unslopify

Audit and rewrite text to remove AI-writing patterns. A deterministic Python core finds the named failures. An agent skill runs the rewrite loop until a fresh-context judge cannot tell a model touched the text.

Install

pip install unslopify

30 seconds

$ unslopify draft.md
draft.md:1: [scene-setting] "In today's fast-paced digital world"
    A weather-report opener about today's fast-moving world before the actual topic.
draft.md:1: [inflated-contrast] "is not just"
    A plain statement dressed up as a reveal by denying a smaller version of itself first.

FAIL draft.md: 2 findings, 0 mechanics, 0 bank

Before:

In today's fast-paced digital world, this release is not just an
update. It is a testament to our team's unwavering commitment,
ensuring a seamless experience for every user.

After:

This release fixes the checkout crash and cuts page load from 3 s
to 1 s. Both changes came out of the June incident review.

What it checks

Four categories of named types. unslopify types prints all of them with definitions and examples.

• formula: manufactured rhythm. Inflated contrast, negative parallelism, slogan fragments, stock triads, fake authority, canned conclusions, AI vocabulary clusters, significance inflation.
• substance: claims a reader cannot verify. Empty abstraction, tacked-on benefits, process instead of reason, unsupported claims.
• wording: buried points. Bureaucratic phrasing, hedge stacks, unexplained jargon, copula avoidance, overlong sentences.
• structure: packaging that delays the point. Scene-setting, request restatement, meta-announcements, redundant conclusions, over-structure.

Types are contextual signals with severities and thresholds, not a banned-word list. Mechanical checks run alongside: em and en dashes, curly quotes, sentence length, semicolon density, and 4-word phrases repeated inside one document.

CLI

unslopify DRAFT.md              # audit; exit 0 pass, 1 findings
unslopify - < draft.txt         # audit stdin
unslopify DRAFT.md --json       # full report as JSON
unslopify DRAFT.md --brief      # rewrite instructions for a model or human
unslopify DRAFT.md --fix        # apply safe mechanical fixes only
unslopify DRAFT.md --bank       # also check the cross-document phrase bank
unslopify commit DRAFT.md --id blog-2026-08   # bank a finished document
unslopify types                 # print the rubric

The phrase bank lives at ~/.unslopify/phrase_bank.jsonl (override with UNSLOPIFY_HOME). Committing a finished document banks its 8-word phrases, and future drafts that reuse any of them fail the --bank check. This is what stops a writer, or an agent, from developing stamps.

Agent skill

SKILL.md turns any harness that supports skills into the full rewrite loop: audit, rewrite against named findings, judgment pass, uniqueness gate, fresh-context judge, final PASS. It also defines an always-on mode that applies the writing rules to every reply.

Install:

• Claude Code: copy SKILL.md into ~/.claude/skills/unslopify/, then invoke with /unslopify.
• Cursor: paste the rules from SKILL.md Mode 1 into a project or user rule, and use the CLI in the terminal for audits.
• Codex / other: include SKILL.md in the system context and expose the unslopify CLI.

Library

from unslopify import audit_text, build_brief, profile_from_sample

report = audit_text(open("draft.md").read(), source="draft.md")
print(report.verdict, report.counts_by_category)
print(build_brief(report))          # instructions for the rewrite pass
voice = profile_from_sample(open("my_writing.md").read())

All models are Pydantic v2. report.model_dump_json() gives a stable, versioned record of every audit, which is also the hook for analytics in hosted deployments.

Pipeline

The always-on mode is simpler: a standing rule loads the skill, and the skill's writing rules apply to every outgoing message.

Design notes

• The audit is deterministic. Same text in, same report out, no model calls, no network. Judgment-only types (meaning loss, jargon the regexes miss) are the agent's job and are marked as such in the rubric.
• The judge must be fresh. A model that watched the rewrite approves its own choices, so the skill requires a separate context for the final read.
• Voice is preserved, not replaced. The optional VoiceProfile measures the author's sentence lengths, contraction rate, and first-person rate, and the rewrite aims at those numbers.

Further reading on plain technical writing: Google developer documentation style guide.

License

MIT.