unslop-file – skill Agent & Tự động hóa cho agent AI

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. Use the deterministic or optional LLM workflow described below, subject to the implementation availability check.

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)

| Mode | What runs | Use when… |

| ---------- | ------------------------------------------------------------------------------------------- | ------------------------------------------------------------ |

| subtle | Stock 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. |

| full | Balanced + 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

This catalog tree does not bundle scripts/ or the referenced implementation trace. The commands below describe the upstream implementation, not an installed helper. If the sibling is absent, do not run python3 -m scripts from an arbitrary directory; use a separately inspected upstream installation or edit the requested prose directly. Do not claim its backup, validation or restoration behavior occurred without observing it.

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.

Before using LLM mode, confirm authorization to send the file contents to the selected external service. Filenames alone cannot detect embedded secrets or confidential prose: inspect and redact sensitive material locally, and protect backups and audit reports. Preserve substantive uncertainty, legal conditions and distinct list requirements; stylistic “tightening” must never delete meaning or make factual claims more certain. Review the diff before replacing the source.

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 (

- 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 ` 

Pattern (before → after)

| # | Before | After (deterministic, --mode balanced) |

| --- | --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- | -------------------------------------------------------------------------------------- |

| 1 | It'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. |

| 2 | The application leverages a microservices architecture that comprises multiple discrete components. | The application uses a microservices architecture that comprises multiple discrete components. |

| 3 | At its core, caching trades memory for latency.

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