesis Prompt Engineering Methodology v2.0: Multi-Agent AI Validation Framework (Version 2.0.0) [Methodology]. Zenodo. https://doi.org/10.5281/zenodo.17972751

BibTeX:

@misc{genesis_v2_2025,
  author = {Lee, Alton and {Manus AI}},
  title = {Genesis Prompt Engineering Methodology v2.0: Multi-Agent AI Validation Framework},
  year = {2025},
  version = {2.0.0},
  url = {https://doi.org/10.5281/zenodo.17972751},
  doi = {10.5281/zenodo.17972751},
  note = {Validated through 87-day production development, 21,356 words}
}

IEEE Style:

A. Lee and Manus AI, "Genesis Prompt Engineering Methodology v2.0: Multi-Agent AI Validation Framework," Version 2.0.0, Zenodo, 2025. [Online]. Available: https://doi.org/10.5281/zenodo.17972751

GitHub Citation

GitHub provides automatic citation support. Click the "Cite this repository" button on the repository page to get formatted citations in APA and BibTeX formats.

DOI Badges

VerifiMind-PEAS v1.1.0:

Genesis Methodology v2.0:

Note: DOI badges will be updated after Zenodo registration is complete.

Release Information

VerifiMind-PEAS MCP Server v0.4.1 (Current):

• Release Date: February 14, 2026
• Highlights: Markdown-first output, Smithery URL removal, PDF deprecated
• Changelog: CHANGELOG.md
• Status: Production deployed on GCP Cloud Run

VerifiMind-PEAS v1.1.0 (Methodology):

• Release Date: December 18, 2025
• Tag: verifimind-v1.1.0
• Release Notes: RELEASE_NOTES_V1.1.0.md
• Status: Production-ready, deployment-ready for Smithery marketplace

Genesis Methodology v2.0:

• Release Date: December 18, 2025
• Tag: genesis-v2.0
• Release Notes: RELEASE_NOTES_GENESIS_V2.0.md
• Status: Production-validated through 87-day development journey

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📜 License

Open Source License (MIT)

VerifiMind-PEAS is released under the MIT License for personal, educational, and open-source use.

Copyright (c) 2025-2026 Alton Lee Wei Bin (creator35lwb)

Permission is hereby granted, free of charge, to any person obtaining a copy of this software and associated documentation files (the "Software"), to deal in the Software without restriction, including without limitation the rights to use, copy, modify, merge, publish, distribute, sublicense, and/or sell copies of the Software, and to permit persons to whom the Software is furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE SOFTWARE.

Commercial License

For enterprises requiring additional features, support, and legal protections, we offer commercial licensing options:

• 🏢 Enterprise Deployment: Production environments with SLA requirements
• 🔒 Proprietary Extensions: Building proprietary features on top of the framework
• 📞 Priority Support: Dedicated support channels and guaranteed response times
• 🛡️ Indemnification: Legal protection and IP indemnification
• 📊 Compliance: Audit trails and compliance reports for regulated industries

Read more: Commercial License

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™️ Trademark Notice

The following are trademarks of Alton Lee:

• VerifiMind™ - Primary brand
• Genesis Methodology™ - Validation methodology
• RefleXion Trinity™ - X-Z-CS agent architecture

Usage Guidelines:

• ✅ Use freely for personal and educational purposes
• ✅ Reference in documentation and discussions
• ❌ Do not use in product names without permission
• ❌ Do not imply official endorsement without permission

Forks and derivatives may use the open-source code under MIT license, but must use different branding.

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📞 Contact

General Inquiries: creator35lwb@gmail.com
Twitter/X: @creator35lwb
GitHub Discussions: Join discussions
MCP Server: verifimind.ysenseai.org (LIVE — v0.5.0)
Landing Page: verifimind.io

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🙏 Acknowledgments

FLYWHEEL TEAM

VerifiMind-PEAS is developed through the FLYWHEEL TEAM multi-agent collaboration protocol:

Agent	Role	Contribution
Alton Lee (L/GODEL)	Human Orchestrator & Founder	Vision, strategy, final decisions
Manus AI (T/CTO)	Strategic Architecture	Documentation, roadmap, ecosystem alignment, skill development, landing page
Claude Code (RNA)	Implementation Lead	Code, testing, deployment, CI/CD, MACP Research Assistant
Gemini (Antigravity)	GCP Operations	Log analysis, monitoring, troubleshooting
Perplexity	Real-Time Research	Market intelligence, competitive analysis

LLM Providers

• Google Gemini: Default FREE provider for innovation analysis and GCP operations
• Anthropic Claude: Ethics and safety validation, code implementation
• OpenAI GPT-4: Technical analysis and structured output
• Moonshot AI Kimi: Innovation and creative insights
• xAI Grok: Alternative perspectives and validation
• Alibaba Qwen: Multilingual support
• Groq / Mistral / Perplexity / Ollama: BYOK multi-provider support

Special Thanks

• Open-source community: For inspiration and collaboration
• Early adopters: For feedback and validation (885+ users and counting)
• Academic researchers: For theoretical foundations
• Google Cloud Platform: For generous free tier enabling $0/month operations

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