slop-grader
A rule-based command-line tool for detecting and scoring clichés, filler phrases, and marketing buzzwords (slop) in AI-generated copy. It supports custom rule sets, line-by-line issue flagging with fix suggestions, and can be integrated into automated writing pipelines for use alongside AI agents.
Timeline (last 90 days)
slop-grader is a rule-based command-line (CLI) tool for detecting filler content in AI-generated copy, outputting an overall document score and line-by-line annotations.
slop-grader was created by developer Lukas.
slop-grader received 82 upvotes and 10 comments on Product Hunt, ranking 11th.
slop-grader is categorized on Product Hunt under Writing, Advertising, Artificial Intelligence, and GitHub.
slop-grader allows users to define custom rule sets to evaluate document quality.
While flagging problematic lines, slop-grader outputs corresponding prompts that can be fed directly to an AI Agent to draft replacement copy.
slop-grader's workflow chain is: rule detection → line-by-line flagging → fix prompt generation → Agent rewriting.
Typical use cases targeted by slop-grader include catching AI filler content in launch copy, cleaning marketing jargon from landing pages, and evaluating narrative flow in launch emails.
The actual effectiveness of slop-grader is highly dependent on the quality of the rule set, as poorly defined rules can lead to distorted scoring.
All Facts (8)
slop-grader is a rule-based command-line (CLI) tool for detecting filler content in AI-generated copy, outputting an overall document score and line-by-line annotations.
50%Unverifiedslop-grader was created by developer Lukas.
50%Unverifiedslop-grader is categorized on Product Hunt under Writing, Advertising, Artificial Intelligence, and GitHub.
50%Unverifiedslop-grader allows users to define custom rule sets to evaluate document quality.
50%UnverifiedWhile flagging problematic lines, slop-grader outputs corresponding prompts that can be fed directly to an AI Agent to draft replacement copy.
50%Unverifiedslop-grader's workflow chain is: rule detection → line-by-line flagging → fix prompt generation → Agent rewriting.
50%UnverifiedTypical use cases targeted by slop-grader include catching AI filler content in launch copy, cleaning marketing jargon from landing pages, and evaluating narrative flow in launch emails.
50%UnverifiedThe actual effectiveness of slop-grader is highly dependent on the quality of the rule set, as poorly defined rules can lead to distorted scoring.
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