Night Market Skill — ported from claude-night-market/scribe. For the full experience with agents, hooks, and commands, install the Claude Code plugin.
AI Slop Detection
AI slop is identified by patterns of usage rather than individual words. While a single "delve" might be acceptable, its proximity to markers like "tapestry" or "embark" signals generated text. We analyze the density of these markers per 100 words, their clustering, and whether the overall tone fits the document type.
Execution Workflow
Start by identifying target files and classifying them as technical docs, narrative prose, or code comments. This allows for context-aware scoring during analysis.
Language Detection
- Auto-detect language from text content using function word frequency
- Override with explicit
--lang parameter (en, de, fr, es)
- Load language-specific patterns from
data/languages/{lang}.yaml
- Fall back to English if detection confidence is low
- See
modules/language-support.md for details on cultural calibration
Vocabulary and Phrase Detection
Load: @modules/vocabulary-patterns.md
We categorize markers into three tiers based on confidence. Tier 1 words appear dramatically more often in AI text and include "delve," "multifaceted," and "leverage." Tier 2 covers context-dependent transitions like "moreover" or "subsequently," while Tier 3 identifies vapid phrases such as "In today's fast-paced world" or "cannot be overstated."
| Word | Context | Human Alternative |
|---|
| delve | "delve into" | explore, examine, look at |
| tapestry | "rich tapestry" | mix, combination, variety |
| realm | "in the realm of" | in, within, regarding |
| embark | "embark on a journey" | start, begin |
| beacon | "a beacon of" | example, model |
| spearheaded | formal attribution | led, started |
| multifaceted | describing complexity | complex, varied |
| comprehensive | describing scope | thorough, complete |
| pivotal | importance marker | key, important |
| nuanced | sophistication signal | subtle, detailed |
| meticulous/meticulously | care marker | careful, detailed |
| intricate | complexity marker | detailed, complex |
| showcasing | display verb | showing, displaying |
| leveraging | business jargon | using |
| streamline | optimization verb | simplify, improve |
Tier 2: Medium-Confidence Markers (Score: 2 each)
Common but context-dependent:
| Category | Words |
|---|
| Transition overuse | moreover, furthermore, indeed, notably, subsequently |
| Intensity clustering | significantly, substantially, fundamentally, profoundly |
| Hedging stacks | potentially, typically, often, might, perhaps |
| Action inflation | revolutionize, transform, unlock, unleash, elevate |
| Empty emphasis | crucial, vital, essential, paramount |
Tier 3: Phrase Patterns (Score: 2-4 each)
| Phrase | Score | Issue |
|---|
| "In today's fast-paced world" | 4 | Vapid opener |
| "It's worth noting that" | 3 | Filler |
| "At its core" | 2 | Positional crutch |
| "Cannot be overstated" | 3 | Empty emphasis |
| "A testament to" | 3 | Attribution cliche |
| "Navigate the complexities" | 4 | Business speak |
| "Unlock the potential" | 4 | Marketing speak |
| "Treasure trove of" | 3 | Overused metaphor |
| "Game changer" | 3 | Buzzword |
| "Look no further" | 4 | Sales pitch |
| "Nestled in the heart of" | 4 | Travel writing cliche |
| "Embark on a journey" | 4 | Melodrama |
| "Ever-evolving landscape" | 4 | Tech cliche |
| "Hustle and bustle" | 3 | Filler |
Step 3: Structural Pattern Detection
Load: @modules/structural-patterns.md
Em Dash Overuse
Count em dashes (—) per 1000 words:
- 0-2: Normal human range
- 3-5: Elevated, review usage
- 6+: Strong AI signal
# Count em dashes in file
grep -o '—' file.md | wc -l
Tricolon Detection
AI loves groups of three with alliteration:
- "fast, efficient, and reliable"
- "clear, concise, and compelling"
- "robust, reliable, and resilient"
Pattern: adjective, adjective, and adjective with similar sounds.
List-to-Prose Ratio
Count bullet points vs paragraph sentences:
- >60% bullets: AI tendency
- Emoji-led bullets: Strong AI signal in technical docs
Sentence Length Uniformity
Measure standard deviation of sentence lengths:
- Low variance (SD < 5 words): AI monotony
- High variance (SD > 10 words): Human variation
Paragraph Symmetry
AI produces "blocky" text with uniform paragraph lengths. Check if paragraphs cluster around the same word count.
Step 4: Sycophantic Pattern Detection
Especially relevant for conversational or instructional content:
| Phrase | Issue |
|---|
| "I'd be happy to" | Servile opener |
| "Great question!" | Empty validation |
| "Absolutely!" | Over-agreement |
| "That's a wonderful point" | Flattery |
| "I'm glad you asked" | Filler |
| "You're absolutely right" | Sycophancy |
These phrases add no information and signal generated content.
Step 5: Calculate Slop Density Score
slop_score = (tier1_count * 3 + tier2_count * 2 + phrase_count * avg_phrase_score) / word_count * 100
| Score | Rating | Action |
|---|
| 0-1.0 | Clean | No action needed |
| 1.0-2.5 | Light | Spot remediation |
| 2.5-5.0 | Moderate | Section rewrite recommended |
| 5.0+ | Heavy | Full document review |
Step 6: Generate Report
Output format:
## Slop Detection Report: [filename]
**Overall Score**: X.X / 10 (Rating)
**Word Count**: N words
**Markers Found**: N total
### High-Confidence Markers
- Line 23: "delve into" -> consider: "explore"
- Line 45: "rich tapestry" -> consider: "variety"
### Structural Issues
- Em dash density: 8/1000 words (HIGH)
- Bullet ratio: 72% (ELEVATED)
- Sentence length SD: 3.2 words (LOW VARIANCE)
### Phrase Patterns
- Line 12: "In today's fast-paced world" (vapid opener)
- Line 89: "cannot be overstated" (empty emphasis)
### Recommendations
1. Replace [specific word] with [alternative]
2. Convert bullet list at line 34-56 to prose
3. Vary sentence structure in paragraphs 3-5
Module Reference
- See
modules/fiction-patterns.md for narrative-specific slop markers
- See
modules/remediation-strategies.md for fix recommendations
Integration with Remediation
After detection, invoke Skill(scribe:doc-generator) with --remediate flag to apply fixes, or manually edit using the report as a guide.
Exit Criteria
- All target files scanned
- Density scores calculated
- Report generated with actionable recommendations
- High-severity items flagged for immediate attention
