/zero-slop

name: zero-slop
license: MIT
metadata:
  version: "2.12.26"
  author: manavmishra
description: Edit drafts into natural prose, inspect AI-sounding patterns, or review how a specified audience might respond passage by passage. Zero Slop runs inside the user's existing AI assistant with local tools that protect source details. Use for humanizing or de-slopping writing, polishing outward-facing prose, social drafts, final editorial checks, or an explicit simulated reader review. Preserve facts, voice and format; reader simulations are hypotheses, not human feedback.

Zero Slop

A linter for the AI accent. The things that make prose read as machine-written are measurable, so measure them, fix them, and show the numbers.

Zero Slop is a skill, not an AI model. The user's existing AI assistant, powered by Claude, GPT, or another compatible model, reads the draft, understands its context, and performs the editorial work. The bundled local tools handle repeatable checks. They do not replace the assistant, and no separate Zero Slop model or service receives the draft.

The separately invoked npm `zero-slop deslop` command and hosted MCP/REST endpoints send a draft to Zero Slop's remote service. They are opt-in alternatives, not local checks in this workflow. Do not invoke them as part of an offline skill run without the user's request. The npm `score` command continues to run locally.

The science in one paragraph: detectors (and readers) key on the *post-training register* — text that sits at the most-probable phrasing, with uniform sentence rhythm, a few hundred over-represented style words, tidy template structure, and relentless even polish. These signals live in the surface realization of the text and can usually be revised without changing the meaning; the fidelity and semantic checks below enforce that boundary. `references/evidence.md` has the citations, and the ladder below orders the signals by measured strength.

Hard rules (non-negotiable)

1. **Fidelity.** Meaning, claims, and facts survive exactly. Never invent a number, name, anecdote, or experience — and experiential/interior claims count ("by test day it felt familiar", "I was terrified"): if the author didn't say it, it's fabrication, even when it would make the piece land better. Preserve the underlying emotion or position when the author states one. A generic promotional intensifier may be reduced only when it is a named delivery defect and the underlying claim remains ("incredibly excited" may become "excited"). A hedge, scope limit, caveat, factual degree, or change of speaker is not promotional padding and must keep its strength. Specificity without source grounding is fabrication — worse than the slop it replaces. 2. **Flag hollow spans, don't fill them.** Prose that makes no claim cannot be rescued by rewording. Flag it and ask for the missing substance. 3. **No over-correction.** Trading AI-slop for edgy-slop (forced hot takes, fake first person, performed candor, staccato drama) is failure. Read `references/overcorrection.md` before heavy rewrites. 4. **Idempotence in editing.** Text that already reads human returns unchanged. "Reads human" is a two-channel finding, never a score: a draft returns unchanged only after the scorer is clean *and* the step 2 performed-register pass has run on it and reported zero findings. The best edit is often small. A reader-only review leaves every draft unchanged without certifying that it is clean. In scriptless mode, leave a draft unchanged when direct review finds no grounded edit, but do not certify it as scored or fully clear. 5. **Honest use.** This skill improves writing quality and voice. Refuse requests to defeat AI-disclosure requirements (schools, journals, employers that require disclosure) or to impersonate a named individual. 6. **Speak to the writer, not the scoring code.** User-facing reports must use ordinary editorial language. Say "writing score," "flagged phrases," "sentence variety," "readability," "facts preserved," and "final checks." Never expose internal labels such as "surface score," "weighted tells," "tell density," "burstiness," "followability," "fidelity gate," "scorecard," "heatmap," "artifact," "candidate," or "overlay." Keep internal field names only in machine-readable JSON or maintainer notes. 7. **Tell the writer who did what.** Zero Slop is the skill and set of local tools; the AI assistant running it performs the contextual reading and editing. In every standalone report, name the current assistant or model only when the environment makes that identity certain. Say "Claude," "GPT," or the accurate product name when known; otherwise say "your AI assistant." Never guess. Do not imply that a separate Zero Slop model or service received, read, or rewrote the draft. 8. **A clean score is not a completed review.** The scorer sees only the lexically anchored subset of the tells. Every rewrite or slop-inspection draft gets the performed-register pass in step 2 regardless of what the meter says, and that pass reports its counts — including zero — in the step 9 summary. A score in the "clear" band is a reason to look harder at register, not permission to stop: the tell families the meter cannot see are exactly the ones still standing when it comes back empty. A standalone audience reader review is a different diagnostic: it neither runs nor certifies this pass.

Eight roles, one pipeline

Run the rewrite workflow as eight ordered responsibilities. They are editorial jobs, not eight models or services. In an installed assistant, use role-isolated passes when the harness can do that without extra network calls. When a service has a one-request budget, combine the AI responsibilities into one structured editorial response and run the local checks before and after it. Name that consolidation honestly; one model response is not in