Questions, answered straight.

AIGX (AI Genome Exchange) is an open, MIT-licensed context format for AI coding agents. It stores a codebase's rules in a centralized .aigx/ directory and adds a per-file boundary index - for each source file, the exact rules, forbidden imports, and gotchas that apply there. To our knowledge it is the only context format validated to win a controlled benchmark.

No. AIGX describes a codebase, not a single prompt. It is a persistent, structured memory an agent reads selectively at the edit site. You make any agent AIGX-aware with one instruction line added to its existing config; the agent then looks up only the context it needs per file.

Honestly, the top formats are a close cluster at matched power - AIGX is not a blowout over good Markdown. Its defensible edge is being first on every primary metric on both a weaker and a stronger model, surviving ~24 deliberate challenger variants, and being the simplest to author. A reproducible tie-at-the-top that holds across model tiers beats a fragile, format-specific spike.

We measured it. In-source // NEVER import X comments were neutral on the weak model and cost the strong model points (parse-noise), while cluttering every diff. The genome lives entirely in .aigx/ so your code and your diffs stay clean.

For parseability and stable, citable rule ids like ARCH-2. A genome is read by an LLM, not a strict XML parser, so well-formedness is recommended but not schema-enforced. Tags make the binding constraint for a file machine-locatable in one lookup.

It is the winning design the benchmark converged on: a lean per-file boundary index (role + a rare forbid + one gotcha + check-ids) plus terse per-concern rule files, with nothing injected into source. Every embellishment tested on top of it - salience tiers, routing tables, prose re-renderings - washed out or hurt.

The format is language- and domain-agnostic plain text. Its validated use is codebase context for coding agents, but the underlying laws - short and scarce, locality over position, one canonical source, measure don't assert - apply whenever you write context for an AI system.

Yes. AIGX is MIT-licensed and tool-agnostic by design. The Foundation exists to publish the spec, benchmark context formats, and collect examples - without locking the format to one model, one IDE, or one vendor. Use it, fork it, build products on it.

Copy the starter .aigx/ into your repo root, fill in files.aigx with one entry per file an agent realistically edits, and add the one-line agent addendum to your AGENTS.md / CLAUDE.md / Cursor rule. Run the bundled linter in CI so a moved file fails the build until its entry is fixed.

llms.txt is a docs index for answer engines - it points to your documentation. AIGX encodes codebase rules and boundaries for an agent that edits the code: per-file forbidden imports, gotchas, and verification checks. Different jobs; they compose well. This very site ships an llms.txt too.