theclaymethod/unslop

Unslop

Humanize AI-generated prose. Audit it first. Rewrite only when the user wants a rewrite.

When to Use

• User asks to "humanize", "de-slop", or "make it sound human"
• Editing AI-generated drafts, emails, articles, social posts
• Text contains AI patterns (throat-clearing, binary contrasts, em-dash abuse, emphasis crutches)
• User says text "sounds like AI" or "sounds robotic"
• Reviewing content before publishing
• User pastes text and asks to "clean it up" or "make it natural"

Arguments

| Argument | Description | Default | |----------|-------------|---------| | --preset | Voice style: crisp, warm, expert, story | crisp | | --strict | Fail if rubric score < 32/40 | false | | --audit-only | Flag AI patterns without rewriting | false | | Input | Text to transform (argument, file path, or stdin) | required |

Modes

This skill has two modes:

• rewrite (default) — diagnose, rewrite, then validate the rewrite
• audit-only — diagnose and assess the text without rewriting it

Trigger audit-only mode when the user says "audit only," "flag only," "scan this," "just detect," "don't rewrite," or passes --audit-only.

Voice Presets

| Preset | Style | Best For | |--------|-------|----------| | crisp | Short, direct, no fluff | Technical writing, documentation | | warm | Friendly, conversational | Emails, blog posts | | expert | Authoritative, confident | Thought leadership, articles | | story | Narrative flow, show don't tell | Case studies, personal posts |

Read the selected preset file from presets/ (e.g., presets/crisp-human.md) before writing. Each preset has specific voice rules, structural patterns, and a quality checklist.

Workflow

Pass 1: Diagnosis

Before rewriting anything, understand what's wrong. This prevents blind rewriting that loses meaning.

1. Read the relevant references based on what you see in the input:

   • references/taboo-phrases.md — the complete catalog of banned phrases and newer structural families, with regex patterns for detection. This is the authoritative list; read it on first use and refer back for edge cases.
   • references/rubric.md — 8 scoring criteria (directness, rhythm, verbs, trust, authenticity, density, fact preservation, template avoidance), 5 points each.
   • references/fact-preservation.md — rules for what must survive transformation unchanged (numbers, names, dates, URLs, quotes, technical terms).

2. Extract constraints from the input — facts that must survive if you rewrite:

   python3 scripts/extract_constraints.py <<< "$INPUT"
   

   This outputs JSON with every number, date, name, URL, and quote that must appear in your output.

3. Scan for AI patterns:

   python3 scripts/banned_phrase_scan.py <<< "$INPUT"
   

   This returns violations grouped by category and severity (hard = always an AI tell, soft = context-dependent). Quoted examples, markdown blockquotes, and code snippets are ignored by default so you don't flag illustrative bad writing in docs. If you explicitly need to audit quoted examples too, run:

   python3 scripts/banned_phrase_scan.py --include-quoted <<< "$INPUT"
   

4. Read the selected preset from presets/ and note its voice rules.

5. Identify: audience, content type, tone target. A LinkedIn post needs different treatment than a technical doc.

Pass 2: Reconstruction

Skip this pass in --audit-only mode.

Rewrite the text. The references you read in Pass 1 are your guide — don't duplicate their rules here, just apply them.

Core principles (the why behind the rules):

• Em-dashes are the #1 AI punctuation tell. Default to zero. Use periods, commas, or parentheses instead. If one is absolutely necessary, max one per several paragraphs.
• AI text delays the point. Cut everything before the actual claim. "Here's the thing:" is throat-clearing. "Let that sink in." is an emphasis crutch. Just state the thing.
• AI inflates significance. "Stands as a testament to" means "is". "Pivotal moment" is almost never pivotal. Replace inflation with the specific fact.
• AI avoids commitment. "It's worth noting that" hedges. "Some experts argue" hides behind unnamed sources. Make claims directly or cite specifically.
• Facts are sacred. Every number, name, date, and URL from the original must appear in your output unchanged. Style is negotiable; accuracy is not.
• Shorter is almost always better. If cutting a sentence doesn't change the meaning, cut it. AI pads; humans compress.

Follow the preset voice characteristics for sentence length, paragraph structure, and tone. Refer to references/edit-library.md for 24 before/after transformation examples if you need guidance on specific pattern types.

For guidance on adding genuine human voice (not just removing AI tells), read references/personality-guide.md. Clean text that's still anonymous and voiceless scores a 3/5 on authenticity — aim for 4+.

Validation

Only run this section when you rewrote the text.

After rewriting, verify your work:

1. Fact preservation — confirm all constraints survived:

   python3 scripts/validate_preservation.py original.txt transformed.txt
   

2. Remaining AI patterns — check your output is clean:

   python3 scripts/banned_phrase_scan.py <<< "$OUTPUT"
   

3. Readability metrics — check rhythm and variance:

   python3 scripts/readability_metrics.py <<< "$OUTPUT"
   

4. Change percentage — flag if >40% changed (may indicate over-editing):

   python3 scripts/diff_check.py original.txt transformed.txt
   

5. Score against rubric — 8 criteria x 5 points = 40 max. Passing: 32/40 (80%). See references/rubric.md for detailed scoring.

Output Format

Adapt output to the context. For a quick fix, just return the cleaned text. For a thorough review, include validation:

Audit Only (--audit-only or user asks for flag-only scan):

## Issues Found

- [Quoted issue, category, why it reads as AI]

## Assessment

- [Which issues are clear problems]
- [Which issues are judgment calls or context-dependent]

Minimal (default for short text / quick fixes):

[The humanized text]

Detailed (for --strict mode or when user asks for analysis):

## Transformed Text

[The humanized version]

## Validation

- Constraints: [X]/[Y] preserved
- AI patterns: [N] remaining (was [M])
- Readability: Grade [X], sentence variance [Y]
- Change: [X]% from original
- Score: [X]/40

## Changes Made

- [List of major transformations applied]

Quick Examples

Input:

Here's the thing: building products is hard. Not because the technology is complex. Because people are complex. Let that sink in.

Output (crisp):

Building products is hard. Not the technology. The people.

Input:

In today's fast-paced business environment, it's becoming increasingly important for organizations to leverage their core competencies while navigating the complex landscape of digital transformation.

Output (crisp):

Companies need to use their strengths while going digital.

Reference Files

Located in this skill's directory. Read them as needed — don't front-load everything into context.

| File | When to Read | |------|-------------| | references/taboo-phrases.md | First use, then for edge cases. Expanded pattern catalog with detection regex, newer structural families, and quote-exemption rules. | | references/rubric.md | When scoring output or in --strict mode. 8 criteria, detailed rubrics. | | references/edit-library.md | When unsure how to transform a specific pattern. 24 before/after examples. | | references/fact-preservation.md | When input has lots of data, names, or quotes. Constraint rules. | | references/personality-guide.md | When output is clean but soulless. How to add genuine voice. | | presets/*.md | After preset selection. Voice-specific rules, patterns, checklists. | | assets/examples/*.md | For extended before/after examples by content type (article, LinkedIn, sales). |

Scripts

All scripts accept stdin or file path arguments and output JSON. Run from the skill directory.

| Script | Purpose | When to Run | |--------|---------|-------------| | scripts/extract_constraints.py | Extract must-preserve facts | Before rewriting | | scripts/banned_phrase_scan.py | Detect AI patterns with severity | Before and after rewriting | | scripts/validate_preservation.py | Verify facts survived | After rewriting | | scripts/readability_metrics.py | Sentence variance, grade level | After rewriting | | scripts/diff_check.py | Change percentage | After rewriting | | scripts/wiki_sync.py | Sync with Wikipedia AI patterns | On /unslop --wiki-sync |

Maintenance Commands

| Command | Action | |---------|--------| | /unslop --add-phrase "phrase" | Add banned phrase | | /unslop --add-structure "pattern\|fix" | Add structural pattern | | /unslop --list-phrases | List all banned phrases | | /unslop --list-structures | List structural patterns | | /unslop --wiki-sync | Sync with Wikipedia for new AI patterns |

Wiki Sync (/unslop --wiki-sync)

Syncs pattern rules with Wikipedia's Signs of AI writing page. Run periodically to pick up new patterns.

Steps:

1. Check for updates: python3 scripts/wiki_sync.py check (exit 0 = no updates)
2. Get structured diff: python3 scripts/wiki_sync.py diff (JSON output with change type, section, words)
3. For each new word/phrase: add to references/taboo-phrases.md in the matching section, and add to scripts/banned_phrase_scan.py BANNED_PHRASES dict with category, severity, and suggestion.
4. Verify: python3 scripts/banned_phrase_scan.py < /dev/null (confirm no syntax errors)

Only add phrases that are genuine AI writing tells for general prose. Skip Wikipedia-specific patterns (broken wikitext, DOI issues, etc.).

Key Principles

1. Diagnosis before writing — understand what's wrong before fixing
2. Facts are sacred — never sacrifice accuracy for style
3. Presets guide, don't constrain — adapt to content type
4. When in doubt, cut — shorter is almost always better
5. Quoted examples are exempt by default — don't flag illustrative bad writing unless the user explicitly wants that
6. Validation is mandatory — run the scripts, especially fact preservation