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:
   ```bash
   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**:
   ```bash
   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:
   ```bash
   python3 scripts