agent-bootstrap-kit
NewPatterns and skills for running a long-lived, AI-agent-driven software repo. Lessons + skills + a droppable CLAUDE.md/PRODUCT.md/docs template.
Summary
This skill provides a structured template and set of patterns for bootstrapping a long-lived AI-agent-driven software repository.
- md, and documentation templates, along with lessons and reusable skills to help developers maintain consistent AI collaboration workflows across their projects.
Install & Usage
mkdir -p .claude/agentsAdd the configuration to .claude/agents/agent-bootstrap-kit.md
@agent-bootstrap-kitUse Cases
Usage Examples
/agent-bootstrap-kit init my-new-project
Set up this repo with the agent bootstrap kit templates for CLAUDE.md and PRODUCT.md.
Apply the agent bootstrap kit to my current project to standardize agent interactions.
Security Audits
Frequently Asked Questions
What is agent-bootstrap-kit?
This skill provides a structured template and set of patterns for bootstrapping a long-lived AI-agent-driven software repository. It includes a CLAUDE.md, PRODUCT.md, and documentation templates, along with lessons and reusable skills to help developers maintain consistent AI collaboration workflows across their projects.
How to install agent-bootstrap-kit?
To install agent-bootstrap-kit: create the agents directory (mkdir -p .claude/agents), then add the config to .claude/agents/agent-bootstrap-kit.md. Finally, @agent-bootstrap-kit in Claude Code.
What is agent-bootstrap-kit best for?
agent-bootstrap-kit is a agent categorized under Documentation. It is designed for: agent. Created by chrismccann-dev.
What can I use agent-bootstrap-kit for?
agent-bootstrap-kit is useful for: Initialize a new repository with a standardized CLAUDE.md and PRODUCT.md for AI-agent collaboration.; Set up documentation templates that guide the AI agent on project conventions, architecture, and goals.; Apply lessons from previous agent-driven projects to avoid common pitfalls in long-lived repos.; Integrate reusable skills (e.g., testing, code review) into an existing agent-driven project.; Quickly onboard a new AI agent to an existing codebase by dropping in the bootstrap kit templates.; Establish a consistent communication pattern between human developers and AI agents across multiple repos..