| 100.00% | Adverbs in dialogue tags | Target: ≤10% dialogue tags with adverbs | | totalTags | 2 | | adverbTagCount | 0 | | adverbTags | (empty) | | dialogueSentences | 57 | | tagDensity | 0.035 | | leniency | 0.07 | | rawRatio | 0 | | effectiveRatio | 0 | |
| 97.67% | AI-ism adverb frequency | Target: <2% AI-ism adverbs (58 tracked) | | wordCount | 2148 | | totalAiIsmAdverbs | 1 | | 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) | |
| 76.72% | AI-ism word frequency | Target: <2% AI-ism words (290 tracked) | | wordCount | 2148 | | totalAiIsms | 10 | | found | | | highlights | | 0 | "weight" | | 1 | "symphony" | | 2 | "velvet" | | 3 | "etched" | | 4 | "trembled" | | 5 | "traced" | | 6 | "scanned" | | 7 | "flicked" |
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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 | 187 | | matches | (empty) | |
| 100.00% | Filter word density | Target: ≤3% sentences with filter/hedge words | | filterCount | 1 | | hedgeCount | 0 | | narrationSentences | 187 | | filterMatches | | | hedgeMatches | (empty) | |
| 100.00% | Gibberish response detection | Target: ≤1% gibberish-like sentences (hard fail if a sentence exceeds 800 words) | | analyzedSentences | 243 | | gibberishSentences | 0 | | adjustedGibberishSentences | 0 | | longSentenceCount | 0 | | runOnParagraphCount | 0 | | giantParagraphCount | 0 | | wordSaladCount | 0 | | repetitionLoopCount | 0 | | controlTokenCount | 0 | | maxSentenceWordsSeen | 48 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Markdown formatting overuse | Target: ≤5% words in markdown formatting | | markdownSpans | 0 | | markdownWords | 0 | | totalWords | 2147 | | ratio | 0 | | matches | (empty) | |
| 100.00% | Missing dialogue indicators (quotation marks) | Target: ≤10% speech attributions without quotation marks | | totalAttributions | 1 | | unquotedAttributions | 0 | | matches | (empty) | |
| 50.00% | Name drop frequency | Target: ≤1.0 per-name mentions per 100 words | | totalMentions | 53 | | wordCount | 1591 | | uniqueNames | 10 | | maxNameDensity | 1.38 | | worstName | "Quinn" | | maxWindowNameDensity | 3.5 | | worstWindowName | "Quinn" | | discoveredNames | | Detective | 1 | | Harlow | 1 | | Quinn | 22 | | Tube | 1 | | Camden | 1 | | Tom | 1 | | Reeves | 15 | | Underground | 1 | | One | 6 | | Past | 4 |
| | persons | | 0 | "Harlow" | | 1 | "Quinn" | | 2 | "Tom" | | 3 | "Reeves" |
| | places | | | globalScore | 0.809 | | windowScore | 0.5 | |
| 100.00% | Narrator intent-glossing | Target: ≤2% narration sentences with intent-glossing patterns | | analyzedSentences | 116 | | glossingSentenceCount | 0 | | matches | (empty) | |
| 100.00% | "Not X but Y" pattern overuse | Target: ≤1 "not X but Y" per 1000 words | | totalMatches | 0 | | per1kWords | 0 | | wordCount | 2147 | | matches | (empty) | |
| 100.00% | Overuse of "that" (subordinate clause padding) | Target: ≤2% sentences with "that" clauses | | thatCount | 1 | | totalSentences | 243 | | matches | | |
| 100.00% | Paragraph length variance | Target: CV ≥0.5 for paragraph word counts | | totalParagraphs | 137 | | mean | 15.67 | | std | 18.47 | | cv | 1.178 | | sampleLengths | | 0 | 21 | | 1 | 8 | | 2 | 8 | | 3 | 90 | | 4 | 16 | | 5 | 33 | | 6 | 42 | | 7 | 6 | | 8 | 12 | | 9 | 3 | | 10 | 28 | | 11 | 14 | | 12 | 1 | | 13 | 17 | | 14 | 2 | | 15 | 25 | | 16 | 7 | | 17 | 34 | | 18 | 5 | | 19 | 97 | | 20 | 8 | | 21 | 12 | | 22 | 9 | | 23 | 5 | | 24 | 4 | | 25 | 2 | | 26 | 27 | | 27 | 8 | | 28 | 35 | | 29 | 4 | | 30 | 6 | | 31 | 6 | | 32 | 13 | | 33 | 12 | | 34 | 24 | | 35 | 2 | | 36 | 8 | | 37 | 28 | | 38 | 4 | | 39 | 20 | | 40 | 16 | | 41 | 8 | | 42 | 22 | | 43 | 14 | | 44 | 2 | | 45 | 4 | | 46 | 4 | | 47 | 11 | | 48 | 4 | | 49 | 10 |
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| 97.76% | Passive voice overuse | Target: ≤2% passive sentences | | passiveCount | 4 | | totalSentences | 187 | | matches | | 0 | "been folded" | | 1 | "been fixed" | | 2 | "been bricked" | | 3 | "been cleaned" |
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| 100.00% | Past progressive (was/were + -ing) overuse | Target: ≤2% past progressive verbs | | pastProgressiveCount | 0 | | totalVerbs | 253 | | matches | (empty) | |
| 100.00% | Em-dash & semicolon overuse | Target: ≤2% sentences with em-dashes/semicolons | | emDashCount | 1 | | semicolonCount | 0 | | flaggedSentences | 1 | | totalSentences | 243 | | ratio | 0.004 | | matches | | 0 | "His coat looked wrong as well—rough wool over a shirt that came from money." |
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| 100.00% | Purple prose (modifier overload) | Target: <4% adverbs, <2% -ly adverbs, no adj stacking | | wordCount | 1266 | | adjectiveStacks | 0 | | stackExamples | (empty) | | adverbCount | 31 | | adverbRatio | 0.02448657187993681 | | lyAdverbCount | 4 | | lyAdverbRatio | 0.00315955766192733 | |
| 100.00% | Repeated phrase echo | Target: ≤20% sentences with echoes (window: 2) | | totalSentences | 243 | | echoCount | 0 | | echoWords | (empty) | |
| 100.00% | Sentence length variance | Target: CV ≥0.4 for sentence word counts | | totalSentences | 243 | | mean | 8.84 | | std | 7.19 | | cv | 0.814 | | sampleLengths | | 0 | 21 | | 1 | 2 | | 2 | 2 | | 3 | 2 | | 4 | 2 | | 5 | 8 | | 6 | 19 | | 7 | 26 | | 8 | 11 | | 9 | 6 | | 10 | 13 | | 11 | 7 | | 12 | 8 | | 13 | 16 | | 14 | 20 | | 15 | 9 | | 16 | 4 | | 17 | 15 | | 18 | 27 | | 19 | 6 | | 20 | 12 | | 21 | 3 | | 22 | 28 | | 23 | 3 | | 24 | 11 | | 25 | 1 | | 26 | 11 | | 27 | 6 | | 28 | 2 | | 29 | 25 | | 30 | 7 | | 31 | 34 | | 32 | 5 | | 33 | 23 | | 34 | 9 | | 35 | 29 | | 36 | 3 | | 37 | 3 | | 38 | 5 | | 39 | 5 | | 40 | 2 | | 41 | 4 | | 42 | 14 | | 43 | 8 | | 44 | 12 | | 45 | 9 | | 46 | 5 | | 47 | 3 | | 48 | 1 | | 49 | 2 |
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| 66.39% | Sentence opener variety | Target: ≥60% unique sentence openers | | consecutiveRepeats | 7 | | diversityRatio | 0.4156378600823045 | | totalSentences | 243 | | uniqueOpeners | 101 | |
| 100.00% | Adverb-first sentence starts | Target: ≥3% sentences starting with an adverb | | adverbCount | 7 | | totalSentences | 163 | | matches | | 0 | "Then at the left hand." | | 1 | "Even the rubbish looked curated." | | 2 | "Even the pathologist looked up" | | 3 | "Just enough to strip away" | | 4 | "Instead she had neat gaps" | | 5 | "A lot more than lay" | | 6 | "Then she looked at Reeves." |
| | ratio | 0.043 | |
| 100.00% | Pronoun-first sentence starts | Target: ≤30% sentences starting with a pronoun | | pronounCount | 32 | | totalSentences | 163 | | matches | | 0 | "She stepped off the last" | | 1 | "Her coat moved with her" | | 2 | "She never wasted steps." | | 3 | "He had his thumbs hooked" | | 4 | "She looked up." | | 5 | "He spread a hand at" | | 6 | "His throat had been cut" | | 7 | "His eyes bulged a touch." | | 8 | "His coat looked wrong as" | | 9 | "His hand had begun to" | | 10 | "Her gaze rose to the" | | 11 | "She pressed two fingers lightly" | | 12 | "She turned the bag." | | 13 | "He had no answer for" | | 14 | "She walked in the direction" | | 15 | "It aimed towards the wall" | | 16 | "He came over with the" | | 17 | "She pointed at the platform" | | 18 | "She traced the end of" | | 19 | "He looked past her at" |
| | ratio | 0.196 | |
| 100.00% | Subject-first sentence starts | Target: ≤72% sentences starting with a subject | | subjectCount | 111 | | totalSentences | 163 | | matches | | 0 | "She stepped off the last" | | 1 | "The platform spread out beneath" | | 2 | "Canvas stalls stood in crooked" | | 3 | "Extension leads looped across old" | | 4 | "A hand-painted sign hung from" | | 5 | "Glass jars glittered on a" | | 6 | "A butcher’s hook swayed from" | | 7 | "Police tape cut across it" | | 8 | "Quinn rolled one sleeve, checked" | | 9 | "Her coat moved with her" | | 10 | "She never wasted steps." | | 11 | "DS Tom Reeves waited beside" | | 12 | "He had his thumbs hooked" | | 13 | "Quinn let her eyes sweep" | | 14 | "She looked up." | | 15 | "Reeves jerked his chin towards" | | 16 | "He spread a hand at" | | 17 | "Quinn crouched without answering him." | | 18 | "The dead man lay on" | | 19 | "His throat had been cut" |
| | ratio | 0.681 | |
| 61.35% | Subordinate conjunction sentence starts | Target: ≥2% sentences starting with a subordinating conjunction | | subConjCount | 2 | | totalSentences | 163 | | matches | | 0 | "By the time Detective Harlow" | | 1 | "If panic had emptied the" |
| | ratio | 0.012 | |
| 100.00% | Technical jargon density | Target: ≤6% sentences with technical-jargon patterns | | analyzedSentences | 55 | | technicalSentenceCount | 3 | | matches | | 0 | "She stepped off the last stair and paused under a strip light that buzzed with a tired yellow shiver." | | 1 | "Police tape cut across it all in bright blue bands that looked cheap against the grime." | | 2 | "The dead man’s own dark coat had been folded once and slid under the back of his head, as if someone had improvised a pillow." |
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| 100.00% | Useless dialogue additions | Target: ≤5% dialogue tags with trailing filler fragments | | totalTags | 2 | | uselessAdditionCount | 0 | | matches | (empty) | |
| 100.00% | Dialogue tag variety (said vs. fancy) | Target: ≤10% fancy dialogue tags | |