CleanSlop vs Humanizer: Features, Pricing & Which Is Better (2026)

A side-by-side comparison of CleanSlop and Humanizer — features, pricing, and ideal use cases — to help you decide which AI tool fits your workflow.

CleanSlop

Freemium

Scans vibe-coded apps for launch-blocking issues like leaked keys and data exposure, and reports what to fix first, right from your coding agent.

Agent-Native Scanning: Tell Claude Code, Codex, Cursor or other agents to scan with CleanSlop and fix findings where you build.
System-Level Code Mapping: Maps routes, background jobs, data and external services so each finding follows a real call path.
Prioritized Reports: Findings grouped as needs attention, worth a look and minor, worst first.
Evidence for Every Finding: Shows where each issue lives in your code and why it was flagged.
Plain-English Fixes: Explains what is wrong, why it matters and what to do about it.
Copy for LLM: Hands any report to your AI tool so it can apply the fixes.
GitHub App Integration: Scan only the repositories you select, read-only access.
Expert Triage Calls: Book a 15-minute call with a CleanSlop engineer to walk through findings.

A solo founder checks a Next.js and Supabase app built with an AI coding assistant before launching.
A developer discovers an exposed AI API key bundled into browser code and moves it to a server route.
A side-project builder verifies that one user's data cannot be read by another through database policies.
A Claude Code user runs a scan from the terminal and has the agent fix the top findings.
A team lead gets a short prioritized list instead of hundreds of linter warnings.
An indie hacker books an expert triage call to confirm an ambiguous finding.

blader

Free

An open agent skill that rewrites AI-sounding text to read like a person wrote it, without changing what the text actually says.

25 Named Patterns: A ranked catalogue of AI-writing tells — from 'not X but Y' staging to decorative bold, chatbot residue, and knowledge-limit disclaimers — each with before and after examples.
Strength-Weighted Detection: The first five patterns justify an edit on a single sighting, while patterns marked weak alone only count when several share a passage, so deliberate stylistic choices survive.
Draft-Critique-Final Loop: Humanizer shows its work by producing a first rewrite, a short critique of whatever still sounds artificial, and then the final version.
No Invention Guarantee: Names, numbers, dates, quotes, and citations must come from the source or the writer; if a sentence needs a missing detail the skill asks rather than fabricating one.
Voice Matching: Supply a writing sample and the rewrite follows its rhythm, word choice, punctuation, and deliberate quirks, including em dashes if you use them.
File-Safe Rewriting: Point it at a file path and it edits prose only, leaving code, data, frontmatter, and link targets untouched.
Agent-Agnostic Install: Distributed as Markdown so it works with any skill-capable agent, via the Skills CLI, the Claude Code plugin, or a ZIP upload in Claude Desktop.
Register-Aware Output: Personal writing keeps the writer's opinions and quirks while technical and reference prose stays neutral and plain.

Cleaning Up AI Drafts: Run a model-generated blog post or essay through Humanizer before publishing so it does not read as machine-written.
Matching a House Voice: Provide a sample of existing published work so rewritten copy matches an established author or brand voice.
Documentation Editing: Point the skill at a repository file to strip decorative headings and staged sentences from technical docs without touching code blocks.
Email and Outreach Polish: Remove sales language and borrowed authority from outbound copy so claims are stated plainly.
Editorial Review: Use the marked list of tells as a critique pass to teach writers which habits read as AI-generated.
Agent Pipeline Step: Chain Humanizer after a drafting agent so generated text is normalized before a human ever reviews it.