Unslop Humanize

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Use this skill when you need humanize natural-language memory files (CLAUDE.md, todos, preferences, docs) by removing AI-isms and adding burstiness while preserving every code block, URL, path, command, and heading exactly. Two modes: --deterministic (fast, regex-based, no API) and LLM (default, calls Claude for...

Category: General & Miscellaneous
Repo: antigravity-awesome-skills
Path: skills/unslop-file/SKILL.md
Updated: 7/5/2026, 4:58:46 PM

AI Summary

Use this skill when you need humanize natural-language memory files (CLAUDE.md, todos, preferences, docs) by removing AI-isms and adding burstiness while preserving every code block, URL, path, command, and heading exactly. Two modes: --deterministic (fast, regex-based, no API) and LLM (default, calls Claude for... It is useful for general automation, multi-purpose workflows, cross-disciplinary tasks, and utility skills. Source: antigravity-awesome-skills (skills/unslop-file/SKILL.md).

Unslop Humanize

When to Use

Use this skill when you need humanize natural-language memory files (CLAUDE.md, todos, preferences, docs) by removing AI-isms and adding burstiness while preserving every code block, URL, path, command, and heading exactly. Two modes: --deterministic (fast, regex-based, no API) and LLM (default, calls Claude for...

Purpose

Rewrite natural-language memory files (CLAUDE.md, AGENTS.md, todos, preferences, docs) so they sound human-written: no sycophancy, no stock vocab, no five-paragraph essay shape, no tricolon padding. Everything technical stays exact: code blocks, inline code, URLs, file paths, commands, headings, tables.

Two modes:

  • --deterministic — fast regex pass that strips canonical AI-isms and tightens tricolons. No API call, no ANTHROPIC_API_KEY needed. Best for batch processing and CI.
  • LLM mode (default) — calls Claude (via Anthropic SDK or claude --print CLI fallback) to do a full rewrite that engineers burstiness, restructures performative paragraphs, and matches voice. Slower but better quality.

Humanized version overwrites the original. A FILE.original.md backup is written first. Re-run after editing the .original.md to regenerate.

Intensity levels (--mode)

ModeWhat runsUse when…
subtleStock vocab only.Structure is fine; you just want AI vocabulary gone.
balanced(Default.) Sycophancy, hedging, transitions, stock vocab, authority tropes, signposting, performative balance, em-dash cap.Everyday docs / READMEs / CLAUDE.md.
fullBalanced + filler phrases + negative-parallelism tricolons + stronger LLM prompt.Marketing copy, release notes, slop-heavy LLM output.

Two-pass audit

Use the deterministic pass to get a report, then fix anything that slipped:

humanize --deterministic --report audit.json doc.md     # writes audit + humanized
humanize doc.md                                         # optional LLM polish on top

audit.json lists every rule that fired, every before → after pair, and counts_by_rule. Great for reviewing what the regex changed before trusting the diff to merge.

Trigger

/unslop-file <filepath>, /unslop:humanize <filepath>, or "humanize memory file", "de-slop this doc", "strip AI tone from this file".

Process

The scripts live in a scripts/ directory adjacent to this SKILL.md.

Common layouts:

  • Full repo: unslop/SKILL.md + unslop/scripts/
  • Synced mirror: skills/unslop-file/SKILL.md + skills/unslop-file/scripts/
  • Codex bundle: plugins/unslop/skills/unslop-file/SKILL.md + sibling scripts/

Always prefer the scripts/ sibling of the currently loaded SKILL file.

Steps:

  1. Locate the directory containing this SKILL.md and its scripts/ sibling.
  2. Run from that directory: python3 -m scripts <absolute_filepath> (LLM mode), or add --deterministic for the regex pass.
  3. CLI flow: detect file type → write .original.md backup → humanize → validate (preserve check + AI-ism residual check) → on validation error: targeted fix call (LLM mode) → retry up to 2 times.
  4. On final failure: report errors, restore original, exit 2.
  5. On success: report path of humanized file and .original.md backup, exit 0.
  6. Return result to user.

Humanization Rules

Remove (canonical AI-isms)

  • Sycophancy openers: "Great question!", "Certainly!", "Absolutely!", "Sure!", "I'd be happy to help", "What a fascinating..."
  • Stock vocab: delve, tapestry, testament (praise form), navigate/embark/journey (figurative), realm, landscape (figurative), pivotal, paramount, seamless, holistic, leverage (filler verb), robust (filler), comprehensive (when "complete" works), cutting-edge, state-of-the-art (filler), interplay, intricate, vibrant, underscore(s)/d/ing (figurative), crucial, vital (role/importance/part), ever-evolving, ever-changing, in today's (digital) world/age, dynamic landscape.
  • Hedging openers: "It's important to note that", "It's worth mentioning", "Generally speaking", "In essence", "At its core", "It should be noted that", "It's also worth pointing out".
  • Authority tropes (sentence start): "At its core,", "In reality,", "Fundamentally,", "What really matters is", "The heart of the matter is", "At the heart of X is/lies".
  • Signposting announcements: "Let's dive in(to ...)", "Let's break this down", "Here's what you need to know", "Without further ado", "In this article, I'll ...", "Buckle up".
  • Transition tics (sentence start): "Furthermore,", "Moreover,", "Additionally,", "In conclusion,", "To summarize,".
  • Performative balance: "however" / "on the other hand" appended to every claim.
  • Em-dash pileups (more than two em-dashes per paragraph).
  • Filler phrases (--mode full only): "in order to" → "to", "due to the fact that" → "because", "prior to" → "before", "with regard to" → "about", "a wide variety of" → "many", "at this point in time" → "now", "the fact that" → "that", etc.
  • Negative-parallelism tricolons (--mode full only): "No guesswork, no bloat, no surprises." — the rhetorical triple-no punch.

Tighten

  • Tricolons: "X, Y, and Z" stacks where two would suffice — keep two, drop the weakest
  • Bullet soup: three bullets that say the same thing → merge into one sentence
  • Five-paragraph essay shapes: vary paragraph length; don't write four paragraphs of identical length

Preserve EXACTLY (never modify)

  • Fenced code blocks (...) — every byte
  • Indented code blocks (4-space)
  • Inline code (...)
  • URLs and markdown links
  • File paths (./src/, /etc/, C:\Users\...)
  • Commands (npm install, git rebase, docker run)
  • Technical terms, proper nouns, API names
  • Dates, version numbers, numerics
  • Environment variables ($HOME, ${NODE_ENV})

Preserve structure

  • All markdown headings (text exact)
  • Bullet hierarchy and nesting
  • Numbered lists
  • Tables (compress cells; keep structure)
  • YAML frontmatter

CRITICAL RULE

Everything inside ``` ... ``` is read-only. No comment changes, no whitespace changes, no line reordering. Inline backticks: same. Code is the substrate; humanization only operates on prose between code regions.

Pattern (before → after)

#BeforeAfter (deterministic, --mode balanced)
1It's important to note that running tests prior to pushing changes is a comprehensive best practice. Additionally, it's worth mentioning that this can prevent broken builds.Running tests before pushing changes is a broad best practice. This can prevent broken builds.
2The application leverages a microservices architecture that comprises multiple discrete components.The application uses a microservices architecture that comprises multiple discrete components.
3At its core, caching trades memory for latency.Caching trades memory for latency.
4Let's dive in. Here is the first step.Here is the first step.
5The intricate interplay between caching and latency is crucial.The detailed link between caching and latency is important.
6In today's digital world, we ship fast.Today, we ship fast.

At --mode full, additionally:

#BeforeAfter
7We ran the tests in order to verify the fix.We ran the tests to verify the fix.
8The build failed due to the fact that the disk was full.The build failed because the disk was full.
9No guesswork, no bloat, no surprises.(stripped)

Reference

  • blader/unslop — Claude-Code skill listing 30+ AI tells; we incorporated the strongest signals.
  • Wikipedia: Signs of AI writing — public taxonomy cross-referenced for vocab.
  • Full comparison + gap analysis: docs/research/IMPLEMENTATION_TRACE.md.

Boundaries

  • Only operate on .md, .txt, .markdown, .rst, or extensionless natural language.
  • Never modify .py, .js, .ts, .json, .yaml, .yml, .toml, .env, .lock, .css, .html, .xml, .sql, .sh.
  • Mixed prose-and-code files: humanize only the prose; leave fenced code untouched.
  • If unsure whether a file is prose or code: leave unchanged.
  • Backup FILE.original.md is written before overwrite. Never humanize a file already named *.original.md.
  • Sensitive paths (anything matching .env*, *.pem, *.key, ~/.ssh/, ~/.aws/, etc.) are refused before any read or API call.
  • Files larger than 500 KB are refused.

Limitations

  • Use this skill only when the task clearly matches its upstream source and local project context.
  • Verify commands, generated code, dependencies, credentials, and external service behavior before applying changes.
  • Do not treat examples as a substitute for environment-specific tests, security review, or user approval for destructive or costly actions.

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