Prelint
Checks every pull request — including the ones written by an AI coding agent — against your actual product docs and past decisions, and flags it when the code technically works but quietly does the wrong thing, before a human has to catch it after the fac
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Description
AI coding agents are fast at writing code that compiles and passes tests, but they don't automatically know your pricing rules, your compliance requirements, or a decision your team made three months ago that isn't written down anywhere obvious. Prelint's whole premise is catching that gap: it reads each pull request alongside your product specs, tickets, and existing context, and flags when a change conflicts with a business rule — wrong pricing logic, a broken discount flow, a compliance requirement like data retention or consent handling — even though the code itself looks fine and passes normal tests.
It started as a GitHub-integrated reviewer and now also works via CLI and MCP, so an AI agent can check its own product decisions against your specs while it's still working, instead of a human discovering the problem only after merge. It reads Markdown or YAML spec files, supports both GitHub and GitLab, and explicitly runs on tenant-isolated, zero-retention infrastructure. Pricing is usage-based: $1 per completed review, with $10 in free credits to start (no credit card) and free use for open source projects.
💬 Our review
The short version: as more code gets AI-generated, "does it compile and pass tests" stops being a sufficient check, and "does it match what we actually decided to build" becomes the real gap — Prelint is one of the more direct attempts to automate that specific check rather than general code-quality linting.
It's a genuinely different lane from typical AI code reviewers that focus on bugs, security or style; Prelint's differentiator is grounding review in your own specs and past decisions rather than generic best practices, which only works as well as the specs you feed it stay current. At $1 per review with $10 free credits and free open-source usage, the entry cost is low enough to trial on a real repo before deciding whether the specs-alignment check earns its place next to your existing code review process — worth it specifically for teams leaning hard on AI agents for implementation, less obviously necessary for teams where a human still designs every change.
💰 Pricing
📊 Global score
🤖 AI-enriched data
1$ par revue complétée ; 10$ de crédits gratuits pour démarrer, pas de CB requise ; gratuit pour les projets open source
Pros
Vérifie l'alignement produit/specs, pas juste le style ou les bugs de code
Fonctionne via GitHub, GitLab, CLI et MCP
Coût d'entrée très faible (1$/revue, crédits gratuits)
Cons
Qualité de la revue dépend directement de specs tenues à jour
Pas de concurrents nommés sur le site pour comparer directement
Approche encore jeune, peu de retours d'usage à grande échelle