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ReachableCEO-AI-Homedir-Public/databank/collab/README-PROPOSAL-AGENTS-v2.md

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🤖 README-PROPOSAL-AGENTS-v2.md

Beautiful documentation for updated AI Agent Guidelines Proposal


📋 Table of Contents


🧠 Overview

Welcome to the beautifully designed documentation for the PROPOSAL-AGENTS-v2.md file. This document represents an updated comprehensive baseline for AI agent operations that aligns with the current state of AGENTS.md after multiple iterations. It can be mounted across all your projects via your AI home directory.

Attribute Details
Purpose Updated baseline AI agent guidelines for all projects
Current State Aligns with v7.0.1 of AGENTS.md
Target User Solo entrepreneur (Founder/CTO/Operations)
AI Usage Optimized for 14+ hours daily interaction
Structure Mountable across multiple project environments

🔄 Current State Alignment

This v2 proposal reflects the current AGENTS.md state including:

Recent Updates

  • Date/Time Format: Full date, time, timezone without seconds (HH:MM format)
  • Authority Structure: Charles N Wyble (@ReachableCEO) is in charge at all times
  • Filesystem Truth: Filesystem is source of truth; check with Charles for discrepancies
  • Databank/PMO Structure: Clear separation with specific access rights
  • Gorgeous Commits: Verbose, detailed commit messages as standard
  • Collaboration Model: Clear distinction between databank/collab/ and databank/artifacts/
  • Versioning: Minor increments for non-major changes (e.g., 7.0.0 to 7.0.1)

Key Changes from v1

┌─────────────────────────────────────────────────────────────┐
│                    EVOLUTION FROM V1 TO V2                  │
├─────────────────────────────────────────────────────────────┤
│  BEFORE (v1)                    │  AFTER (v2)               │
│  - Basic collab/output model     │  - Advanced databank/PMO  │
│  - Simple authority structure    │  - Clear authority rules  │
│  - Basic date format             │  - 24-hour time format    │
│  - No explicit truth rules       │  - Filesystem source truth│
│  - Output directory reference    │  - Artifacts model        │
└─────────────────────────────────────────────────────────────┘

Key Features

🎯 Core Operating Principles

Principle Current Implementation
Context Awareness Databank/PMO separation with specific access rights
Communication Protocol Questions -> proposals -> implementation workflow
Documentation Standards Beautiful formatting with date/time/timezone

⚙️ Operational Guidelines

  • Repository Management: Clean structure with conventional commits
  • Documentation Standards: Date/time headers, revision tracking, changelogs
  • Workflow Adherence: Follow question -> proposal -> implementation

🧩 LLM Optimization Practices

  • Prompt Engineering: Clear, structured requests with context
  • Code Generation: Consistent with project patterns
  • Quality Assurance: Comprehensive validation and testing

📊 Implementation Guide

Step 1: Understanding the Current Framework

  1. Databank Context → Readonly access except for designated areas
  2. PMO Updates → Read-write access for project tracking when appropriate
  3. Authority Model → Charles in charge at all times

Step 2: Following Documentation Standards

  • Include full date/time header with timezone (HH:MM format, 24-hour)
  • Maintain change tracking/revision table with full date/time
  • Create changelog in source files with full date/time
  • Apply "make it beautiful" rule to all documentation

Step 3: Operational Excellence

  • Use atomic commits with conventional commit messages
  • Write gorgeous, verbose commit messages when needed
  • Commit frequently to local repository
  • Respect file access permissions (readonly vs read-write)

🏆 Best Practices

🛡️ Security Practices

  • 🔐 Verify file permissions and access controls
  • 🛡️ Sanitize inputs and outputs appropriately
  • 🔐 Protect sensitive information and credentials
  • 🔒 Follow secure coding principles

📈 Quality Assurance

  • Implement appropriate testing strategies
  • Ensure code quality and maintainability
  • Perform regular documentation updates
  • Validate outputs against expected outcomes

Performance Considerations

  • Optimize for efficient processing
  • 💾 Minimize resource usage where possible
  • 📊 Consider impact on system performance
  • 🔄 Implement caching strategies when appropriate

👑 Authority Structure

Decision Hierarchy

Level Authority Responsibilities
Primary Charles N Wyble (@ReachableCEO) Final decision maker on all matters
Implementation AI Agents Execute within defined guidelines
Review Charles Validate all major changes

When to Stop and Ask

  • When docs and filesystem conflict
  • When discrepancy isn't in git history
  • When Charles manually modifies filesystem
  • When unsure about file access permissions

🚀 Solo Entrepreneur Optimization

For someone using AI 14+ hours daily with multiple projects:

Need Current Solution
Time Efficiency Atomic operations with clear authority structure
Context Switching Consistent interfaces across projects
Decision Documentation Clear reasoning trails with proper attribution
Multi-Project Impact Considerations for interconnected projects

🤝 Communication Workflow

┌─────────────┐    ┌──────────────┐    ┌─────────────────┐
│   Question  │ -> │   Proposal   │ -> │ Implementation  │
│             │    │              │    │                 │
│ What to do? │    │ How to do it?│    │ Execute & Test  │
└─────────────┘    └──────────────┘    └─────────────────┘

Primary Channels

  • Collaboration: Use databank/collab/ directory for human/AI interaction
  • Documentation: Maintain comprehensive records with proper date/time
  • Change Management: Use version control with proper tracking

💼 Founder/CTO Specific Considerations

Decision Making Framework

  • 📊 Document reasoning for complex decisions with full context
  • 🔗 Consider impact across multiple projects with clear attribution
  • 📜 Maintain traceability for future reference with proper versioning
  • 🔄 Suggest alternatives when appropriate with complete information

Scalability Planning

  • 🏗️ Design solutions that work across multiple project contexts
  • 🧱 Use modular, reusable components with proper documentation
  • 📈 Plan for increasing complexity as projects grow
  • 🔗 Maintain consistent interfaces across projects

📈 Active Development Status

🔄 Note: This proposal reflects the current living knowledge base that supports your 14+ hours daily AI usage.

Current Focus Areas

  • Documentation standards
  • Operational guidelines
  • LLM optimization
  • Authority structure
  • Versioning approach
  • Integration patterns (planned)
  • Performance metrics (planned)

📞 Getting Help

For questions about implementing these guidelines:

  1. Create a new issue in the databank/collab/ directory
  2. Reference this proposal document
  3. Provide specific context about your use case
  4. Follow the established question -> proposal -> implementation workflow
  5. Ask Charles if documentation conflicts with filesystem

Last updated: October 24, 2025 09:43 CDT
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