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openclaude-improved
OpenClaude is an open-source CLI coding agent that runs on any platform and supports a wide range of LLM providers, offering the same tools and workflows as Claude Code.
AI repository intelligence
FR-AI / ANALYSISWhy it is worth attention
It addresses structural AI tells that persist after word-level editing, grounded in a 2026 UMD/Google DeepMind study, and includes voice calibration and explicit rules against replacement tics.
Who it is for
- Claude users who write or edit English text
- Content creators wanting to avoid AI detection
- Editors and writers refining AI-generated drafts
- Anyone who wants their AI-assisted writing to sound genuinely human
Use cases
- Humanize a blog post or article generated with Claude
- Rewrite an AI-written email or report to match your personal style
- De-AI a launch post or social media update before publishing
- Edit academic or professional text to remove over-explanation and template structure
Strengths
- Three-level approach (typography, vocabulary, structure) backed by research on 61,608 texts
- Voice calibration that learns from user-provided texts and saves as editable markdown
- Explicit blacklist with 19 categories of constructions, each with before/after examples
- Field-tested with real feedback that led to v1.1 improvements
Considerations
- Does not aim to beat AI detectors; goal is text acceptable to human readers
- Requires user-provided texts for proper voice calibration
- Initial public launch (v1.0) still had detectable AI style; v1.1 is a fix but further layers may remain
README quick start
Unslop
A Claude skill that removes signs of AI writing from English text. Not just the word-level tells ("delve", "it's important to note") but the structural ones: over-explained conclusions, template paragraphs, invented specifics. Calibrates to your personal voice.
GitHub About line: "English text humanizer for Claude: typography, vocabulary, structure. Calibrates to your voice. Built on the UMD / Google DeepMind study and Wikipedia's Signs of AI writing."
Why word-swapping doesn't work
Every humanizer promises to make AI text "sound human", and most of them swap vocabulary: "delve" out, "explore" in. There are two problems with that.
First, the wordlist decays. "Delve" peaked in 2023 and collapsed in 2025. GPT-5.1 suppresses em dashes. Wikipedia's Signs of AI writing now dates its vocabulary lists by model era, because each generation retires the previous tells.
Second, and worse: the words were never the real signal. In 2026, the University of Maryland and Google DeepMind compared 61,608 texts written by humans and five models. When they ran AI texts through surface editing (clichés, purple prose, redundant exposition removed), a classifier looking only at structure barely noticed: detection dropped from 95.5% to 93.9%. The text gives itself away by how it's built. AI states the moral of every paragraph, runs single-track claim-support-takeaway structure, renders emotion through body metaphors, avoids naming real things, and rounds every ending to a tidy point.
A humanizer that only touches vocabulary fixes the layer that was already fixing itself, and leaves the durable signal intact. This skill works on three levels at once.
How it's built
Level 1: typography and mechanics. Em dashes, mixed quotation marks, Title Case Headings, bold on every "key term", inline-header bullet lists, "Conclusion" sections, markdown debris like utm_source=chatgpt.com. Cheap fixes; partially grep-checkable, and the skill greps itself.
Level 2: vocabulary and rhetoric. 19 categories of constructions with stop lists and fixes: inflated significance ("stands as a testament"), negative parallelisms ("it's not X, it's Y"), the rule of three, superficial -ing analysis ("...highlighting the importance of"), copula avoidance ("serves as" for "is"), synonym cycling, vague attributions, an
Description
English text humanizer for Claude: typography, vocabulary, structure. Calibrates to your voice. Built on the UMD / Google DeepMind study and Wikipedia's Signs of AI writing
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