ai-context-kit

AI Context Kit

Author: Marco Siccardi (MSiccDev Software Development)
Purpose: An AGENTS-first, skills-first framework for maintaining consistent, context-aware AI collaboration across different LLM providers, projects, and development phases.

Overview

This repository provides a comprehensive AGENTS-first context and workflow system designed to enable consistent, context-aware AI collaboration across multiple projects and platforms.

Primary Agent Entrypoint

• Root AGENTS.md is the primary operational entrypoint for agents in this repository.
• specs/context_aware_ai_session_spec.md is the normative source of truth for the full session model.
• docs/spec-rationale.md is the companion document with background reasoning, extended examples, and future enhancement notes.

The Evolution

What began as a way to extract and reuse prompts across AI providers has evolved into a sophisticated architecture for AI collaboration:

• Layered architecture – Personal user context + project AGENTS.md context create complete AI workspace configurations
• Skill-based workflows – Create and validate context files using reusable skills with detailed operational logic
• Provider-agnostic – Designed to work across different LLM environments; results may vary by platform

The System

This framework consists of:

• Personal user context artifacts – Your professional identity, skills, preferences, and working style
• Project-specific AGENTS.md context – Scope, tech stack, roles, objectives, and operational guidance per project
• Skill workflows in skills/ – Canonical create/validate/governance operational logic
• Session specification – How AI assistants should maintain and adapt context during work sessions
• Templates in templates/ – Canonical artifact structures aligned to the spec

All components are designed to work across hosted and local LLM environments, ensuring that every AI assistant understands your background, working style, and project context without repeated explanations. You may need to adjust your context files for specific platforms.

See It In Action

The same question, with and without AI Context Kit loaded:

Without AI Context Kit:

User: "Help me add authentication to the app."

AI: "Sure! What framework are you using? What kind of auth — OAuth, JWT, sessions? What's your backend stack?..."

The AI starts from scratch every session.

With AI Context Kit loaded:

User: "Help me add authentication to the app."

AI: "Given your SwiftUI codebase and the TCA architecture you're migrating to, I'd model this as a new AuthenticationFeature reducer rather than adding it to an existing one. Want me to scaffold the reducer and effect first, or start with the SwiftUI view bindings?"

The AI already knows your stack, your existing patterns, and your preferences.

The difference is not magic — it is reduced friction. The AI spends zero turns asking who you are, what you are building, or how you like to work. That adds up significantly across a full development session.

Repository Structure