| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 9 | | adverbTagCount | 1 | | adverbTags | | 0 | "Navarro's tone shifted just [just]" |
| | dialogueSentences | 33 | | tagDensity | 0.273 | | leniency | 0.545 | | rawRatio | 0.111 | | effectiveRatio | 0.061 | |
| 85.25% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1356 | | totalAiIsmAdverbs | 4 | | found | | 0 | | | 1 | | | 2 | | | 3 | | adverb | "deliberately" | | count | 1 |
|
| | highlights | | 0 | "slowly" | | 1 | "slightly" | | 2 | "gently" | | 3 | "deliberately" |
| |
| 80.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 74.19% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1356 | | totalAiIsms | 7 | | found | | | highlights | | 0 | "tracing" | | 1 | "flickered" | | 2 | "echoing" | | 3 | "mosaic" | | 4 | "magnetic" | | 5 | "navigate" | | 6 | "perfect" |
| |
| 100.00% | Cliché density | Target: ≤1 cliche(s) per 800-word window | | totalCliches | 0 | | maxInWindow | 0 | | found | (empty) | | highlights | (empty) | |
| 100.00% | Emotion telling (show vs. tell) | Target: ≤3% sentences with emotion telling | | emotionTells | 0 | | narrationSentences | 90 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 2 | | hedgeCount | 0 | | narrationSentences | 90 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 114 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 34 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1356 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 3 | | unquotedAttributions | 0 | | matches | (empty) | |
| 83.33% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 1090 | | uniqueNames | 14 | | maxNameDensity | 1.1 | | worstName | "Quinn" | | maxWindowNameDensity | 2.5 | | worstWindowName | "Quinn" | | discoveredNames | | Harlow | 1 | | Quinn | 12 | | Navarro | 7 | | Underground | 1 | | London | 1 | | Northern | 1 | | Ovaltine | 1 | | Brylcreem | 1 | | Edwardian | 1 | | Webb | 3 | | Camden | 2 | | Kowalski | 1 | | British | 1 | | Museum | 1 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Navarro" | | 3 | "Brylcreem" | | 4 | "Webb" | | 5 | "Camden" | | 6 | "Kowalski" | | 7 | "Museum" |
| | places | | | globalScore | 0.95 | | windowScore | 0.833 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 66 | | glossingSentenceCount | 1 | | matches | | 0 | "appeared behind her, flashlight in hand" |
| |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1356 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 114 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 46 | | mean | 29.48 | | std | 24.86 | | cv | 0.843 | | sampleLengths | | 0 | 6 | | 1 | 61 | | 2 | 41 | | 3 | 78 | | 4 | 50 | | 5 | 2 | | 6 | 16 | | 7 | 7 | | 8 | 28 | | 9 | 3 | | 10 | 8 | | 11 | 75 | | 12 | 35 | | 13 | 4 | | 14 | 15 | | 15 | 60 | | 16 | 21 | | 17 | 4 | | 18 | 28 | | 19 | 47 | | 20 | 33 | | 21 | 6 | | 22 | 1 | | 23 | 22 | | 24 | 82 | | 25 | 5 | | 26 | 53 | | 27 | 75 | | 28 | 5 | | 29 | 32 | | 30 | 22 | | 31 | 11 | | 32 | 8 | | 33 | 29 | | 34 | 86 | | 35 | 50 | | 36 | 60 | | 37 | 5 | | 38 | 17 | | 39 | 6 | | 40 | 37 | | 41 | 21 | | 42 | 5 | | 43 | 56 | | 44 | 13 | | 45 | 27 |
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| 70.18% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 9 | | totalSentences | 90 | | matches | | 0 | "been sealed" | | 1 | "was decommissioned" | | 2 | "was balled" | | 3 | "been shaped" | | 4 | "been moved" | | 5 | "was turned" | | 6 | "were closed" | | 7 | "been contained" | | 8 | "were corroded" |
| |
| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 1 | | totalVerbs | 179 | | matches | | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 0 | | semicolonCount | 0 | | flaggedSentences | 0 | | totalSentences | 114 | | ratio | 0 | | matches | (empty) | |
| 94.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1094 | | adjectiveStacks | 1 | | stackExamples | | 0 | "against centuries-old brick." |
| | adverbCount | 36 | | adverbRatio | 0.03290676416819013 | | lyAdverbCount | 11 | | lyAdverbRatio | 0.010054844606946984 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 114 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 114 | | mean | 11.89 | | std | 7.82 | | cv | 0.657 | | sampleLengths | | 0 | 6 | | 1 | 20 | | 2 | 11 | | 3 | 30 | | 4 | 34 | | 5 | 7 | | 6 | 3 | | 7 | 18 | | 8 | 23 | | 9 | 1 | | 10 | 22 | | 11 | 11 | | 12 | 27 | | 13 | 3 | | 14 | 5 | | 15 | 7 | | 16 | 8 | | 17 | 2 | | 18 | 12 | | 19 | 4 | | 20 | 3 | | 21 | 4 | | 22 | 28 | | 23 | 3 | | 24 | 8 | | 25 | 10 | | 26 | 24 | | 27 | 14 | | 28 | 27 | | 29 | 7 | | 30 | 18 | | 31 | 10 | | 32 | 4 | | 33 | 9 | | 34 | 6 | | 35 | 3 | | 36 | 22 | | 37 | 20 | | 38 | 15 | | 39 | 8 | | 40 | 13 | | 41 | 4 | | 42 | 12 | | 43 | 16 | | 44 | 10 | | 45 | 15 | | 46 | 16 | | 47 | 6 | | 48 | 2 | | 49 | 10 |
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| 73.45% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 2 | | diversityRatio | 0.45132743362831856 | | totalSentences | 113 | | uniqueOpeners | 51 | |
| 0.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 23 | | totalSentences | 84 | | matches | | 0 | "It stepped right through the" | | 1 | "She pulled on fresh nitrile" | | 2 | "Her worn leather watch caught" | | 3 | "Her light caught something near" | | 4 | "She moved closer, stepping over" | | 5 | "He knew her methods well" | | 6 | "She didn't pick it up" | | 7 | "She photographed the item from" | | 8 | "She dropped the fragment into" | | 9 | "She stood and walked the" | | 10 | "She pointed to the second" | | 11 | "He was quiet for a" | | 12 | "She walked back to the" | | 13 | "His eyes were closed as" | | 14 | "It spread too evenly, as" | | 15 | "It spread in uneven sheets," | | 16 | "She stood and moved to" | | 17 | "They'd blackened and curled inward," | | 18 | "He crossed to her side" | | 19 | "His easy demeanour shifted." |
| | ratio | 0.274 | |
| 79.05% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 64 | | totalSentences | 84 | | matches | | 0 | "The boot print shouldn't be" | | 1 | "Detective Harlow Quinn crouched at" | | 2 | "It stepped right through the" | | 3 | "DS Navarro stood three metres" | | 4 | "Quinn said nothing." | | 5 | "She pulled on fresh nitrile" | | 6 | "Her worn leather watch caught" | | 7 | "The station had been sealed" | | 8 | "Someone had cut through the" | | 9 | "The body lay splayed on" | | 10 | "Quinn examined the visible forearms." | | 11 | "Whatever killed him, it hadn't" | | 12 | "Quinn's jaw tightened." | | 13 | "Quinn rose and turned slowly," | | 14 | "The station had once served" | | 15 | "Pigeon droppings crusted the overhead" | | 16 | "Her light caught something near" | | 17 | "She moved closer, stepping over" | | 18 | "Navarro's tone shifted, just slightly" | | 19 | "He knew her methods well" |
| | ratio | 0.762 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 84 | | matches | (empty) | | ratio | 0 | |
| 85.71% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 50 | | technicalSentenceCount | 4 | | matches | | 0 | "The face bore strange etchings she couldn't immediately place, angular symbols that weren't quite alphabetic but carried an unsettling intentionality." | | 1 | "Beyond it lay a narrow corridor, pitch black, the air carrying a scent that didn't belong in an abandoned tube station." | | 2 | "They'd blackened and curled inward, every stem and leaf turned to carbon as if subjected to intense heat from below." | | 3 | "The surface streets above were calling, the real world with its paperwork and CCTV and witnesses who didn't know about bone tokens or compasses that spun in sea…" |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 9 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |