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

Date/Time

Friday October 24, 2025 09:43 CDT (UTC-05:00)

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PROPOSAL-AGENTS.md - v2

This file proposes an updated comprehensive baseline AGENTS.md file that reflects the current state after multiple iterations and optimizations. It serves as a guide that can be used across all projects via mounting in the AI home directory. It optimizes for LLM consumption and addresses the needs of a solo entrepreneur in a founder/CTO role covering operations tasks with 14+ hours daily AI usage.

Core Operating Principles for AI Agents

Context Awareness

• You are operating within a mounted AI home directory with separated databank (readonly) and PMO (read-write)
• Databank (/ai-home/databank/): Contains readonly context, guidelines, and personal information
• PMO (/ai-home/pmo/): Contains project management functionality where updates are allowed
• Always consider the multi-project implications of your actions
• Respect the readonly nature of the databank and only update PMO when appropriate

Communication Protocol

• Primary communication channel: collab/ directory in mounted AI home directory
• Use question -> proposal -> implementation workflow
• Document all significant decisions and changes with proper revision tracking

Documentation Standards (Apply to ALL files you create)

• Date/Time Headers: Include full date/time with timezone in all markdown files (date, time, timezone format)
• Change Tracking: Maintain revision tables in all documents with full date/time/timezone
• Changelog: Include changelogs in all source code files with full date/time/timezone
• Make It Beautiful Rule: All documentation follows beautiful formatting standards (tables, bullet points, clear structure, visual hierarchy)

Repository Management

Structure Requirements

• Databank: Readonly context (do not modify except in designated areas)
  • databank/personal/ - Personal information
  • databank/agents/ - Agent guidelines and tools
  • databank/context/ - General context information
  • databank/operations/ - Operational environment information
  • databank/templates/ - Template files for projects
  • databank/collab/ - Human/AI interaction space (read/write for interaction)
  • databank/artifacts/ - AI-managed content (fully managed by AI)
• PMO: Read-write project management (updates allowed here)
  • pmo/artifacts/dashboard/ - Dashboard views
  • pmo/artifacts/projects/ - Project registry and tracking
  • pmo/artifacts/reports/ - Status reports
  • pmo/artifacts/resources/ - Resource management
  • pmo/artifacts/config/ - Configuration
  • pmo/artifacts/docs/ - Documentation
  • pmo/collab/ - PMO-specific collaboration
• Keep top-level repository clean (databank and pmo directories only)
• Use conventional commits (chore:, feat:, docs:, fix:, etc.)
• Commit frequently using atomic commits
• Only commit to local repository (no git push operations)

Access Rights

• Databank (readonly): Access only for most content, with exceptions for databank/collab/ and databank/artifacts/
• PMO (read-write): Only update when project milestones reached or status updates needed
• Collab (readonly): Access for reference, no modifications in active projects

Development Workflow

Pre-Work Checklist

• Read project-specific documentation first
• Check collab/rules directory for project-specific guidelines (SECURITY.md, RELEASE.md, GITFLOW.md, etc.)
• Review databank context for consistent understanding
• Understand project dependencies and constraints

Implementation Standards

• Follow conventional commits with beautiful, descriptive messages
• Maintain consistency with existing codebase
• Add appropriate documentation and comments
• Consider maintainability and future extensions

Verification Process

• Validate operations before execution
• Run appropriate tests and quality checks
• Verify outputs against expected outcomes
• Implement defensive programming practices

PMO Update Guidelines

When to Update PMO

• When project milestones are reached
• When project status changes significantly
• When new projects are initiated
• When projects are completed or paused
• When resource allocation changes

What to Update in PMO

• Project registry in pmo/artifacts/projects/
• Dashboard information in pmo/artifacts/dashboard/
• Status reports in pmo/artifacts/reports/
• Resource tracking in pmo/artifacts/resources/
• Configuration in pmo/artifacts/config/

What NOT to Update

• Never modify general databank files - they are readonly
• Do not create new top-level directories
• Do not modify collab files in active projects without explicit permission
• Do not add audit logs to this repository (audit logs belong in projects)

Authority and Decision Making Guidelines

Authority Structure

• Charles N Wyble (@ReachableCEO) is in charge at all times
• If something is adrift between docs and filesystem/code, stop and ask Charles to resolve the issue
• Especially if discrepancy isn't reflected in git or conversation history, ask for clarification
• When Charles modifies filesystem manually (vs having AI do it), Charles will ensure AI integrates the changes into mental model
• Do not create or modify things that Charles hasn't explicitly instructed
• The filesystem is the source of truth
• If you notice discrepancies between documentation and actual filesystem, ask Charles to resolve

Decision Documentation

• Document reasoning for complex decisions
• Consider impact across multiple interconnected projects
• Maintain traceability for future reference
• Suggest alternatives when appropriate

Best Practices Integration

LLM Optimization Practices

Prompt Engineering

• Structure requests with clear context from mounted AI home directory
• Use explicit, unambiguous language
• Provide sufficient context without unnecessary verbosity
• Break multi-step processes into clear, sequential instructions

Code Generation

• Follow established project patterns and conventions
• Maintain consistency with existing code style
• Add appropriate error handling and validation
• Consider performance implications

Quality Assurance

• Implement appropriate testing strategies
• Ensure code quality and maintainability
• Perform validation against requirements
• Include appropriate logging and monitoring

Security Practices

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

Performance Considerations

• Optimize for efficient processing
• Consider resource usage and constraints
• Implement appropriate caching strategies
• Monitor and optimize for performance

Git and Version Control

Commit Standards

• Use conventional commits with semantic meaning
• Make commits atomic (one logical change per commit)
• Write gorgeous, verbose commit messages when needed
• Include comprehensive context and detailed descriptions
• Follow the aesthetic principles of beautiful commits

Guidelines for Gorgeous Commit Messages

• Be verbose and comprehensive when it adds value
• Include context about why the change was made
• Explain the impact of the changes when relevant
• Use clear, descriptive language that future-you will understand
• Follow the format: "type(scope): short description" for the first line
• Add a blank line followed by detailed explanation when needed
• Include any relevant references (issues, discussions, etc.)
• Aim for beauty in both form and function
• Think of commit messages as documentation for the changes

Branching and Merging

• Follow project-specific branching strategies
• Respect existing GitFlow patterns
• Use feature branches for significant changes
• Maintain clean commit history

Environment Management

Context Integration

• Recognize that databank is mounted readonly across multiple environments
• PMO is mounted read-write for project tracking
• Maintain consistency in behavior across different projects
• Respect environment-specific configurations

Collaboration and Artifacts

• Use databank/collab/ for human/AI interaction and communication
• Use databank/artifacts/ for AI-managed content (docs, code, config, templates)
• AI has full management control over databank/artifacts/ directory
• Human interaction primarily occurs in databank/collab/ directory
• The AI manages the databank/artifacts/ directory structure as needed
• Common pattern: databank/artifacts/docs/, databank/artifacts/code/, databank/artifacts/config/, etc.
• Maintain clean separation between human-managed and AI-managed resources
• Follow consistent naming conventions across artifacts

Tool Integration

• Work with existing development tools and workflows
• Maintain compatibility with CI/CD pipelines
• Use project-appropriate build and deployment processes
• Respect project-specific dependencies and versions

AI Tool Context (for agents working in this environment)

• Codex - Primary daily driver (subscription-based), best for code generation and completion
• Qwen - Heavy system orchestration, excels at shell/Docker operations
• Gemini - Primarily used for audits and analysis
• Claude - Used occasionally for specific tasks

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Change Tracking/Revision Table

Date/Time	Version	Description	Author
2025-10-24 09:43 CDT (UTC-05:00)	2.0.1	Standardize to change tracking table only	Charles N Wyble (@ReachableCEO)
2025-10-24 09:43 CDT (UTC-05:00)	2.0.0	Update proposal to reflect current AGENTS.md state	Charles N Wyble (@ReachableCEO)
2025-10-24 09:43 CDT (UTC-05:00)	1.0.0	Initial proposal for baseline AGENTS.md	Charles N Wyble (@ReachableCEO)

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