slop-grader

slop-grader is a rule-based command-line interface (CLI) tool that evaluates documents against custom rulesets, producing a document score together with line-by-line flags. It is explicitly designed to guide auto-fixing with an AI agent: the flagged output includes prompts and instructions that can be pasted directly into an agent so the agent can draft sharper copy. The maker describes it as a tool that checks any text document against rules such as English grammar, German grammar, and AI filler detection, and it lists three immediate uses: catching AI filler in launch copy, stripping buzzwords from landing pages, and scoring narrative flow in launch emails. It is an open-source tool that requires Node.js on your machine and an account with either TypeSafe or OpenRouter. slop-grader speaks directly to a common side effect of AI-assisted writing. When launch copy, landing pages, emails, or other documents are drafted or polished with large language models, they often carry a recognisable residue of filler, buzzwords, and unexamined narrative flow. slop-grader is positioned as a way to detect that residue before a document ships. Crucially, it does not simply rewrite the text for you: it scores the document against a ruleset and marks the individual lines that fail, which is a lighter and more reviewable intervention. A commenter on the launch summarised the appeal of this approach, noting that the line-by-line flags are a useful touch because they make it much easier to see exactly what needs fixing instead of rewriting the whole document. The tool ships with rules that work out of the box, including English grammar, German grammar, and AI filler detection. Beyond those built-in checks, the maker emphasises that the real power comes from creating custom rules for your own use case, and these rules can be written in plain language. The examples given include SEO checks, legal clauses, and tone of address, such as keeping the German informal "Du" versus the formal "Sie" consistent throughout a document. Because the rules are user-authored and expressed in everyday language, the same tool can be adapted to very different kinds of content review without changing the underlying program. To help users get started, a built-in skill is provided in the project repository at skills/create-slop-grader-rules/SKILL.md, which is intended for creating custom rules. Rules in slop-grader are framed as questions, and those questions can be evaluated in two different scopes. Some rules are applied line by line, such as "Does this line make a promise that requires a legal disclaimer?" Others are applied across the entire document, such as "Does the opening earn the reader's next 30 seconds?" This dual scope means the tool can police both local wording problems and broader structural or narrative qualities. The maker notes that once you have built a curated ruleset for your use case, it can be a very powerful tool. In a reply to a commenter asking how the tool handles words that are considered buzzwords in one industry but normal in another, the maker's answer was simply that you can create custom rules for your use case, which keeps the definition of "slop" under the user's control rather than baked into the product. The output of a run is a list of flagged lines plus instructions, and those instructions can be pasted directly into an AI agent to fix the document. In other words, the tool flags its outputs as prompts so that agents can draft fixes. When asked whether the output can be used directly in an AI coding or writing agent workflow, the maker confirmed that the tool outputs a list of flagged lines and instructions that you can paste directly into an AI agent to fix the document. One nuance that came up in discussion is that the tool does not explain why a given rule fired. The maker's suggested workaround is to create separate rules for each check, because the AI agent is then very good at inferring the problem. Every rule is matched against every line separately, and because of the model it uses, evaluating every rule separately remains very cheap and fast, so this granular approach does not punish users with a slow or expensive run. Under the hood, slop-grader runs on Jev, the new AI model available at typesafe.ai. The maker describes Jev as a so-called "System One" model, different from an LLM, and specialised in answering structured questions. That specialisation is what makes the ruleset approach practical: checking a document takes seconds and costs less than a cent. Running the tool requires Node.js installed on your machine plus an account with either TypeSafe or OpenRouter, which means the product is deliberately lightweight and fits into an existing developer environment rather than requiring a separate application. Users should be aware that text is evaluated on an external AI server, as the maker states explicitly. The whole package is distributed as an open-source CLI, so the rulesets themselves become an artefact that a team can curate and reuse over time. The benefits that follow from this design are speed, cost, and precision. Because a document check takes seconds and costs less than a cent, it is feasible to run a ruleset repeatedly during a writing or launch process rather than treating it as a one-off audit. Because the results are line-level, the feedback is actionable and easy to review, and because the instructions are formatted for an agent, the fix step can be automated. And because rules are written in plain language and can be scoped either to a single line or to the whole document, a team can encode its own quality bar, from grammar and buzzword control to legal disclaimers and consistent tone of address. The maker's framing that a curated ruleset becomes very powerful once assembled suggests the tool rewards a small amount of upfront investment in rule authoring. The stated scenarios for slop-grader revolve around launch and marketing writing. You can use it to catch AI filler in launch copy, to strip buzzwords from landing pages, and to score narrative flow in launch emails. It can also be applied wherever a document needs to conform to conventions that a reader could phrase as a yes/no question, which is how the German "Du" versus "Sie" consistency example was presented in the comments. Rules such as checking whether a line makes a promise that requires a legal disclaimer, or whether an opening earns the reader's next thirty seconds, illustrate the kind of editorial and structural checks the tool is intended to carry out. In each case the flagged lines and accompanying instructions are then handed to an AI agent to draft the corrected text. slop-grader is aimed at people who produce written content and want a repeatable quality gate: makers writing launch material, marketing and advertising copywriters, and developers or technical writers comfortable working from a command line. It is listed as free, it is open-source and hosted on GitHub, and it runs as a Node.js CLI. The two supported routes for running it are an account with TypeSafe, the company behind the Jev model it depends on, or an account with OpenRouter. Because rules can be created in plain language with the help of a built-in skill, using the tool does not require writing code beyond installing and invoking the CLI. No mobile or web application is mentioned; the entire experience is a command-line workflow that plugs into an AI agent for the fixing step. In summary, slop-grader turns text quality review into a rule-based, scored, line-level check that is fast, inexpensive, and designed to feed an AI agent. Instead of asking a model to rewrite a document wholesale, it identifies precisely which lines break your rules, emits instructions the agent can act on, and lets you define what counts as slop through custom rules in plain language. With built-in checks for English grammar, German grammar, and AI filler detection, plus the ability to add SEO checks, legal clauses, and tone-of-address rules, it offers a configurable and lightweight way to keep launch copy, landing pages, and emails sharp.