| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 19 | | adverbTagCount | 2 | | adverbTags | | 0 | "Quinn said quietly [quietly]" | | 1 | "she said slowly [slowly]" |
| | dialogueSentences | 51 | | tagDensity | 0.373 | | leniency | 0.745 | | rawRatio | 0.105 | | effectiveRatio | 0.078 | |
| 74.17% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1355 | | totalAiIsmAdverbs | 7 | | found | | | highlights | | 0 | "carefully" | | 1 | "slightly" | | 2 | "lazily" | | 3 | "very" | | 4 | "slowly" | | 5 | "suddenly" |
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| 100.00% | AI-ism character names | Target: 0 AI-default names (17 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 100.00% | AI-ism location names | Target: 0 AI-default location names (33 tracked, −20% each) | | codexExemptions | (empty) | | found | (empty) | |
| 85.24% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1355 | | totalAiIsms | 4 | | found | | | highlights | | 0 | "velvet" | | 1 | "footsteps" | | 2 | "etched" |
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| 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 | 105 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 0 | | hedgeCount | 0 | | narrationSentences | 105 | | filterMatches | (empty) | | hedgeMatches | (empty) | |
| 98.34% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 135 | | gibberishSentences | 1 | | adjustedGibberishSentences | 1 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 59 | | ratio | 0.007 | | matches | | 0 | "She'd been seconded to the Met's的特殊案件组 — the SIB, the Sensitive Investigations Branch, whatever name they were using this quarter — for six months." |
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| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 9 | | markdownWords | 21 | | totalWords | 1357 | | ratio | 0.015 | | matches | | 0 | "Museum" | | 1 | "Wet" | | 2 | "Slaked lime" | | 3 | "added" | | 4 | "was" | | 5 | "gone" | | 6 | "dead" | | 7 | "this" | | 8 | "Museum called. Restricted archives broken into. Staff member missing. Name: Eva Kowalski." |
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| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 8 | | unquotedAttributions | 0 | | matches | (empty) | |
| 66.67% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 34 | | wordCount | 819 | | uniqueNames | 15 | | maxNameDensity | 1.59 | | worstName | "Quinn" | | maxWindowNameDensity | 3 | | worstWindowName | "Okoro" | | discoveredNames | | Northern | 1 | | Quinn | 13 | | Young | 1 | | Tube | 1 | | Fiona | 1 | | Okoro | 8 | | Sensitive | 1 | | Investigations | 1 | | Branch | 1 | | Limehouse | 1 | | Marsh | 1 | | Eva | 1 | | Aurora | 1 | | Ashford | 1 | | Professional | 1 |
| | persons | | 0 | "Quinn" | | 1 | "Tube" | | 2 | "Fiona" | | 3 | "Okoro" | | 4 | "Limehouse" | | 5 | "Eva" | | 6 | "Ashford" |
| | places | | | globalScore | 0.706 | | windowScore | 0.667 | |
| 96.81% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 47 | | glossingSentenceCount | 1 | | matches | | 0 | "as though reading her suspicion" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 1357 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 0 | | totalSentences | 135 | | matches | (empty) | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 54 | | mean | 25.13 | | std | 25.69 | | cv | 1.022 | | sampleLengths | | 0 | 104 | | 1 | 19 | | 2 | 29 | | 3 | 22 | | 4 | 24 | | 5 | 7 | | 6 | 2 | | 7 | 74 | | 8 | 24 | | 9 | 12 | | 10 | 28 | | 11 | 4 | | 12 | 10 | | 13 | 44 | | 14 | 13 | | 15 | 35 | | 16 | 11 | | 17 | 104 | | 18 | 29 | | 19 | 2 | | 20 | 62 | | 21 | 5 | | 22 | 31 | | 23 | 5 | | 24 | 7 | | 25 | 40 | | 26 | 6 | | 27 | 35 | | 28 | 8 | | 29 | 5 | | 30 | 47 | | 31 | 9 | | 32 | 68 | | 33 | 7 | | 34 | 3 | | 35 | 68 | | 36 | 5 | | 37 | 4 | | 38 | 7 | | 39 | 71 | | 40 | 12 | | 41 | 17 | | 42 | 59 | | 43 | 19 | | 44 | 8 | | 45 | 9 | | 46 | 34 | | 47 | 3 | | 48 | 19 | | 49 | 22 |
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| 100.00% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 1 | | totalSentences | 105 | | matches | | |
| 6.76% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 138 | | matches | | 0 | "were using" | | 1 | "was still spinning" | | 2 | "was breaking" | | 3 | "was already moving" |
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| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 2 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 135 | | ratio | 0.007 | | matches | | 0 | "She'd been seconded to the Met's的特殊案件组 — the SIB, the Sensitive Investigations Branch, whatever name they were using this quarter — for six months." |
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| 97.74% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 822 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 35 | | adverbRatio | 0.04257907542579075 | | lyAdverbCount | 13 | | lyAdverbRatio | 0.015815085158150853 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 135 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 135 | | mean | 10.05 | | std | 10.56 | | cv | 1.05 | | sampleLengths | | 0 | 26 | | 1 | 9 | | 2 | 12 | | 3 | 37 | | 4 | 2 | | 5 | 18 | | 6 | 13 | | 7 | 1 | | 8 | 1 | | 9 | 4 | | 10 | 13 | | 11 | 1 | | 12 | 1 | | 13 | 5 | | 14 | 9 | | 15 | 18 | | 16 | 4 | | 17 | 16 | | 18 | 8 | | 19 | 1 | | 20 | 3 | | 21 | 3 | | 22 | 2 | | 23 | 23 | | 24 | 2 | | 25 | 2 | | 26 | 27 | | 27 | 2 | | 28 | 2 | | 29 | 1 | | 30 | 15 | | 31 | 3 | | 32 | 6 | | 33 | 13 | | 34 | 1 | | 35 | 1 | | 36 | 5 | | 37 | 7 | | 38 | 16 | | 39 | 12 | | 40 | 4 | | 41 | 10 | | 42 | 20 | | 43 | 3 | | 44 | 19 | | 45 | 2 | | 46 | 4 | | 47 | 5 | | 48 | 4 | | 49 | 8 |
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| 76.79% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 8 | | diversityRatio | 0.4962962962962963 | | totalSentences | 135 | | uniqueOpeners | 67 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 3 | | totalSentences | 82 | | matches | | 0 | "Instead she crouched again, studying" | | 1 | "Then a third time." | | 2 | "Just the photo and a" |
| | ratio | 0.037 | |
| 93.17% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 26 | | totalSentences | 82 | | matches | | 0 | "She stepped carefully around a" | | 1 | "His hands wouldn't stop moving." | | 2 | "She produced her warrant card," | | 3 | "She noted the absence without" | | 4 | "She leaned closer." | | 5 | "She touched a fragment with" | | 6 | "Her knees protested." | | 7 | "His hands had stopped moving." | | 8 | "She was shorter than Quinn" | | 9 | "She'd been seconded to the" | | 10 | "She gestured at the stalls," | | 11 | "It wasn't a question" | | 12 | "She didn't rise to it." | | 13 | "She produced latex gloves from" | | 14 | "Her fingers closed around cold" | | 15 | "She drew out a small" | | 16 | "She looked at Quinn with" | | 17 | "She turned slowly, eyes tracking" | | 18 | "She pulled the ID from" | | 19 | "she said slowly" |
| | ratio | 0.317 | |
| 45.37% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 68 | | totalSentences | 82 | | matches | | 0 | "The body lay face-down in" | | 1 | "Someone had hung lanterns from" | | 2 | "Brass things, shaped like skulls," | | 3 | "The woodblock flooring that shouldn't" | | 4 | "An underground black market, buried" | | 5 | "She stepped carefully around a" | | 6 | "Several hours at least." | | 7 | "A uniformed constable intercepted her" | | 8 | "His hands wouldn't stop moving." | | 9 | "She produced her warrant card," | | 10 | "The word snagged." | | 11 | "Quinn filed it." | | 12 | "The body belonged to a" | | 13 | "She noted the absence without" | | 14 | "She leaned closer." | | 15 | "The chalk wasn't chalk at" | | 16 | "She touched a fragment with" | | 17 | "The kind used in old" | | 18 | "the constable said, as though" | | 19 | "Her knees protested." |
| | ratio | 0.829 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 82 | | matches | (empty) | | ratio | 0 | |
| 40.82% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 21 | | technicalSentenceCount | 3 | | matches | | 0 | "The woodblock flooring that shouldn't have existed stretched in neat rows toward a row of stalls draped in velvet and chain, their canvas canopies stitched with…" | | 1 | "The body belonged to a man in his fifties, heavyset, wearing a dark wool overcoat that had cost more than Quinn's monthly rent." | | 2 | "DS Fiona Okoro emerged from behind a stall draped in black velvet, its wares hidden beneath glass cases that caught the skull-lantern light." |
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| 98.68% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 19 | | uselessAdditionCount | 1 | | matches | | 0 | "the constable said, as though reading her suspicion" |
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| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 4 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 51 | | tagDensity | 0.078 | | leniency | 0.157 | | rawRatio | 0 | | effectiveRatio | 0 | |