teach-chiron

Scan project source files and generate a persistent .chiron-context.md.

2|Updated Apr 9, 2026
One-click install
npx skills add https://github.com/xDido/chiron --skill teach-chiron
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: teach-chiron
Source: https://github.com/xDido/chiron/tree/main/.cursor/skills/teach-chiron
Command: npx skills add https://github.com/xDido/chiron --skill teach-chiron

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve?

One-time comprehensive project scan that reads every important file in the codebase and writes .chiron-context.md — the persistent context file that all other chiron skills reference instead of re-scanning.

Core Features & Use Cases

  • Generates a complete, project-wide context for subsequent chiron skills.
  • Identifies and records API surfaces, data models, wiring/bootstrap, configuration, tests, and documentation.
  • Use Case: Onboard a new project or refresh context after major refactors to ensure all chiron skills have up-to-date context.

Quick Start

Run this command once per project to generate the persistent .chiron-context.md and refresh it after major refactors.

Frequently Asked Questions about teach-chiron

High-intent search queries and answers about installing and using this skill.

FAQPage Schema
How do I generate persistent context for an AI assistant from my codebase?

A project scan builds persistent context by reading important codebase files and exporting a root .chiron-context.md file. This maps source files and records API surfaces for downstream AI assistant automation skills.

Does project onboarding automation exclude generated artifacts when scanning source files?

Project onboarding automation enforces scanning rules that exclude generated artifacts during the project scan. It maps actual source files, data models, and configuration to ensure the persistent context remains accurate for AI assistants.

When do I need to refresh my codebase context file?

You need to refresh your codebase context file during major refactors or project updates. Running the project scan again ensures all API surfaces, wiring, and bootstrap configurations are up-to-date for subsequent AI assistant skills.

What is the best way to map API surfaces and data models for downstream automation?

The best way to map API surfaces and data models is a comprehensive project scan that writes a persistent .chiron-context.md file. This records structural codebase information for other AI assistant automation skills to reference.

Can I use the generated context file with other AI assistant skills?

Yes, you can use the generated context file with other AI assistant skills. The .chiron-context.md file serves as a persistent reference so downstream automation skills access project context without re-scanning the codebase.