AI Context Kit: A Portable Instruction System for Context-Aware AI Collaboration

Today I’m releasing “AI Context Kit”, a template repository that aims to make AI collaboration repeatable, portable, and predictable across LLM providers.

If you use more than one model (or more than one project), you already know the problem: context gets lost, instructions drift, and the same prompt yields wildly different results. I built AI Context Kit to fix that with a structured instruction layer that stays stable across tools and sessions.

Background

This project started as a simple experiment: extracting prompts I kept reusing across different AI providers. Very quickly I noticed the real problem wasn’t the prompts — it was the missing context. Each tool had its own format, I had multiple projects running in parallel, and I kept re‑explaining the same things.

So the idea evolved from reusable prompts to a layered instruction system: a personal user context + project‑specific instructions, backed by a spec. That architecture is what eventually became AI Context Kit.

What does it solve?

Most AI workflows start as ad‑hoc prompts. That works for quick questions, but breaks down in real projects when:

• roles and phases change over time,
• output format needs to stay consistent,
• project rules must be enforced,
• and different models interpret the same request differently.

AI Context Kit turns this into a formal, reusable system.

What do I get with AI Context Kit?

The repo gives you a spec + templates + prompts approach:

1. A formal specification for session state, roles, phases, and command handling
2. Canonical templates for user context and project instructions
3. Prompt workflows to create and validate instruction files

That means you can define your context once, reuse it everywhere, and keep it versioned like any other part of your workflow.

Diving into the repository

If you only read one file, start with the spec:

• specs/context_aware_ai_session_spec.md
  This is the authoritative model for session state, transitions, and command namespacing.

Then look at the canonical templates:

• templates/usercontext_template.instructions.md
• templates/project_template.instructions.md

And finally the prompt workflows:

• prompts/create-usercontext-instructions.prompt.md
• prompts/create-project-instructions.prompt.md
• prompts/validate-usercontext-instructions.prompt.md
• prompts/validate-project-instructions.prompt.md

Command namespacing (important)

One design choice I’m enforcing here is namespaced commands. It prevents collisions when you load multiple instruction sets.

Example:

• /ack.context
• /ack.mode developer
• /ack.phase implementation

The spec documents the generic pattern as /tag.command, and this project uses /ack.* as its namespace.

Quick start

This is a template repository. To get started:

1. Click “Use this template” on GitHub and create a new repository.
2. Set the repository to Private (recommended, since your user context will probably contain personal data!)
3. In your new repo, use the creation prompts to generate your user context and project instructions.
4. Validate both files with the included prompts.
5. Load them into your AI tool of choice.
6. For updating your repository when the template evolves, follow the instructions in README.md.

That’s it. From there, the AI should remain more consistent across sessions and tools.

Who is this for?

This is for AI‑interested developers who want:

• consistent collaboration across projects,
• a reusable instruction system,
• and a clear way to manage AI session state.

If you’re already using prompts and instructions but feel like you’re constantly re‑explaining context, this is for you.

The repo

You can find the AI Context Kit repository here on my GitHub account: AI Context Kit Repository

If you try it, I’d love to get feedback! I am looking forward to hearing your experiences, especially from people running multi‑project or multi‑LLM workflows. Feel free to comment below, open an issue on the repo, or reach out to me on Mastodon, Bluesky, or LinkedIn.

Outlook

This is just the beginning. If you look at the repository, you’ll already see a few issues for future improvements. My plan is to keep evolving the spec and templates based on real‑world usage - that’s why your feedback is so important!

Until the next post, happy coding, everyone!

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Title image note: The title image of this post was generated with the help of AI. It visually represents AI Context Kit as structured infrastructure — a stable, secure instruction layer at the center of complex, multi‑model AI workflows.

Disclaimer: This blog post was written with the help of AI. I provided the structure and key points, reviewed the draft, and edited it to fit my style and technical standards.