Open-source AI text humanizers and slop detectors — strip 'delve', 'tapestry', em-dash clusters, and other AI tells. Code, prose, and academic modes.
Anti-Slop tools are AI-powered software designed to help developers and teams tackle anti-slop-related tasks more efficiently. These tools are typically published as open-source projects on GitHub and can be integrated into existing workflows via MCP (Model Context Protocol), Claude Skills, or standalone agent frameworks. On Agent Skills Hub, we index 14 quality-scored anti-slop tools across languages including Shell, Python, JavaScript.
In 2026, the AI agent ecosystem is maturing rapidly. Anti-Slop tools can significantly boost development efficiency by automating repetitive tasks, reducing human error, and providing intelligent suggestions. The top 3 tools — taste-skill, humanizer, Humanizer-zh — have earned an average of 2,818 GitHub stars, reflecting strong community validation. 10 of the listed tools come with clear open-source licenses, ensuring freedom to use and modify.
When choosing a anti-slop tool, consider these factors: 1) Community activity — GitHub stars and recent commit frequency indicate reliability; 2) Integration method — check if it supports MCP, Claude, or your preferred agent framework; 3) Language compatibility — the most common language in this list is Shell; 4) Quality score — Agent Skills Hub's composite score evaluates code quality, documentation completeness, and maintenance activity. Our recommendation: start with taste-skill — it ranks highest in both star count and quality score.
Taste-Skill - gives your AI good taste. stops the AI from generating boring, generic slop
Claude Code skill that removes signs of AI-generated writing from text
```bash
mkdir -p ~/.claude/skills
git clone https://github.com/blader/humanizer.git ~/.claude/skills/humanizer
```Humanizer 的汉化版本，Claude Code Skills，旨在消除文本中 AI 生成的痕迹。
免费本地 AI 文本去痕迹工具 | Chinese AI text detection & humanization. N-gram perplexity analysis, 20+ detection patterns, academic AIGC reduction (知网/维普/万方), 7 style transforms. Zero dependencies, runs locally.
AI writing skill that eliminates detectable AI patterns. Works with Claude Code, Codex, Cursor, Gemini CLI, and 8+ other agents.
A Claude Code skill that removes signs of AI-generated writing from academic medical papers, making them sound more natural and professionally written.
Free open-source AI text humanizer to convert AI-generated content into undetectable, human-like writing. Bypass Turnitin, GPTZero, and all major AI detectors. No sign-up required. Try our unlimited free online tool
说人话｜Chinese-first AI writing refinement skill that reduces boilerplate and AI tone while preserving facts, terminology, and technical context.
Stop your AI from writing like AI. A universal system prompt eliminating every known LLM style tell — works with Claude Code, Gemini CLI, Codex CLI, Copilot, Cursor, and any web AI.
Detects and rewrites AI writing patterns in Korean, English, Chinese, and Japanese. Runs as a skill for Claude Code, Codex CLI, Cursor, and OpenCode, or as a standalone Node.js CLI.
Skill that audits and rewrites content to remove AI writing patterns. Use it with your favorite agents including Claude Code, OpenClaw, and Hermes.
State Machine definitions for Agentic AI patterns including tool use, reflection, collaboration, human-in-the-loop, machine generation, and more.
🧠 Obsidian plugin that embeds Claude Code as an AI writing companion — smart context, undo, diff preview, multi-thread chat.
| Tool | Stars | Language | License | Score |
|---|---|---|---|---|
| taste-skill | ★ 15.8k | Shell | MIT | 54 |
| humanizer | ★ 11.9k | — | MIT | 54 |
| Humanizer-zh | ★ 8.3k | — | MIT | 39 |
| humanize-chinese | ★ 305 | Python | — | 40 |
| anti-ai-slop-writing | ★ 91 | — | — | 46 |
| humanizer_academic | ★ 56 | — | — | 35 |
| humanize-text | ★ 743 | Python | MIT | 44 |
| shuorenhua | ★ 352 | — | MIT | 45 |
| anti-slop-writing | ★ 53 | — | MIT | 41 |
| patina | ★ 91 | JavaScript | MIT | 36 |
| avoid-ai-writing | ★ 1.5k | — | MIT | 56 |
| slop-guard | ★ 123 | Python | MIT | 35 |
| AgenticStateMachines | ★ 98 | TypeScript | — | 32 |
| Niki-AI | ★ 55 | JavaScript | MIT | 32 |
The top anti-slop tools in 2026 are taste-skill, humanizer, Humanizer-zh. Agent Skills Hub ranks 14 options by GitHub stars, quality score (6 dimensions including completeness, examples, and agent readiness), and recent activity. The list is rebuilt every 8 hours from live GitHub data.
taste-skill (15.8k stars) is the most adopted choice for general anti-slop workflows, written in Shell. humanizer (11.9k stars) is a strong alternative. Pick by your existing stack: match the language and runtime your team already uses to minimize integration cost. If unsure, start with taste-skill — it has the deepest community and the most examples online.
Avoid pre-built anti-slop tools when (1) your use case requires deep customization that the tool's plugin system doesn't support, (2) you have strict compliance requirements that ban third-party dependencies, (3) the tool's maintenance is inactive (last commit >6 months ago), or (4) your data volume is small enough that a 50-line custom script is cheaper than learning the tool. For most production workflows above 100 requests/day, the time savings from a maintained tool outweigh the customization loss.
For most teams, yes. taste-skill has 15.8k stars worth of community testing, handles edge cases you haven't thought of, and ships with documentation. Build your own only when (1) your requirements are deeply non-standard, (2) you have a security/compliance reason to avoid OSS dependencies, or (3) the maintenance burden is small enough (<200 lines of code) that you'll save time long-term. The break-even point is usually around 2-3 weeks of dev time saved.
Most anti-slop tools listed are open source under permissive licenses (MIT, Apache 2.0). A handful offer paid managed/cloud versions on top of free self-hosted core. Always check the LICENSE file on each tool's GitHub repository before commercial use — some use AGPL or non-commercial restrictions that may not fit your deployment model.