Fewer repeat incidents
Known failure classes stop sneaking back in on the next human or AI diff.
JavaScript and TypeScript — one bar for every author
Within that coverage, it nudges code toward senior practice. It does not merge for you — you fix, waive, or ignore on purpose.
Shared rules cut spaghetti early, make onboarding lighter, and trim repeat bugs so new work spends less time on old debt.
What that tends to mean:
Known failure classes stop sneaking back in on the next human or AI diff.
New hires and agents learn the real rules from output, not from chasing tenured engineers.
Less silent debt means less rework — more calendar for what you planned to build.
Junior, senior, or AI: same repo, different habits.
Lintora checks every change against the policies you turned on and keeps quality consistent.
Before — secrets in the query string (GET)
async function login(user, pass) {
const res = await fetch(`/login?user=${user}&pass=${pass}`);
return res.json();
}After — POST JSON body, typed parameters, explicit HTTP errors
async function login(user: string, pass: string) {
const res = await fetch(`/login`, {
method: "POST",
headers: { "Content-Type": "application/json" },
body: JSON.stringify({ user, pass }),
});
if (!res.ok) {
throw new Error(`login_failed:${res.status}`);
}
return res.json();
}WHAT LINTORA REPORTS (MESSAGE)
ACTION REQUIRED: Remove token from logs or URL parameters. Sensitive credential exposure detected.
WHY IT MATTERS (ROOT CAUSE)
Identification and Authentication Failures (OWASP A07). Authentication tokens exposed in plain text within logs or URLs can be intercepted by unauthorized parties, leading to session hijacking and full account compromise. URLs are logged by various systems (proxies, browser history), and log files are frequent targets for internal reconnaissance.
WHAT TO DO (FIX)
1. IMMEDIATELY remove tokens from log messages and URL strings. 2. USE the 'Authorization' header (e.g., Bearer <token>) for all API requests. 3. LOG only non-sensitive metadata such as userId or correlationId. 4. REDACT or MASK the token if it must be logged for critical troubleshooting. 5. IF exposure is manually verified as safe or masked, use the explicit exception: // lintora-disable-next-line SEC-001-027.
AI writes code.
Lintora enforces standards.
Make AI-generated code production-grade.
Lintora is a policy enforcement tool for codebases that ensures consistent quality across human and AI-generated code. It runs a versioned catalog of named policies on JavaScript and TypeScript, reports structured findings with stable IDs, and supports the same workflow locally and in CI.
A model-generated pull request adds a new API client with permissive TLS, silent error swallowing, and credentials read from query strings. ESLint stays green because the file is formatted and imports resolve. Lintora flags the security and validation policies you enabled, cites each hit with a stable policy ID, and blocks merge until the team fixes or explicitly waives the rule. The same bar applies whether the author is a human or an agent.
Thread mirrors the policy spotlight above: same catalog-style message, root cause, and fix text Lintora emits for that rule.
Lintora is a static analysis and governance-as-code engine for JavaScript and TypeScript. It runs a curated catalog of named policies, each with a stable ID, on every change so human, junior, and AI-generated diffs face the same merge bar. It reports structured findings; it does not silently auto-merge or blanket auto-fix production logic in CI.
ESLint excels at style and plugin rules you configure yourself; SonarQube often powers hosted dashboards and multi-language programs. Lintora focuses on semantic engineering policies for JS and TS, ships rules inside the dependency you pin, runs locally and in CI with deterministic output, and pairs cleanly with ESLint for layout while acting as a fast in-repo policy gate where SonarQube is heavier or broader than you need.
Yes. Lintora treats the diff as the source of truth: every pull request is checked against the same enabled policies regardless of author. High-volume AI edits get the same stable IDs, severities, and waiver mechanics as human-written code, so risky patterns surface before merge.
You enable policy families and paths for the repository, pin @lintora/core, and run npx lintora run locally and in CI. Each policy maps to AST-first runners (with type-aware hooks where needed), emits findings with stable IDs you can gate on, and supports intentional waivers per rule instead of silent blanket disables.
AI and juniors ship volume; seniors disagree on details. Without a shared check, quality becomes whoever touched the file last.
If "done" is not defined in the repo, every merge is a lottery.
Lintora catches drift on the diff while it is still cheap to fix.
You stay in charge. Lintora only surfaces where the code diverges from policies you agreed to run.
Each finding is:
Install @lintora/core from npm, run npx lintora run at the repo root, mirror the same command in CI. Flags and config: README on the npm package page and this site's CLI guide.
Stop letting quality depend on who — or what — wrote the last change.
Policy counts on this page come from the same registry the engine ships.
Full CLI and configuration: @lintora/core on npm.
Static analysis for JavaScript and TypeScript: AST-first runners, optional type-aware hooks. Policy logic ships in the package you pin — not from a hosted rule sandbox.
Run Lintora on the repo. Output lists every enabled policy break — fix on your terms, then wire the same command in CI.
Governance-as-code for JS/TS: many policies, each with a focused runner and optional type hooks.
What ships in the package is what your team validated.
Boundaries keep expectations honest.
Pair Lintora with strict TypeScript, tests, and review for full assurance.