gds-generate-project-context

Generates a project-context.md file capturing implementation rules for AI coding agents.

Updated Mar 14, 2026
One-click install
npx skills add https://github.com/ArchaonHW/MingGoRTS --skill gds-generate-project-context-archaonhw
Or copy as Structured Prompt for Agent
Please help me install this Agent Skill.
Skill: gds-generate-project-context
Source: https://github.com/ArchaonHW/MingGoRTS/tree/main/.agents/skills/gds-generate-project-context
Command: npx skills add https://github.com/ArchaonHW/MingGoRTS --skill gds-generate-project-context-archaonhw

SYSTEM DOCUMENTATION & REQUIREMENTS

What problem does it solve? AI agents working on a game project often miss unobvious conventions, version constraints, and project-specific rules, producing inconsistent code. This Skill runs a guided workflow to capture those critical rules into a single project-context.md file that agents read before implementing code. ## Core Features & Use Cases - Guided Discovery: Scans architecture documents, engine configs, and existing code to identify the technology stack, naming conventions, and coding patterns. - Collaborative Rule Generation: Walks through engine-specific, performance, organization, testing, and platform rule categories with user approval at each step. - LLM-Optimized Output: Produces a lean, scannable project-context.md with frontmatter tracking and usage guidelines. - Use Case: A game studio onboarding multiple AI agents to a custom C++ engine project uses this Skill to document dependency-direction rules, headless testing conventions, and build requirements so every agent generates compliant code. ## Quick Start Ask the AI to generate project context for this repository and follow the step-by-step discovery prompts.

Frequently Asked Questions about gds-generate-project-context

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

FAQPage Schema
How do I create a project context file for AI coding agents?

Run this workflow and it will scan your architecture documents, engine configs, and codebase to discover your technology stack and patterns. It then guides you through rule categories with approval menus before writing the final project-context.md file.

What should a project context file for AI agents include?

It should include exact technology versions, engine-specific lifecycle rules, performance constraints, code organization and naming conventions, testing requirements, platform rules, and anti-patterns to avoid. The focus is on unobvious details agents would otherwise miss.

Does this workflow support Unity, Unreal, and Godot projects?

Yes, the discovery step checks for engine-specific files like ProjectSettings, .uproject files, and project.godot, and the generation step includes tailored rule sections for Unity, Unreal, and Godot patterns as well as custom engines.

Can I customize the workflow steps and persistent facts?

Yes, the customize.toml file supports activation step overrides, persistent facts including file references, and an on_complete hook. Team and user-level TOML overrides merge with the defaults using structural merge rules.

What happens if a project-context.md file already exists?

The discovery step detects the existing file, reports how many sections it contains, and asks whether you want to update it or create a new one before making any changes.