slop-grader

slop-grader is a rule-based command line tool that evaluates documents against custom rulesets and produces document scores and line-by-line flags to guide auto-fixing with an AI agent. It is built for anyone who writes, edits, or reviews text and wants that work checked against an explicit, repeatable standard rather than a vague impression. The tool is open source, runs locally in a terminal, and is designed around the idea that you curate a ruleset for your own use case and then reuse it as a powerful way to grade text consistently. Rather than rewriting a document for you, slop-grader grades it, tells you which lines failed which rules, and hands off instructions that an AI agent can act on to produce sharper copy. The context behind slop-grader is the flood of AI-generated writing. Text produced quickly by large language models tends to be padded with filler, stacked with buzzwords, and vague about what it actually promises. Reviewing that text by hand is slow, subjective, and inconsistent: two readers can disagree about whether the same paragraph is clear, and nobody notices a slow drift in tone across a long document until a reader finally complains. slop-grader takes a different position by making each check explicit. Instead of asking whether a document reads well, you express what you care about as a rule, and the tool answers that rule for every line it reviews. The result is a score plus a list of specific lines that need attention, which makes the standard applied to a document visible and repeatable. The heart of the product is the ruleset. Rules are written in plain language and can be anything you are able to phrase as a question. Some rules are evaluated line by line, such as "Does this line make a promise that requires a legal disclaimer?" Others are evaluated across the entire document, such as "Does the opening earn the reader's next 30 seconds?" That flexibility means the same tool can cover very different jobs: SEO checks on a page, legal clause review, tone of address, or keeping a formal or informal register consistent. A concrete example given by the maker is German address: keeping "Du" versus "Sie" consistent throughout a document. Because rules are yours to define, words that are buzzwords in one industry but completely normal in another are handled by simply writing the rule that fits your use case. slop-grader also ships with built-in rules, so you do not have to start from nothing. The included checks cover English grammar, German grammar, and AI filler detection. The maker notes that once you have built a curated ruleset for your use case, the tool becomes very powerful, and there is a built-in skill in the repository for creating custom rules, published as a SKILL markdown file on GitHub. That skill is intended to help you author the plain-language rules your workflow needs, whether they concern promises and disclaimers, openings that hold attention, SEO conditions, legal clauses, or tone of address. The output format is deliberately practical. slop-grader produces document scores and a list of flagged lines together with instructions that you can paste directly into an AI agent to fix the document. The tool flags its outputs as prompts so that agents can draft the fixes. This matters because a line-level flag makes it much easier to see exactly what needs fixing instead of rewriting an entire document from scratch. One product detail worth knowing is that when a rule fires on a line, the tool does not explain why it thinks the rule was violated. The maker's suggested workaround is to create separate rules for each check; the AI agent receiving the output is then very good at inferring the underlying problem from the flagged lines. Under the hood, slop-grader runs on Jev, available at typesafe.ai, which is described as a new AI model that is different from an LLM. Jev is a so-called System One model specialized in answering structured questions. That specialization is what makes the rule-by-rule approach practical: every rule is matched against every line separately, and because of the model's design that evaluation stays both cheap and fast. Checking a document takes seconds and costs less than a cent. Because evaluation happens on a remote model, the maker notes that text is evaluated on an external AI server, which is a relevant consideration when you decide what to run through the tool. The practical benefits follow directly from that design. Grading is consistent because the same rules are applied to every document and every line, so the standard does not drift between reviewers or between sessions. Feedback is precise because it arrives as flagged lines rather than a general verdict, which narrows the editing work to the specific places that broke a rule. The pipeline is fast and inexpensive enough that running a full check on a document takes seconds and costs less than a cent, so it can be part of a regular workflow rather than an occasional deep review. And because the output is written as instructions for an agent, the handoff from "this line is wrong" to "here is a sharper version" is automated rather than manual. Day-to-day, the maker describes using slop-grader to catch AI filler in launch copy, to strip buzzwords from landing pages, and to score narrative flow in launch emails. Those examples show the range: launch copy is checked for filler, landing pages are checked for buzzword density, and launch emails are graded on whether the narrative flows. Beyond those, the ruleset model supports SEO checks on content, review of legal clauses and disclaimers, and tone-of-address enforcement such as keeping German "Du" versus "Sie" consistent across a document. In each case the workflow is the same: run the document through your ruleset, read the score and the flagged lines, then paste the flagged output and instructions into an AI agent so it can draft the fixes. Getting started has a few stated requirements. You need Node.js installed on your machine, and you need an account with either TypeSafe or OpenRouter, since the evaluation runs on Jev. The tool is open source and is distributed as a CLI, and it is listed as free. The project lives on GitHub, and the maker has also published a skill for creating custom rules in the repository. It was launched on Product Hunt and is categorized as a command line tool, with topics covering writing, advertising, artificial intelligence, and GitHub. Text is evaluated on an external AI server, which users should factor into what documents they submit. The takeaway is that slop-grader reframes text review as a linting problem for prose. By turning what you care about into plain-language questions, evaluating them line by line and document-wide with a fast, low-cost structured-question model, and emitting flagged lines alongside agent-ready instructions, it replaces subjective rewriting with a score, a precise list of problem lines, and a clear path to an automated fix.