What happens when you press Scan.

No. SlopZero combines deterministic sensors and high-probability lessons learned from production codebases with specialized decision models. Instead of sending your whole repository to a general chat model, we map how your app connects, extract the precise code tree for each execution path, and pass those trees to our decision models to evaluate the deeper context of your code.

Built-in checks in app builders and hosting platforms catch single-file or build-time issues, like a missing environment variable or a failed build. SlopZero maps how your routes, data access, background work, and external services connect across files, then passes the precise code trees to specialized decision models so we catch multi-file architectural and scaling risks that only show up under real traffic.

Checking one file in isolation flags noise whenever an auth guard, validator, or limit lives a hop away in an imported helper or middleware. SlopZero assembles the precise code tree along each call and import path and passes it to specialized decision models. Paths that are already handled in context are filtered out, so your report and coding agent only receive confirmed issues ranked by severity.

When our decision models confirm a risk over a code tree, SlopZero already knows the entrypoints, helpers, and call path involved. Instead of handing your coding agent a vague file warning that forces it to search your repository and burn context tokens, SlopZero delivers the confirmed finding with structured remediation steps and the exact files along the path, then verifies the fix on a follow-up rescan.

Local testing runs with one user, a warm process, and a handful of rows. Architectural and scaling risks only surface when concurrent users, larger tables, slow third-party networks, and process restarts happen at the same time. SlopZero traces how your execution paths behave under production conditions before real users hit them.

Your formatter, linter, and CI pipeline already handle code style, file length, and dead code. A 500-line file can be perfectly safe in production, while a 30-line route handler can stall under real traffic. We focus the scan on runtime reliability, trust boundaries, data isolation, and scaling under real traffic.

No, not unless you explicitly opt in. Customer scans never feed lessons into our learning system unless you turn on training for a specific GitHub repository in your settings, and folders uploaded from your coding agent are never used for training.

No. We do not execute your code, install packages, or write to your repository. Analysis is read-only over a shallow clone on the web, or over the folder your agent uploads. The GitHub App is read-only on the repos you tick, and you can revoke it. Agent uploads never write back to git.

We keep a read-only snapshot of your code next to the report for as long as your plan keeps history (7 days on Free, 30 on Starter, 1 year on Pro, unlimited on Max), then delete it. During judgment, only the extracted code trees are evaluated by our decision models, never your whole repository. When scanning from the CLI, .env files and private keys stay on your machine.

Tell your agent “Scan with slopzero.dev”, or connect over MCP. It scans the folder you are working in, including unpushed changes, while .env files and keys stay on your machine. Sign in with GitHub or Stripe Link. Every credit pack or monthly plan waits for your approval before anything is charged. The agent pulls in the confirmed issues and their remediation steps, applies the fixes, and runs a follow-up scan to confirm they closed. More on using SlopZero from your agent.

A collection of SKILL.md files, clever audit prompts, or linter wrappers that ask an LLM to evaluate your repository still depends on an expensive frontier model exploring your codebase from scratch. You get probabilistic results where two runs on the same commit disagree, heavy token burn, and a bloated context window right when your coding agent needs to focus on fixing code. SlopZero runs outside your agent's context window, judges precise code trees with specialized decision models, and returns verified findings with actionable remediation.