How do you measure whether a repository is ready for AI contributors?

Filippo — Thu, 03 Sep 2026 14:27:47 +0000

A repository can look healthy to a human contributor while leaving an AI coding agent without clear operating rules.

A README may explain the project. CONTRIBUTING.md may explain how to submit a pull request. CI may verify the result.

But several questions can remain unanswered:

That is a different problem from conventional repository health.

A deterministic approach

I built RepoPolicyScore to evaluate this problem using published, deterministic checks:

The current scoring model contains 25 checks grouped into seven areas:

The same repository evidence evaluated with the same scoring version produces the same result.

An LLM does not decide whether a repository “feels” ready.

That distinction matters because maintainers need findings they can inspect, reproduce and challenge.

RepoPolicyScore keeps AI Contribution Readiness and Repository Health separate.

Repository Health examines operational signals such as:

These signals matter, but they do not prove that an AI coding agent understands its boundaries.

Combining everything into one score would hide that distinction.

Identifying a missing rule is only useful if the maintainer can close the gap.

RepoPolicyScore therefore provides evidence behind its findings, prioritizes fixes and can generate repository-specific policy wording.

The intended workflow is:

The scanner is read-only. It requires no account or OAuth, doesn’t clone the repository, doesn’t execute its code and doesn’t request write access.

The product is live, but the scoring model needs exposure to more real repository structures.

The most useful feedback is not whether the page looks good. It is:

If you maintain a public GitHub repository, scan it and challenge the result:

I’d rather find weaknesses in the model now than preserve a score that maintainers cannot trust.