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Deadpath
find the code your agents keep rewriting around
Deadpath finds dead code in your repo unused exports, orphan files, unreachable functions, and dependencies that nothing imports—then puts that scan where your coding agents work: Claude Code, Cursor, and Codex. Most “AI dead code” tools ask a model to guess what looks unused. Agents then rewrite around the same dead files week after week. Deadpath flips that. The core scanner is deterministic. It builds a reference graph from the codebase and reports high-confidence findings with evidence.
I kept shipping AI features and watching the same failure mode: agents and humans both work around dead code instead of removing it.
We would leave an unused helper “just in case,” add a new route beside an old one, keep a package in the lockfile after the import was gone. On the next task, Claude or Cursor would route around those files again. The dead path stayed in the tree, and the cost showed up as slower reviews, noisier diffs, and more tokens spent understanding code that never ran.
I tried the usual tools. Linters catch unused locals. Bundle tree-shakers help at the edge. Full “AI review” products comment on the diff, not the whole graph. Nothing gave me one local command that said: here is the evidence this export is never reached—and nothing put that same answer inside Claude, Cursor, and Codex without three different products.
So I built Deadpath: a deterministic dead-code scan first, optional model explain second, and thin plugins so agents call the scanner instead of guessing. No auto-delete. A plan you can turn into a PR.
It sits next to the other tools I’m building in public—Cosen for cost/traces/security at runtime, DeadPath for pre-merge review across UX, API, performance, and engineering. Deadpath is the hygiene step: prove what nothing still reaches, then cut on purpose.
If you try it, run deadpath scan --mock on your messiest repo and tell me what it gets wrong. False positives are the product; I want those reports.
About Deadpath on Product Hunt
“find the code your agents keep rewriting around”
Deadpath was submitted on Product Hunt and earned 3 upvotes and 1 comments, placing #120 on the daily leaderboard. Deadpath finds dead code in your repo unused exports, orphan files, unreachable functions, and dependencies that nothing imports—then puts that scan where your coding agents work: Claude Code, Cursor, and Codex. Most “AI dead code” tools ask a model to guess what looks unused. Agents then rewrite around the same dead files week after week. Deadpath flips that. The core scanner is deterministic. It builds a reference graph from the codebase and reports high-confidence findings with evidence.
On the analytics side, Deadpath competes within Open Source, Developer Tools, GitHub and Development — topics that collectively have 636.9k followers on Product Hunt. The dashboard above tracks how Deadpath performed against the three products that launched closest to it on the same day.
Who hunted Deadpath?
Deadpath was hunted by Vivek Singh. A “hunter” on Product Hunt is the community member who submits a product to the platform — uploading the images, the link, and tagging the makers behind it. Hunters typically write the first comment explaining why a product is worth attention, and their followers are notified the moment they post. Around 79% of featured launches on Product Hunt are self-hunted by their makers, but a well-known hunter still acts as a signal of quality to the rest of the community. See the full all-time top hunters leaderboard to discover who is shaping the Product Hunt ecosystem.
For a complete overview of Deadpath including community comment highlights and product details, visit the product overview.
Vivek Singh