Show HN: Argot, a Rust AI guardrail based on your codebase AST patterns

Argot, a Rust-based AI guardrail that uses a repository's AST patterns to detect foreign code changes, reports 97.3% recall on foreign patterns and 0.25% false positive rate on real edits, checking a diff in 200 ms. The tool, introduced on Hacker News, learns from a codebase's history to flag changes that are out of place, with benchmarks showing 620/637 (97.3%) fixture recall and 264/272 (97.1%) real recall across 36 and 25 corpora respectively, spanning 12 languages. Argot operates as a static binary with tree-sitter adapters and learned models per language, and integrates via CLI, skills, commit hooks, or GitHub Action.

Learn the repository argot init learns the voice, semantic neighbours, architecture, and test signals on your machine. AI writes the code. argot harnesses it with the one thing that can’t hallucinate: your repo’s own history . Deterministic, measured, local. 97.3% of foreign patterns caught · 0.25% of real edits flagged checks a diff in200 ms A concrete example Clean, type-correct PRs can still be foreign to your repository. This is its real output. Why this matters A green check can hide a weakened test. argot pairs it with the changed code and names both. 154/164 93.9% — detector-specific fixture catch; controls and accepted-history results are separate. Read the detector scope and controls. /benchmarks Evidence you can reproduce argot audit compares accepted changes with the repository history before them. Findings are prompts to inspect, not defect verdicts. Then run argot init and choose a recurring check path. Measured, not promised 97.3% 620/637 97.3% — detector-specific fixture recall; not a product-wide accuracy claim. Fixture recall; the changed symbol is visible in the diff. 36 corpora / 12 languages / visible foreign-import and foreign-api fixture symbols after excluded-host purge · d1007f50; generated at=2026-07-28 97.1% 264/272 97.1% — detector-specific real recall; control result is separate. Authored internal layering violations across 25 corpora and 12 languages. 25 corpora / 12 languages; authored internal layering violations · 7886a7967c6fe5b822a249083c7871894db1efcf; generated at=2026-07-20 93.9% 154/164 93.9% — detector-specific fixture catch; controls and accepted-history results are separate. Authored production fit-to-check gaming fixtures. 23 corpora / 12 languages; authored test-gaming fixtures, production fit→check · 1b0de32d; just integrity-verify, 2026-07-28 One static binary . Twelve languages — each with its own tree-sitter adapter and its own learned model: Recall on patterns planted in real files; false alarms on a temporal holdout. Even the structural blind spot — masked foreign — is published, not hidden. How it works Run argot init , then choose the CLI, skills, a commit hook, or a GitHub Action. The Claude plugin adds a narrow pre-write prompt — not a full acceptance-time check. Install the CLI seven on-demand skills for compatible hosts: /argot-setup reads your tree, writes argot.toml, verifies the catch /argot-refresh reviews scope and mutes, then refreshes the learned snapshot /argot-check scores each diff, flags what’s foreign — never blocks /argot-review-pr reviews one PR against your repo’s voice, no checkout /argot-setup-ci a non-blocking voice score on every PR /argot-write-rule turns a convention you state into a tested rule /argot-suggest-rules finds your conventions, codifies oneThe Claude plugin adds optional MCP context and a narrow, fail-open pre-write prompt; agents still decide when to call Argot. Get the plugin → /docs/plugin/ bash $ argot init Step 1/2: training voice model … Step 2/2: calibrating threshold … Corpus 1129 files · 503 learned · 626 tests/docs skipped Verdict: Ready Snapshot → .argot/ · voice · semantic index · health Review + commit → argot.toml · .argot/ Next: argot check The learned snapshot is committed; caches stay local. CI reads the base branch copy and never fits. The whole model, in one glance Argot’s learned state is a reviewed snapshot in Git , not a hosted service. Every developer, agent, and PR compares against the same repository memory. argot init learns the voice, semantic neighbours, architecture, and test signals on your machine. Review and commit argot.toml + .argot/ . It is repository-specific learned state, like a lockfile for checks. Local tools, agents, and CI read that snapshot. A PR is judged against the base branch , so it cannot teach itself. After material accepted drift, status recommends /argot-refresh : review changed scope and mutes, fit locally, recommit. Integrations The GitHub Action reads the committed base snapshot and is non-blocking by default. Intentional divergence remains a human decision, recorded as an audit trail. Start here: open each row in the review queue. crates/argot-cli/src/voice diff.rs:202–282 Evidence: option, highlighted, Move — unfamiliar vocabulary here. Review: rewrite in the repository’s established form if unintended. crates/argot-core/src/compose.rs:1–14 Evidence: Lazy::new is not used by similar code here. Review: compare the call with the repository’s established API. 💬 Prompts for review, not proof of defects. main · CI never fits.Findings land on the PR. Snapshot health stays visible in the same summary. Under the hood Four local engines, one static Rust binary — model included, nothing fetched — all grounded in your git history. A 15.6 MB table argot distilled from a code encoder turns every function into a vector — how argot knows you already wrote this . Nothing to download, no GPU, no cloud: it ships compiled in and works air-gapped. Two frequency tables and a callee clustering — the imports, callees, and token shapes your repo actually uses . Your module-dependency topology. A new edge that reverses the established direction is flagged with the direction it breaks. tree-sitter tracks what every test asserts. A test skipped, gutted, or deleted beside a prod change gets paired and named. Measured on FastAPI, laptop CPU. Single static binary — no Python, no Node, no model download, no GPU. Advanced capabilities argot conventions finds the shared API and where code belongs. Turn one convention into a small, testable rule. where each kind of code lives — learned from your layout // check.rhai — a route that existed before // this change, and is silently gone now let now = ; for m in ts query ROUTES { now.push m.text ; } for m in ts query old ROUTES { if now.contains m.text { report m.line, m.text + " removed — deprecate first docs/api-lifecycle.md " ; } } MIT-licensed open source. Audit first, then choose the recurring check that fits your workflow.