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Agent Protocols 🤖

Agent Protocols is a structured framework of instructions, personas, skills, and SDLC workflows designed to optimize agentic AI coding assistants. It serves as a centralized, shared foundation to help LLM-based agents maintain code quality, architectural consistency, and professional standards across all your projects.

Table of Contents

How to Use and Update

This framework is distributed via the dist branch and is meant to be added as a Git submodule in your project's .agents/ directory.

1. Initial Setup

Add the submodule to your project:

git submodule add -b dist https://github.com/dsj1984/agent-protocols.git .agents

2. Update Strategies

Regularly update the protocols to pick up the latest personas and skills.

A. Manual Update (Bash)

git submodule update --remote --merge .agents && git add .agents && git commit -m "chore: update agent-protocols"

B. Manual Update (PowerShell)

git submodule update --remote --merge .agents; git add .agents; git commit -m "chore: update agent-protocols"

C. Automated Update (package.json)

Add the following script to your package.json for one-command updates:

"scripts": {
  "update:agents": "git submodule update --remote --merge .agents && git add .agents && git commit -m \"chore: update agent-protocols\""
}

Now you can run: npm run update:agents.

  1. Read the Full Guide: For detailed configuring, using personas/skills, and more, refer to the detailed protocol guide: 👉 .agents/README.md

Repository Structure

The core of this repository lives entirely within the .agents/ directory, which is what gets distributed to consumers.

agent-protocols/
├── .agents/                 # ← Distributed to consumers via the `dist` branch
│   ├── instructions.md      # Core system prompt & rules
│   ├── personas/            # Role-specific behavior constraints
│   ├── skills/              # Tech-stack-specific guardrails
│   ├── templates/           # Markdown templates
│   ├── workflows/           # SDLC automation slash commands
│   └── README.md            # Detailed consumer user guide
├── .github/                 # CI/CD automation for this repository
├── package.json             # Tooling: markdownlint, prettier, husky
└── README.md                # ← You are here

Key distinction: Only the .agents/ directory is distributed to consumers via the dist branch. The rest of the repository contains internal tooling and CI/CD pipelines for developing the protocols.

Contributions

If you are contributing to or modifying this repository:

Prerequisites & Setup

  • Node.js 20+
  • npm
npm install

This also installs Husky Git hooks via the prepare script, which configures lint-staged to auto-format and lint markdown files on every commit.

Quality Control

All markdown is validated with markdownlint and formatted with prettier:

Command Description
npm run lint Check all markdown for lint errors
npm run format Auto-format all markdown files

Git Workflow

  1. Create a feature branch from main.
  2. Make your changes to files inside .agents/.
  3. Commit — Husky + lint-staged will automatically lint and format staged .md files before the commit is accepted.
  4. Open a Pull Request against main. The ci.yml workflow will validate your changes.

CI/CD Pipeline

Workflow Trigger Purpose
ci.yml Push/PR to main Validates markdown, runs security scans, and syncs dist branch

When changes to .agents/** are merged into main, the ci workflow automatically copies the .agents/ directory contents to the dist branch if the build passes. Consumers pinned to dist will pick up the changes on their next submodule update.

Personal Agentic Dev Stack

The development of these protocols leverages an agent-first stack optimized for speed, precision, and high-context reasoning:

  • LLM Engine: Google AI Ultra
  • Planning Assistant: Gemini Deep Think
  • Agentic IDE: Google Antigravity IDE (using Gemini & Claude models)
  • Asynchronous Agent: Google Jules (experimental)
  • Context Engine: Context7 (indirectly via MCP)
  • Voice Interface: Wispr Flow

About

Agent Protocols is a structured framework of instructions, personas, skills, and SDLC workflows designed to optimize agentic AI coding assistants. It serves as a centralized, shared foundation to help LLM-based agents maintain code quality, architectural consistency, and professional standards across all your projects.

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