| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 10 | | adverbTagCount | 1 | | adverbTags | | 0 | "she said slowly [slowly]" |
| | dialogueSentences | 31 | | tagDensity | 0.323 | | leniency | 0.645 | | rawRatio | 0.1 | | effectiveRatio | 0.065 | |
| 87.10% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 1163 | | totalAiIsmAdverbs | 3 | | found | | | highlights | | |
| 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) | |
| 74.20% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 1163 | | totalAiIsms | 6 | | found | | | highlights | | 0 | "weight" | | 1 | "echoed" | | 2 | "etched" | | 3 | "magnetic" |
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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 | 78 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 1 | | narrationSentences | 78 | | filterMatches | | | hedgeMatches | | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 99 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | repeatedSegmentCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 1182 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 12 | | unquotedAttributions | 0 | | matches | (empty) | |
| 100.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 25 | | wordCount | 959 | | uniqueNames | 9 | | maxNameDensity | 0.94 | | worstName | "Quinn" | | maxWindowNameDensity | 1.5 | | worstWindowName | "Quinn" | | discoveredNames | | Camden | 1 | | Tube | 1 | | Harlow | 1 | | Quinn | 9 | | Abena | 1 | | Osei | 8 | | Aldridge | 2 | | Morris | 1 | | Martin | 1 |
| | persons | | 0 | "Camden" | | 1 | "Harlow" | | 2 | "Quinn" | | 3 | "Osei" | | 4 | "Aldridge" | | 5 | "Morris" | | 6 | "Martin" |
| | places | (empty) | | globalScore | 1 | | windowScore | 1 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 51 | | glossingSentenceCount | 1 | | matches | | 0 | "looked like the knuckle-bone of something" |
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| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 1 | | per1kWords | 0.846 | | wordCount | 1182 | | matches | | 0 | "not at her but through her, at something past her" |
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| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 99 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 43 | | mean | 27.49 | | std | 28.29 | | cv | 1.029 | | sampleLengths | | 0 | 14 | | 1 | 99 | | 2 | 2 | | 3 | 62 | | 4 | 39 | | 5 | 2 | | 6 | 2 | | 7 | 105 | | 8 | 3 | | 9 | 78 | | 10 | 38 | | 11 | 3 | | 12 | 9 | | 13 | 7 | | 14 | 12 | | 15 | 27 | | 16 | 4 | | 17 | 70 | | 18 | 30 | | 19 | 2 | | 20 | 51 | | 21 | 59 | | 22 | 4 | | 23 | 53 | | 24 | 10 | | 25 | 19 | | 26 | 2 | | 27 | 2 | | 28 | 2 | | 29 | 6 | | 30 | 24 | | 31 | 8 | | 32 | 33 | | 33 | 53 | | 34 | 52 | | 35 | 7 | | 36 | 40 | | 37 | 22 | | 38 | 3 | | 39 | 2 | | 40 | 10 | | 41 | 78 | | 42 | 34 |
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| 78.27% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 6 | | totalSentences | 78 | | matches | | 0 | "been called" | | 1 | "been wired" | | 2 | "been sealed" | | 3 | "were scuffed" | | 4 | "was etched" | | 5 | "got written" |
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| 32.29% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 4 | | totalVerbs | 159 | | matches | | 0 | "were pointing" | | 1 | "were counting" | | 2 | "was standing" | | 3 | "was beginning" |
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| 0.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 8 | | semicolonCount | 0 | | flaggedSentences | 6 | | totalSentences | 99 | | ratio | 0.061 | | matches | | 0 | "She checked her watch — the worn leather strap tightened automatically when she flexed her wrist — and logged the time." | | 1 | "The second was his face — the skin had the texture of old paper, drawn tight, as though something had been slowly drunk out of him from the inside." | | 2 | "The knees of her trousers kissed the tiles and she felt the cold through the fabric, and beneath the cold something else — that pressure again, low and humming, the way a transformer hummed if you put your hand on the casing." | | 3 | "Its face was etched with markings that were not compass points — she could see that even in the poor light." | | 4 | "Not spinning — tracking." | | 5 | "Somewhere down the tunnel, water dripped at intervals that were almost — but not quite — regular, as though something were counting." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 956 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 23 | | adverbRatio | 0.02405857740585774 | | lyAdverbCount | 7 | | lyAdverbRatio | 0.007322175732217573 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 99 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 99 | | mean | 11.94 | | std | 10.37 | | cv | 0.869 | | sampleLengths | | 0 | 14 | | 1 | 25 | | 2 | 22 | | 3 | 21 | | 4 | 1 | | 5 | 9 | | 6 | 21 | | 7 | 2 | | 8 | 7 | | 9 | 35 | | 10 | 8 | | 11 | 12 | | 12 | 14 | | 13 | 14 | | 14 | 10 | | 15 | 1 | | 16 | 2 | | 17 | 2 | | 18 | 3 | | 19 | 24 | | 20 | 13 | | 21 | 13 | | 22 | 12 | | 23 | 23 | | 24 | 1 | | 25 | 16 | | 26 | 3 | | 27 | 13 | | 28 | 19 | | 29 | 3 | | 30 | 6 | | 31 | 29 | | 32 | 4 | | 33 | 4 | | 34 | 7 | | 35 | 31 | | 36 | 3 | | 37 | 9 | | 38 | 7 | | 39 | 12 | | 40 | 8 | | 41 | 19 | | 42 | 3 | | 43 | 1 | | 44 | 2 | | 45 | 42 | | 46 | 5 | | 47 | 1 | | 48 | 6 | | 49 | 14 |
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| 64.65% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 11 | | diversityRatio | 0.45454545454545453 | | totalSentences | 99 | | uniqueOpeners | 45 | |
| 99.50% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 2 | | totalSentences | 67 | | matches | | 0 | "Then the body." | | 1 | "Somewhere down the tunnel, water" |
| | ratio | 0.03 | |
| 58.81% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 27 | | totalSentences | 67 | | matches | | 0 | "She checked her watch —" | | 1 | "She'd been called out of" | | 2 | "Her boots rang on the" | | 3 | "She stopped herself" | | 4 | "She walked the perimeter first," | | 5 | "She didn't use the word," | | 6 | "He had been a man" | | 7 | "He lay on his back" | | 8 | "His eyes were open." | | 9 | "They were entirely white." | | 10 | "She looked at Aldridge's hands." | | 11 | "She moved her eyes across" | | 12 | "She fished a gloved finger" | | 13 | "Its casing was green with" | | 14 | "Its face was etched with" | | 15 | "she said slowly" | | 16 | "She said, with the careful" | | 17 | "She had learned three years" | | 18 | "She had learned to keep" | | 19 | "She thought of Morris." |
| | ratio | 0.403 | |
| 42.09% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 56 | | totalSentences | 67 | | matches | | 0 | "The tiles at Camden's disused" | | 1 | "Detective Harlow Quinn ducked beneath" | | 2 | "The air down there had" | | 3 | "She checked her watch —" | | 4 | "She'd been called out of" | | 5 | "The forensics team had been" | | 6 | "Her boots rang on the" | | 7 | "The platform stretched in both" | | 8 | "DC Abena Osei straightened from" | | 9 | "Osei was young, good, and" | | 10 | "She stopped herself" | | 11 | "Quinn didn't answer." | | 12 | "She walked the perimeter first," | | 13 | "The platform had been sealed" | | 14 | "Candle stubs sat in niches" | | 15 | "The tiles were scuffed in" | | 16 | "She didn't use the word," | | 17 | "He had been a man" | | 18 | "He lay on his back" | | 19 | "That was the first thing" |
| | ratio | 0.836 | |
| 0.00% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 0 | | totalSentences | 67 | | matches | (empty) | | ratio | 0 | |
| 63.49% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 36 | | technicalSentenceCount | 4 | | matches | | 0 | "The tiles at Camden's disused Tube platform were slick with something that wasn't water." | | 1 | "A small brass compass lay against his palm, as though it had fallen from his fingers." | | 2 | "As she shifted her weight, it swung with her, adjusting, as though it were pointing not at her but through her, at something past her." | | 3 | "Somewhere down the tunnel, water dripped at intervals that were almost — but not quite — regular, as though something were counting." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 10 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | | totalTags | 6 | | fancyCount | 0 | | fancyTags | (empty) | | dialogueSentences | 31 | | tagDensity | 0.194 | | leniency | 0.387 | | rawRatio | 0 | | effectiveRatio | 0 | |