Hi HN, I built farol at Winnin, where we open-sourced it (MIT).

AI agents now write a lot of our code, and AI reviewers catch a lot of the line-level problems. But someone still has to press "merge", and that person has the same question every time: how risky is this, and where should I spend my attention? A 40-file PR where 38 files are isolated is very different from a 3-file PR that changes a model used by 23 endpoints.

farol is a CLI (and a GitHub Action) that answers that statically:

- classifies the PR (global / touches-existing / isolated / unknown) and gives a 0-100 merge risk score - ranks the files by risk and says why: what each one reaches (routes, endpoints, jobs), contract breaks in API responses, dependency changes, security patterns - diff coverage from your existing coverage report, so "38% of the changed lines are tested" sits next to the risky files - an HTML report where you mark files as reviewed and the score and test plan follow; incremental mode for "what changed since my last review"; suggested reviewers from git blame

How: it uses the TypeScript compiler API for TS/React (React Router, Next.js, TanStack Router) and TS backends, a small go/types helper for Go, and Python's ast for FastAPI/Flask/Django and friends. No LLM, no network during analysis, and it never executes the analyzed repo's code. It's deterministic and informs only; it never blocks a merge.

Live demo (two reports from a real PR, nothing to install): https://winnin.github.io/farol/demo/

Try it on a branch: npx @winninjs/farol --help

Limitations: only TS, Go and Python for now, and the reachability is static, so dynamic dispatch and reflection make it fail closed ("unknown") rather than guess. I'd love feedback on the risk score and on false positives in your codebase.