If you've looked for a way to make AI-generated writing sound human lately, you've met the word unslop. It appears in Cursor skill lists, Claude Code plugin marketplaces, and writing-tool threads — and it sounds like a product. It isn't. It's a folk name, shared by at least seven open-source projects that disagree with each other about what "removing AI slop" even means.
This guide sorts them into three philosophies, gives each an honest verdict, and tells you which one fits your workflow. It ends with the cheapest intervention point of all: preventing slop instead of cleaning it.
Why "unslop" became a category
A year ago, "AI slop" was a complaint; now it's an editorial standard. Publications reject submissions that read machine-made, teams ship docs that customers actually read, and everyone has seen the same em-dash-heavy, bullet-pointed prose enough times to recognize it on sight. Unslop caught on as a name because it promises the opposite of that recognition — writing that passes the human test. The seven projects below disagree about how to get there, but they agree the problem is real.
1. The typography-swapping unslop: fast, deterministic, superficial
The most literal unslop is jere-mie/unslop: a tiny CLI written in Zig that strips what its README calls the "LLM accent." Em-dashes become hyphens, curly quotes become straight quotes, ellipses become three dots, non-breaking spaces become spaces. Zero dependencies, one static binary, recursive repo sanitizing in milliseconds, gitignored paths skipped by default, atomic file writes so a crash never leaves a file half-written.
A sibling lineage (packaged via uv, unslop --stdin --deterministic) adds one important idea: reproducibility. Same input, same output, every run — which is what you need when the step runs unattended in a pipeline. The cluxion skill wrapper frames it exactly right: a deterministic final pass before committing docs or publishing posts.
The honest verdict: this layer kills the glyph fingerprint, not the voice. A paragraph that read like AI still reads like AI — it just uses hyphens now. Install one as a pre-commit hook or CI step; don't mistake it for a writing-quality strategy.
2. The LLM copyeditors: slower, actually changes the voice
The second family admits that typography isn't the problem — the sentence shapes are. crypdick/unslop is a Claude Code plugin that detects the constellation of habits that make AI writing recognizable and rewrites the text the way a human copyeditor would: cutting filler, deflating importance, saying things straight. You say "unslop this" and get back prose that keeps your ideas but drops the AI voice.
mcnamee's standalone unslop skill does the same job with a design choice the others should copy: transparency. /unslop <text or file> returns the cleaned text and nothing else; ask "what changed?" and you get a before/after table with a reason per edit. No dependencies, no configuration, works on an airgapped machine.
kandulanikhilvarma's unslop-suite casts the widest net — prose, code, commit messages, decks — and can also audit without rewriting, scoring how machine-made a document reads.
The honest verdict: this is the only layer that changes how the writing sounds. The price is an extra model pass per document, variable output, and a review step you can't skip — an LLM sanding off slop can sand off your voice along with it. The before/after table is the best defense against that.
3. The empiricists: don't guess, measure
The cleverest idea in the family doesn't prescribe anything. Instead of a human guessing which phrases are overused, it generates 50+ domain-specific outputs from a model, runs statistical analysis on the recurring defaults, and writes a custom avoidance profile. Visual mode even screenshots HTML output to catch CSS and layout clichés. It ships with prebuilt profiles for writing and React design. (Find it via the avoid-slop directory, which catalogs this whole space.)
The philosophy behind it is the sharpest sentence in the anti-slop literature: telling a model to "write better" just creates a new flavor of slop; listing what not to do forces novelty rather than template substitution. That's worth remembering regardless of which tool you pick.
The honest verdict: highest setup cost, highest ceiling. Best for teams with a recurring output type — measure once, benefit forever.
The cheaper move: prevention beats cleanup
All three families above are post-hoc: the slop gets written, then scrubbed. The cheapest intervention point is upstream — a skill that stops the model from generating the tells in the first place.
That's the design behind stop-slop: roughly 30 banned phrases across 7 categories, 8 banned structural patterns, and a scoring rubric that triggers a revision pass when a draft scores below 35/50. Its best idea is "false agency" detection — AI gives inanimate things human verbs ("the data tells us," "the market rewards") to dodge naming actual actors, and naming the human fixes it. Em-dashes aren't "used sparingly" in this system. They're banned.
An honest note: prevention doesn't replace cleanup. A banned-phrase list can't fix pacing or structure — that's what the copyeditor layer is for. But it moves most of the work to the moment it's cheapest: generation time.
Which one should you install?
- Commit messages, docs, changelogs in CI — the Zig CLI. Deterministic, instant, zero dependencies.
- Blog posts and essays you care about — mcnamee's skill (the before/after table protects your voice), or the crypdick plugin if you live in Claude Code.
- Recurring team output like landing pages or release notes — the empirical profiler. Measure once, benefit forever.
- Everything, at the source — a prevention skill in your system prompt or Claude Project. One minute to install, and it makes the other layers' jobs smaller.
This is adapted from our longer guide, which ends with a concrete pick for frontend designers too: Unslop, Explained: Which AI Writing Cleaner Fits You.
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